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Moreno-Grau S, Vernekar M, Lopez-Pineda A, Mas-Montserrat D, Barrabés M, Quinto-Cortés CD, Moatamed B, Lee MTM, Yu Z, Numakura K, Matsuda Y, Wall JD, Ioannidis AG, Katsanis N, Takano T, Bustamante CD. Polygenic risk score portability for common diseases across genetically diverse populations. Hum Genomics 2024; 18:93. [PMID: 39218908 PMCID: PMC11367857 DOI: 10.1186/s40246-024-00664-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) derived from European individuals have reduced portability across global populations, limiting their clinical implementation at worldwide scale. Here, we investigate the performance of a wide range of PRS models across four ancestry groups (Africans, Europeans, East Asians, and South Asians) for 14 conditions of high-medical interest. METHODS To select the best-performing model per trait, we first compared PRS performances for publicly available scores, and constructed new models using different methods (LDpred2, PRS-CSx and SNPnet). We used 285 K European individuals from the UK Biobank (UKBB) for training and 18 K, including diverse ancestries, for testing. We then evaluated PRS portability for the best models in Europeans and compared their accuracies with respect to the best PRS per ancestry. Finally, we validated the selected PRS models using an independent set of 8,417 individuals from Biobank of the Americas-Genomelink (BbofA-GL); and performed a PRS-Phewas. RESULTS We confirmed a decay in PRS performances relative to Europeans when the evaluation was conducted using the best-PRS model for Europeans (51.3% for South Asians, 46.6% for East Asians and 39.4% for Africans). We observed an improvement in the PRS performances when specifically selecting ancestry specific PRS models (phenotype variance increase: 1.62 for Africans, 1.40 for South Asians and 0.96 for East Asians). Additionally, when we selected the optimal model conditional on ancestry for CAD, HDL-C and LDL-C, hypertension, hypothyroidism and T2D, PRS performance for studied populations was more comparable to what was observed in Europeans. Finally, we were able to independently validate tested models for Europeans, and conducted a PRS-Phewas, identifying cross-trait interplay between cardiometabolic conditions, and between immune-mediated components. CONCLUSION Our work comprehensively evaluated PRS accuracy across a wide range of phenotypes, reducing the uncertainty with respect to which PRS model to choose and in which ancestry group. This evaluation has let us identify specific conditions where implementing risk-prioritization strategies could have practical utility across diverse ancestral groups, contributing to democratizing the implementation of PRS.
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Affiliation(s)
- Sonia Moreno-Grau
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road, Stanford, CA, 94305, USA
| | - Manvi Vernekar
- Genomelink, Inc, 2150 Shattuck Avenue, Berkeley, CA, 94704, USA
| | - Arturo Lopez-Pineda
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA
- , Amphora Health. Batallon Independencia 80, Morelia, Michoacan, 58260, Mexico
- Escuela Nacional de Estudios Superiores, Unidad Morelia, Universidad Nacional Autonoma de México, Antigua Carretera a Pátzcuaro No. 8701, Col. Ex Hacienda de San José de la Huerta, Morelia, Michoacán, C.P. 58190, Mexico
| | | | - Míriam Barrabés
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA
| | | | - Babak Moatamed
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA
| | | | - Zhenning Yu
- Genomelink, Inc, 2150 Shattuck Avenue, Berkeley, CA, 94704, USA
| | | | - Yuta Matsuda
- Genomelink, Inc, 2150 Shattuck Avenue, Berkeley, CA, 94704, USA
| | - Jeffrey D Wall
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA
| | - Alexander G Ioannidis
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road, Stanford, CA, 94305, USA
- University of California Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA
| | | | - Tomohiro Takano
- Genomelink, Inc, 2150 Shattuck Avenue, Berkeley, CA, 94704, USA.
- Japan: Awakens Japan K.K. (Japanese subsidiary of Genomelink, Inc.), 2-11-3, Meguro, Meguro-ku, 1530063, Tokyo, Japan.
| | - Carlos D Bustamante
- Galatea Bio, Inc, 14350 Commerce Way, Miami Lakes, FL, 33146, USA.
- Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road, Stanford, CA, 94305, USA.
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Sharew NT, Clark SR, Schubert KO, Amare AT. Pharmacogenomic scores in psychiatry: systematic review of current evidence. Transl Psychiatry 2024; 14:322. [PMID: 39107294 PMCID: PMC11303815 DOI: 10.1038/s41398-024-02998-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 08/10/2024] Open
Abstract
In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.
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Affiliation(s)
- Nigussie T Sharew
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
- Asrat Woldeyes Health Science Campus, Debre Berhan University, Debre Berhan, Ethiopia
| | - Scott R Clark
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
| | - K Oliver Schubert
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia
- Division of Mental Health, Northern Adelaide Local Health Network, SA Health, Adelaide, Australia
- Headspace Adelaide Early Psychosis - Sonder, Adelaide, SA, Australia
| | - Azmeraw T Amare
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
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Tsuo K, Shi Z, Ge T, Mandla R, Hou K, Ding Y, Pasaniuc B, Wang Y, Martin AR. All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606846. [PMID: 39149254 PMCID: PMC11326295 DOI: 10.1101/2024.08.06.606846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ~750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count (R 2: 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.
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Affiliation(s)
- Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhuozheng Shi
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kangcheng Hou
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Bogdan Pasaniuc
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Kuś A, Sterenborg RBTM, Haug EB, Galesloot TE, Visser WE, Smit JWA, Bednarczuk T, Peeters RP, Åsvold BO, Teumer A, Medici M. Towards Personalized TSH Reference Ranges: A Genetic and Population-Based Approach in Three Independent Cohorts. Thyroid 2024; 34:969-979. [PMID: 38919119 DOI: 10.1089/thy.2024.0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Background: Serum thyroid-stimulating hormone (TSH) measurement is the diagnostic cornerstone for primary thyroid dysfunction. There is high inter-individual but limited intra-individual variation in TSH concentrations, largely due to genetic factors. The currently used wide population-based reference intervals may lead to inappropriate management decisions. Methods: A polygenic score (PGS) including 59 genetic variants was used to calculate genetically determined TSH reference ranges in a thyroid disease-free cohort (n = 6,834). Its effect on reclassification of diagnoses was investigated when compared to using population-based reference ranges. Next, results were validated in a second independent population-based thyroid disease-free cohort (n = 3,800). Potential clinical implications were assessed in a third independent population-based cohort including individuals without thyroid disease (n = 26,321) as well as individuals on levothyroxine (LT4) treatment (n = 1,132). Results: PGS was a much stronger predictor of individual TSH concentrations than FT4 (total variance in TSH concentrations explained 9.2-11.1% vs. 2.4-2.7%, respectively) or any other nongenetic factor (total variance in TSH concentrations explained 0.2-1.8%). Genetically determined TSH reference ranges differed significantly between PGS quartiles in all cohorts, while the differences in FT4 concentrations were absent or only minor. Up to 24.7-30.1% of individuals, previously classified as having subclinical hypo- and hyperthyroidism when using population-based TSH reference ranges, were reclassified as euthyroid when genetically determined TSH reference ranges were applied. Individuals in the higher PGS quartiles had a higher probability of being prescribed LT4 treatment compared to individuals from the lower PGS quartiles (3.3% in Q1 vs. 5.2% in Q4, Pfor trend =1.7 × 10-8). Conclusions: Individual genetic profiles have the potential to personalize TSH reference ranges, with large effects on reclassification of diagnosis and LT4 prescriptions. As the currently used PGS can only predict approximately 10% of inter-individual variation in TSH concentrations, it should be further improved when more genetic variants determining TSH concentrations are identified in future studies.
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Affiliation(s)
- Aleksander Kuś
- Department of Internal Medicine, Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland
| | - Rosalie B T M Sterenborg
- Department of Internal Medicine, Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eirin B Haug
- Department of Public Health and Nursing, HUNT Center for Molecular and Clinical Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tessel E Galesloot
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - W Edward Visser
- Department of Internal Medicine, Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Johannes W A Smit
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tomasz Bednarczuk
- Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland
| | - Robin P Peeters
- Department of Internal Medicine, Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Bjørn O Åsvold
- Department of Public Health and Nursing, HUNT Center for Molecular and Clinical Epidemiology, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Public Health and Nursing, HUNT Research Centre, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Marco Medici
- Department of Internal Medicine, Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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Kelemen M, Vigorito E, Fachal L, Anderson CA, Wallace C. shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores. Am J Hum Genet 2024; 111:1006-1017. [PMID: 38703768 PMCID: PMC11179256 DOI: 10.1016/j.ajhg.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 05/06/2024] Open
Abstract
We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.
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Affiliation(s)
- Martin Kelemen
- Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK; Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK.
| | - Elena Vigorito
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Laura Fachal
- Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
| | | | - Chris Wallace
- Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, UK; MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Step K, Ndong Sima CAA, Mata I, Bardien S. Exploring the role of underrepresented populations in polygenic risk scores for neurodegenerative disease risk prediction. Front Neurosci 2024; 18:1380860. [PMID: 38859922 PMCID: PMC11163124 DOI: 10.3389/fnins.2024.1380860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/14/2024] [Indexed: 06/12/2024] Open
Affiliation(s)
- Kathryn Step
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Carene Anne Alene Ndong Sima
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Ignacio Mata
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
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Saffie Awad P, Makarious MB, Elsayed I, Sanyaolu A, Wild Crea P, Schumacher Schuh AF, Levine KS, Vitale D, Korestky MJ, Kim J, Peixoto Leal T, Perinan MT, Dey S, Noyce AJ, Reyes-Palomares A, Rodriguez-Losada N, Foo JN, Mohamed W, Heilbron K, Norcliffe-Kaufmann L, Rizig M, Okubadejo N, Nalls M, Blauwendraat C, Singleton A, Leonard H, Mata IF, Bandres Ciga S. Insights into Ancestral Diversity in Parkinsons Disease Risk: A Comparative Assessment of Polygenic Risk Scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.28.23299090. [PMID: 38076954 PMCID: PMC10705647 DOI: 10.1101/2023.11.28.23299090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Objectives To evaluate and compare different polygenic risk score (PRS) models in predicting Parkinsons disease (PD) across diverse ancestries, focusing on identifying the most suitable approach for each population and potentially contributing to equitable advancements in precision medicine. Methods We constructed a total of 105 PRS across individual level data from seven diverse ancestries. First, a cross-ancestry conventional PRS comparison was implemented by utilizing the 90 known European risk loci with weighted effects from four independent summary statistics including European, East Asian, Latino/Admixed American, and African/Admixed. These models were adjusted by sex, age, and principal components (28 PRS) and by sex, age, and percentage of admixture (28 PRS) for comparison. Secondly, a novel and refined multi-ancestry best-fit PRS approach was then applied across the seven ancestries by leveraging multi-ancestry meta-analyzed summary statistics and using a p-value thresholding approach (49 PRS) to enhance prediction applicability in a global setting. Results European-based PRS models predicted disease status across all ancestries to differing degrees of accuracy. Ashkenazi Jewish had the highest Odds Ratio (OR): 1.96 (95% CI: 1.69-2.25, p < 0.0001) with an AUC (Area Under the Curve) of 68%. Conversely, the East Asian population, despite having fewer predictive variants (84 out of 90), had an OR of 1.37 (95% CI: 1.32-1.42) and an AUC of 62%, illustrating the cross-ancestry transferability of this model. Lower OR alongside broader confidence intervals were observed in other populations, including Africans (OR =1.38, 95% CI: 1.12-1.63, p=0.001). Adjustment by percentage of admixture did not outperform principal components. Multi-ancestry best-fit PRS models improved risk prediction in European, Ashkenazi Jewish, and African ancestries, yet didn't surpass conventional PRS in admixed populations such as Latino/American admixed and African admixed populations. Interpretation The present study represents a novel and comprehensive assessment of PRS performance across seven ancestries in PD, highlighting the inadequacy of a 'one size fits all' approach in genetic risk prediction. We demonstrated that European based PD PRS models are partially transferable to other ancestries and could be improved by a novel best-fit multi-ancestry PRS, especially in non-admixed populations.
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Ghouse J, Sveinbjörnsson G, Vujkovic M, Seidelin AS, Gellert-Kristensen H, Ahlberg G, Tragante V, Rand SA, Brancale J, Vilarinho S, Lundegaard PR, Sørensen E, Erikstrup C, Bruun MT, Jensen BA, Brunak S, Banasik K, Ullum H, Verweij N, Lotta L, Baras A, Mirshahi T, Carey DJ, Kaplan DE, Lynch J, Morgan T, Schwantes-An TH, Dochtermann DR, Pyarajan S, Tsao PS, Laisk T, Mägi R, Kozlitina J, Tybjærg-Hansen A, Jones D, Knowlton KU, Nadauld L, Ferkingstad E, Björnsson ES, Ulfarsson MO, Sturluson Á, Sulem P, Pedersen OB, Ostrowski SR, Gudbjartsson DF, Stefansson K, Olesen MS, Chang KM, Holm H, Bundgaard H, Stender S. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis. Nat Genet 2024; 56:827-837. [PMID: 38632349 PMCID: PMC11096111 DOI: 10.1038/s41588-024-01720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Affiliation(s)
- Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne-Sofie Seidelin
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Helene Gellert-Kristensen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gustav Ahlberg
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Søren A Rand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Brancale
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Silvia Vilarinho
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pia Rengtved Lundegaard
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
| | | | - Niek Verweij
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Luca Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David E Kaplan
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Timothy Morgan
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medicine, University of California, Irvine, CA, USA
| | - Tae-Hwi Schwantes-An
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Daniel R Dochtermann
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Julia Kozlitina
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | - Einar S Björnsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Morten Salling Olesen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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9
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Momin MM, Zhou X, Hyppönen E, Benyamin B, Lee SH. Cross-ancestry genetic architecture and prediction for cholesterol traits. Hum Genet 2024; 143:635-648. [PMID: 38536467 DOI: 10.1007/s00439-024-02660-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 02/13/2024] [Indexed: 05/18/2024]
Abstract
While cholesterol is essential, a high level of cholesterol is associated with the risk of cardiovascular diseases. Genome-wide association studies (GWASs) have proven successful in identifying genetic variants that are linked to cholesterol levels, predominantly in white European populations. However, the extent to which genetic effects on cholesterol vary across different ancestries remains largely unexplored. Here, we estimate cross-ancestry genetic correlation to address questions on how genetic effects are shared across ancestries. We find significant genetic heterogeneity between ancestries for cholesterol traits. Furthermore, we demonstrate that single nucleotide polymorphisms (SNPs) with concordant effects across ancestries for cholesterol are more frequently found in regulatory regions compared to other genomic regions. Indeed, the positive genetic covariance between ancestries is mostly driven by the effects of the concordant SNPs, whereas the genetic heterogeneity is attributed to the discordant SNPs. We also show that the predictive ability of the concordant SNPs is significantly higher than the discordant SNPs in the cross-ancestry polygenic prediction. The list of concordant SNPs for cholesterol is available in GWAS Catalog. These findings have relevance for the understanding of shared genetic architecture across ancestries, contributing to the development of clinical strategies for polygenic prediction of cholesterol in cross-ancestral settings.
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Affiliation(s)
- Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, 4225, Bangladesh.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
| | - Xuan Zhou
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA, 5000, Australia.
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10
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Elashi AA, Toor SM, Umlai UKI, Al-Sarraj YA, Taheri S, Suhre K, Abou-Samra AB, Albagha OME. Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus. BMC Med Genomics 2024; 17:115. [PMID: 38685053 PMCID: PMC11059680 DOI: 10.1186/s12920-024-01855-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar. METHODS Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment. RESULTS We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel. CONCLUSION Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.
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Affiliation(s)
- Asma A Elashi
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Salman M Toor
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Umm-Kulthum Ismail Umlai
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Yasser A Al-Sarraj
- Qatar Genome Program (QGP), Qatar Foundation Research, Development and Innovation, Qatar Foundation (QF), Doha, P.O. Box 5825, Qatar
| | - Shahrad Taheri
- Qatar Metabolic Institute, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, P.O. Box 24144, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, 510065, New York, USA
| | | | - Omar M E Albagha
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, EH4 2XU, Edinburgh, UK.
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11
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O'Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An ensemble penalized regression method for multi-ancestry polygenic risk prediction. Nat Commun 2024; 15:3238. [PMID: 38622117 PMCID: PMC11271575 DOI: 10.1038/s41467-024-47357-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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12
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Zhang T, Zhou G, Klei L, Liu P, Chouldechova A, Zhao H, Roeder K, G'Sell M, Devlin B. Evaluating and improving health equity and fairness of polygenic scores. HGG ADVANCES 2024; 5:100280. [PMID: 38402414 PMCID: PMC10937319 DOI: 10.1016/j.xhgg.2024.100280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/26/2024] Open
Abstract
Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.
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Affiliation(s)
- Tianyu Zhang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Geyu Zhou
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peng Liu
- Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alexandra Chouldechova
- Microsoft Research NYC, New York, NY 10012, USA; Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Max G'Sell
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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13
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O’Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.15.532652. [PMID: 36993331 PMCID: PMC10055041 DOI: 10.1101/2023.03.15.532652] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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14
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Jin J, Zhan J, Zhang J, Zhao R, O'Connell J, Jiang Y, Buyske S, Gignoux C, Haiman C, Kenny EE, Kooperberg C, North K, Koelsch BL, Wojcik G, Zhang H, Chatterjee N. MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. CELL GENOMICS 2024; 4:100539. [PMID: 38604127 PMCID: PMC11019365 DOI: 10.1016/j.xgen.2024.100539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/07/2023] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19103, USA.
| | | | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | | | - Steven Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA
| | - Christopher Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Eimear E Kenny
- Icahn Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | | | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Haoyu Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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15
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Rosenthal EA, Hsu L, Thomas M, Peters U, Kachulis C, Patterson K, Jarvik GP. Comparing ancestry calibration approaches for a trans-ancestry colorectal cancer polygenic risk score. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.23.23296753. [PMID: 37961088 PMCID: PMC10635167 DOI: 10.1101/2023.10.23.23296753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Colorectal cancer (CRC) is a complex disease with monogenic, polygenic and environmental risk factors. Polygenic risk scores (PRS) are being developed to identify high polygenic risk individuals. Due to differences in genetic background, PRS distributions vary by ancestry, necessitating calibration. Methods We compared four calibration methods using the All of Us Research Program Whole Genome Sequence data for a CRC PRS previously developed in participants of European and East Asian ancestry. The methods contrasted results from linear models with A) the entire data set or an ancestrally diverse training set AND B) covariates including principal components of ancestry or admixture. Calibration with the training set adjusted the variance in addition to the mean. Results All methods performed similarly within ancestry with OR (95% C.I.) per s.d. change in PRS: African 1.5 (1.02, 2.08), Admixed American 2.2 (1.27, 3.85), European 1.6 (1.43, 1.89), and Middle Eastern 1.1 (0.71, 1.63). Using admixture and an ancestrally diverse training set provided distributions closest to standard Normal with accurate upper tail frequencies. Conclusion Although the PRS is predictive of CRC risk for most ancestries, its performance varies by ancestry. Post-hoc calibration preserves the risk prediction within ancestries. Training a calibration model on ancestrally diverse participants to adjust both the mean and variance of the PRS, using admixture as covariates, created standard Normal z-scores. These z-scores can be used to identify patients at high polygenic risk, and can be incorporated into comprehensive risk scores including other known risk factors, allowing for more precise risk estimates.
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16
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Espinosa Dice AL, Lawn RB, Ratanatharathorn A, Roberts AL, Denckla CA, Kim AH, de la Rosa PA, Zhu Y, VanderWeele TJ, Koenen KC. Childhood maltreatment and health in the UK Biobank: triangulation of outcome-wide and polygenic risk score analyses. BMC Med 2024; 22:135. [PMID: 38523269 PMCID: PMC10962116 DOI: 10.1186/s12916-024-03360-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/15/2024] [Indexed: 03/26/2024] Open
Abstract
BACKGROUND Childhood maltreatment is common globally and impacts morbidity, mortality, and well-being. Our understanding of its impact is constrained by key substantive and methodological limitations of extant research, including understudied physical health outcomes and bias due to unmeasured confounding. We address these limitations through a large-scale outcome-wide triangulation study. METHODS We performed two outcome-wide analyses (OWAs) in the UK Biobank. First, we examined the relationship between self-reported maltreatment exposure (number of maltreatment types, via Childhood Trauma Screener) and 414 outcomes in a sub-sample of 157,316 individuals using generalized linear models ("observational OWA"). Outcomes covered a broad range of health themes including health behaviors, cardiovascular disease, digestive health, socioeconomic status, and pain. Second, we examined the relationship between a polygenic risk score for maltreatment and 298 outcomes in a non-overlapping sample of 243,006 individuals ("genetic OWA"). We triangulated results across OWAs based on differing sources of bias. RESULTS Overall, 23.8% of the analytic sample for the observational OWA reported at least one maltreatment type. Of 298 outcomes examined in both OWAs, 25% were significant in both OWAs and concordant in the direction of association. Most of these were considered robust in the observational OWA according to sensitivity analyses and included outcomes such as marital separation (OR from observational OWA, ORo = 1.25 (95% CI: 1.21, 1.29); OR from genetic OWA, ORg = 1.06 (1.03, 1.08)), major diet changes due to illness (ORo = 1.27 (1.24, 1.29); ORg = 1.01 (1.00, 1.03)), certain intestinal diseases (ORo = 1.14 (1.10, 1.18); ORg = 1.03 (1.01, 1.06)), hearing difficulty with background noise (ORo = 1.11 (1.11, 1.12); ORg = 1.01 (1.00, 1.01)), knee arthrosis (ORo = 1.13 (1.09, 1.18); ORg = 1.03 (1.01, 1.05)), frequent sleeplessness (ORo = 1.21 (1.20, 1.23); ORg = 1.02 (1.01, 1.03)), and low household income (ORo = 1.28 (1.26, 1.31); ORg = 1.02 (1.01, 1.03)). Approximately 62% of results were significant in the observational OWA but not the genetic OWA, including numerous cardiovascular outcomes. Only 6 outcomes were significant in the genetic OWA and null in the observational OWA; these included diastolic blood pressure and glaucoma. No outcomes were statistically significant in opposite directions in the two analyses, and 11% were not significant in either OWA. CONCLUSIONS Our findings underscore the far-reaching negative effects of childhood maltreatment in later life and the utility of an outcome-wide triangulation design with sensitivity analyses for improving causal inference.
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Affiliation(s)
- Ana Lucia Espinosa Dice
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Rebecca B Lawn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York City, NY, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christy A Denckla
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ariel H Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Pedro A de la Rosa
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Institute for Culture and Society, University of Navarra, Pamplona, Spain
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Psychiatric Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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17
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Peyrot WJ, Panagiotaropoulou G, Olde Loohuis LM, Adams MJ, Awasthi S, Ge T, McIntosh AM, Mitchell BL, Mullins N, O'Connell KS, Penninx BWJH, Posthuma D, Ripke S, Ruderfer DM, Uffelmann E, Vilhjalmsson BJ, Zhu Z, Smoller JW, Price AL. Distinguishing different psychiatric disorders using DDx-PRS. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302228. [PMID: 38352307 PMCID: PMC10862992 DOI: 10.1101/2024.02.02.24302228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
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18
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Sun Q, Rowland BT, Chen J, Mikhaylova AV, Avery C, Peters U, Lundin J, Matise T, Buyske S, Tao R, Mathias RA, Reiner AP, Auer PL, Cox NJ, Kooperberg C, Thornton TA, Raffield LM, Li Y. Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI. Nat Commun 2024; 15:1016. [PMID: 38310129 PMCID: PMC10838303 DOI: 10.1038/s41467-024-45135-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bryce T Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Tara Matise
- Department of Genetics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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19
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Liao K, Zöllner S. A Stacking Framework for Polygenic Risk Prediction in Admixed Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.31.24302103. [PMID: 38434717 PMCID: PMC10907988 DOI: 10.1101/2024.01.31.24302103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Polygenic risk scores (PRS) are summaries of an individual's personalized genetic risk for a trait or disease. However, PRS often perform poorly for phenotype prediction when the ancestry of the target population does not match the population in which GWAS effect sizes were estimated. For many populations this can be addressed by performing GWAS in the target population. However, admixed individuals (whose genomes can be traced to multiple ancestral populations) lie on an ancestry continuum and are not easily represented as a discrete population. Here, we propose slaPRS (stacking local ancestry PRS), which incorporates multiple ancestry GWAS to alleviate the ancestry dependence of PRS in admixed samples. slaPRS uses ensemble learning (stacking) to combine local population specific PRS in regions across the genome. We compare slaPRS to single population PRS and a method that combines single population PRS globally. In simulations, slaPRS outperformed existing approaches and reduced the ancestry dependence of PRS in African Americans. In lipid traits from African British individuals (UK Biobank), slaPRS again improved on single population PRS while performing comparably to the globally combined PRS. slaPRS provides a data-driven and flexible framework to incorporate multiple population-specific GWAS and local ancestry in samples of admixed ancestry.
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Affiliation(s)
- Kevin Liao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA
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20
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn NS, Arias J, Belbin G, Below JE, Berndt SI, Chung WK, Cimino JJ, Clayton EW, Connolly JJ, Crosslin DR, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth RR, Ge T, Glessner JT, Gordon AS, Patterson C, Hakonarson H, Harden M, Harr M, Hirschhorn JN, Hoggart C, Hsu L, Irvin MR, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos RJF, Luo Y, Malolepsza E, Manolio TA, Martin LJ, McCarthy L, McNally EM, Meigs JB, Mersha TB, Mosley JD, Musick A, Namjou B, Pai N, Pesce LL, Peters U, Peterson JF, Prows CA, Puckelwartz MJ, Rehm HL, Roden DM, Rosenthal EA, Rowley R, Sawicki KT, Schaid DJ, Smit RAJ, Smith JL, Smoller JW, Thomas M, Tiwari H, Toledo DM, Vaitinadin NS, Veenstra D, Walunas TL, Wang Z, Wei WQ, Weng C, Wiesner GL, Yin X, Kenny EE. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 2024; 30:480-487. [PMID: 38374346 PMCID: PMC10878968 DOI: 10.1038/s41591-024-02796-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.
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Affiliation(s)
| | - Leah C Kottyan
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Josh Arias
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gillian Belbin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Sonja I Berndt
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - James J Cimino
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | - David R Crosslin
- Tulane University, New Orleans, LA, USA
- University of Washington, Seattle, WA, USA
| | | | | | - QiPing Feng
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Tian Ge
- Mass General Brigham, Boston, MA, USA
| | | | | | | | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Clive Hoggart
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Li Hsu
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | | | | | | | - Amit Khera
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Katie Larkin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nita Limdi
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuan Luo
- Northwestern University, Evanston, IL, USA
| | | | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Li McCarthy
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Nihal Pai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Cynthia A Prows
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dan M Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | | | - Hemant Tiwari
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | | | | | - Zhe Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
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21
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Cui R, Elzur RA, Kanai M, Ulirsch JC, Weissbrod O, Daly MJ, Neale BM, Fan Z, Finucane HK. Improving fine-mapping by modeling infinitesimal effects. Nat Genet 2024; 56:162-169. [PMID: 38036779 PMCID: PMC11056999 DOI: 10.1038/s41588-023-01597-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Fine-mapping aims to identify causal genetic variants for phenotypes. Bayesian fine-mapping algorithms (for example, SuSiE, FINEMAP, ABF and COJO-ABF) are widely used, but assessing posterior probability calibration remains challenging in real data, where model misspecification probably exists, and true causal variants are unknown. We introduce replication failure rate (RFR), a metric to assess fine-mapping consistency by downsampling. SuSiE, FINEMAP and COJO-ABF show high RFR, indicating potential overconfidence in their output. Simulations reveal that nonsparse genetic architecture can lead to miscalibration, while imputation noise, nonuniform distribution of causal variants and quality control filters have minimal impact. Here we present SuSiE-inf and FINEMAP-inf, fine-mapping methods modeling infinitesimal effects alongside fewer larger causal effects. Our methods show improved calibration, RFR and functional enrichment, competitive recall and computational efficiency. Notably, using our methods' posterior effect sizes substantially increases polygenic risk score accuracy over SuSiE and FINEMAP. Our work improves causal variant identification for complex traits, a fundamental goal of human genetics.
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Affiliation(s)
- Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Roy A Elzur
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Jacob C Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhou Fan
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA.
| | - Hilary K Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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22
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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23
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Zhang MJ, Durvasula A, Chiang C, Koch EM, Strober BJ, Shi H, Barton AR, Kim SS, Weissbrod O, Loh PR, Gazal S, Sunyaev S, Price AL. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection. RESEARCH SQUARE 2023:rs.3.rs-3707248. [PMID: 38168385 PMCID: PMC10760228 DOI: 10.21203/rs.3.rs-3707248/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
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Affiliation(s)
- Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Colby Chiang
- Department of Pediatrics, Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA
| | - Evan M. Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alison R. Barton
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Samuel S. Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Shamil Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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24
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Zhang MJ, Durvasula A, Chiang C, Koch EM, Strober BJ, Shi H, Barton AR, Kim SS, Weissbrod O, Loh PR, Gazal S, Sunyaev S, Price AL. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.04.23299391. [PMID: 38106023 PMCID: PMC10723494 DOI: 10.1101/2023.12.04.23299391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
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Affiliation(s)
- Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Colby Chiang
- Department of Pediatrics, Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Evan M Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alison R Barton
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Shamil Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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25
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Zhai S, Mehrotra DV, Shen J. Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities. Brief Bioinform 2023; 25:bbad470. [PMID: 38152980 PMCID: PMC10782924 DOI: 10.1093/bib/bbad470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023] Open
Abstract
Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.
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Affiliation(s)
- Song Zhai
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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26
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Jain PR, Burch M, Martinez M, Mir P, Fichna JP, Zekanowski C, Rizzo R, Tümer Z, Barta C, Yannaki E, Stamatoyannopoulos J, Drineas P, Paschou P. Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation. BMC Genom Data 2023; 24:70. [PMID: 37986041 PMCID: PMC10662565 DOI: 10.1186/s12863-023-01168-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/20/2023] [Indexed: 11/22/2023] Open
Abstract
Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.
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Affiliation(s)
- Pritesh R Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Myson Burch
- Department of Computer Sciences, Purdue University, West Lafayette, IN, USA
| | - Melanie Martinez
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Pablo Mir
- Unidad de Trastornos del Movimiento, Instituto de Biomedicina de Sevilla (IBiS). Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Jakub P Fichna
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Neurogenetics and Functional Genomics, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
| | - Cezary Zekanowski
- Department of Neurogenetics and Functional Genomics, Mossakowski Medical Research Institute, Polish Academy of Sciences, Warsaw, Poland
| | - Renata Rizzo
- Child and Adolescent Neurology and Psychiatry, Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Zeynep Tümer
- Department of Clinical Genetics, Kennedy Center, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Csaba Barta
- Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Evangelia Yannaki
- Hematology Department- Hematopoietic Cell Transplantation Unit, Gene and Cell Therapy Center, George Papanikolaou Hospital, Thessaloniki, Greece
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Stamatoyannopoulos
- Altius Institute for Biomedical Sciences, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Medicine, Division of Oncology, University of Washington, Seattle, WA, USA
| | - Petros Drineas
- Department of Computer Sciences, Purdue University, West Lafayette, IN, USA
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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27
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Mbuya-Bienge C, Pashayan N, Kazemali CD, Lapointe J, Simard J, Nabi H. A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population. Cancers (Basel) 2023; 15:5380. [PMID: 38001640 PMCID: PMC10670420 DOI: 10.3390/cancers15225380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/26/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.
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Affiliation(s)
- Cynthia Mbuya-Bienge
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London WC1E 6BT, UK;
| | - Cornelia D. Kazemali
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Julie Lapointe
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Jacques Simard
- Endocrinology and Nephology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada;
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Hermann Nabi
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
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28
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Busby GB, Kulm S, Bolli A, Kintzle J, Domenico PD, Bottà G. Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments. Nat Commun 2023; 14:7105. [PMID: 37925478 PMCID: PMC10625612 DOI: 10.1038/s41467-023-42897-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 10/25/2023] [Indexed: 11/06/2023] Open
Abstract
Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.
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Affiliation(s)
| | - Scott Kulm
- Allelica Inc, 447 Broadway, New York, NY, 10013, USA
| | | | - Jen Kintzle
- Allelica Inc, 447 Broadway, New York, NY, 10013, USA
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29
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Tanigawa Y, Kellis M. Power of inclusion: Enhancing polygenic prediction with admixed individuals. Am J Hum Genet 2023; 110:1888-1902. [PMID: 37890495 PMCID: PMC10645553 DOI: 10.1016/j.ajhg.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 09/22/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.
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Affiliation(s)
- Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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30
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Jeon S, Lo YC, Morimoto LM, Metayer C, Ma X, Wiemels JL, de Smith AJ, Chiang CWK. Evaluating genomic polygenic risk scores for childhood acute lymphoblastic leukemia in Latinos. HGG ADVANCES 2023; 4:100239. [PMID: 37710962 PMCID: PMC10550840 DOI: 10.1016/j.xhgg.2023.100239] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 09/16/2023] Open
Abstract
The utility of polygenic risk score (PRS) models has not been comprehensively evaluated for childhood acute lymphoblastic leukemia (ALL), the most common type of cancer in children. Previous PRS models for ALL were based on significant loci observed in genome-wide association studies (GWASs), even though genomic PRS models have been shown to improve prediction performance for a number of complex diseases. In the United States, Latino (LAT) children have the highest risk of ALL, but the transferability of PRS models to LAT children has not been studied. In this study, we constructed and evaluated genomic PRS models based on either non-Latino White (NLW) GWAS or a multi-ancestry GWAS. We found that the best PRS models performed similarly between held-out NLW and LAT samples (PseudoR2 = 0.086 ± 0.023 in NLW vs. 0.060 ± 0.020 in LAT), and can be improved for LAT if we performed GWAS in LAT-only (PseudoR2 = 0.116 ± 0.026) or multi-ancestry samples (PseudoR2 = 0.131 ± 0.025). However, the best genomic models currently do not have better prediction accuracy than a conventional model using all known ALL-associated loci in the literature (PseudoR2 = 0.166 ± 0.025), which includes loci from GWAS populations that we could not access to train genomic PRS models. Our results suggest that larger and more inclusive GWASs may be needed for genomic PRS to be useful for ALL. Moreover, the comparable performance between populations may suggest a more oligogenic architecture for ALL, where some large effect loci may be shared between populations. Future PRS models that move away from the infinite causal loci assumption may further improve PRS for ALL.
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Affiliation(s)
- Soyoung Jeon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ying Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Libby M Morimoto
- Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Catherine Metayer
- Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Xiaomei Ma
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Joseph L Wiemels
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam J de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, Los Angeles, CA, USA.
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31
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Wang Y, Kanai M, Tan T, Kamariza M, Tsuo K, Yuan K, Zhou W, Okada Y, Huang H, Turley P, Atkinson EG, Martin AR. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. CELL GENOMICS 2023; 3:100408. [PMID: 37868036 PMCID: PMC10589629 DOI: 10.1016/j.xgen.2023.100408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/21/2023] [Accepted: 08/22/2023] [Indexed: 10/24/2023]
Abstract
Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - the BioBank Japan Project
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Society of Fellows, Harvard University, Cambridge, MA 02138, USA
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Patrick Turley
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Thomas M, Su YR, Rosenthal EA, Sakoda LC, Schmit SL, Timofeeva MN, Chen Z, Fernandez-Rozadilla C, Law PJ, Murphy N, Carreras-Torres R, Diez-Obrero V, van Duijnhoven FJB, Jiang S, Shin A, Wolk A, Phipps AI, Burnett-Hartman A, Gsur A, Chan AT, Zauber AG, Wu AH, Lindblom A, Um CY, Tangen CM, Gignoux C, Newton C, Haiman CA, Qu C, Bishop DT, Buchanan DD, Crosslin DR, Conti DV, Kim DH, Hauser E, White E, Siegel E, Schumacher FR, Rennert G, Giles GG, Hampel H, Brenner H, Oze I, Oh JH, Lee JK, Schneider JL, Chang-Claude J, Kim J, Huyghe JR, Zheng J, Hampe J, Greenson J, Hopper JL, Palmer JR, Visvanathan K, Matsuo K, Matsuda K, Jung KJ, Li L, Le Marchand L, Vodickova L, Bujanda L, Gunter MJ, Matejcic M, Jenkins MA, Slattery ML, D'Amato M, Wang M, Hoffmeister M, Woods MO, Kim M, Song M, Iwasaki M, Du M, Udaltsova N, Sawada N, Vodicka P, Campbell PT, Newcomb PA, Cai Q, Pearlman R, Pai RK, Schoen RE, Steinfelder RS, Haile RW, Vandenputtelaar R, Prentice RL, Küry S, Castellví-Bel S, Tsugane S, Berndt SI, Lee SC, Brezina S, Weinstein SJ, Chanock SJ, Jee SH, Kweon SS, Vadaparampil S, Harrison TA, Yamaji T, Keku TO, Vymetalkova V, Arndt V, Jia WH, Shu XO, Lin Y, Ahn YO, Stadler ZK, Van Guelpen B, Ulrich CM, Platz EA, Potter JD, Li CI, Meester R, Moreno V, Figueiredo JC, Casey G, Lansdorp Vogelaar I, Dunlop MG, Gruber SB, Hayes RB, Pharoah PDP, Houlston RS, Jarvik GP, Tomlinson IP, Zheng W, Corley DA, Peters U, Hsu L. Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations. Nat Commun 2023; 14:6147. [PMID: 37783704 PMCID: PMC10545678 DOI: 10.1038/s41467-023-41819-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/19/2023] [Indexed: 10/04/2023] Open
Abstract
Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.
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Affiliation(s)
- Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Elisabeth A Rosenthal
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, 98195, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, USA
| | - Maria N Timofeeva
- Danish Institute for Advanced Study (DIAS), Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, U, Germany
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ceres Fernandez-Rozadilla
- Instituto de Investigacion Sanitaria de Santiago (IDIS), Choupana sn, 15706, Santiago de Compostela, Spain
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, The Institute of Cancer Reseach, London, SW7 3RP, UK
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Robert Carreras-Torres
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190, Girona, Spain
| | - Virginia Diez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, 08908, Spain
| | | | - Shangqing Jiang
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul National University Cancer Research Institute, Seoul, South Korea
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | | | - Andrea Gsur
- .Center for Cancer Research, Medical University Vienna, Vienna, Austria
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Ann G Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna H Wu
- University of Southern California, Preventative Medicine, Los Angeles, CA, USA
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Gignoux
- Colorado Center for Personalized Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA
| | - Christina Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Christopher A Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3000, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3000, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC, 3000, Australia
| | - David R Crosslin
- Department of Bioinformatics and Medical Education, University of Washington Medical Center, Seattle, WA, 98195, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, Hallym University College of Medicine, Okcheon-dong, South Korea
| | - Elizabeth Hauser
- VA Cooperative Studies Program Epidemiology Center, Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Erin Siegel
- Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Isao Oze
- .Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Jae Hwan Oh
- .Research Institute and Hospital, National Cancer Center, Goyang, South Korea, South Korea
| | - Jeffrey K Lee
- .Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48104, USA
| | | | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, South Korea
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jiayin Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden, Germany
| | - Joel Greenson
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48104, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Julie R Palmer
- Slone Epidemiology Center, School of Medicine, Boston University, Boston, MA, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keitaro Matsuo
- Division of Molecular and Clinical Epidemiology, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Keum Ji Jung
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Luis Bujanda
- Department of Gastroenterology, Biodonostia Health Research Institute, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Universidad del País Vasco (UPV/EHU), San Sebastián, Spain
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Mark A Jenkins
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3000, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Mauro D'Amato
- Department of Medicine and Surgery, LUM University, Camassima, Italy
- Gastrointestinal Genetics Lab, CIC bioGUNE-BRTA, Derio, Spain
| | - Meilin Wang
- Department of Environmental Genomics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Michelle Kim
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Mingyang Song
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Epidemiology and Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Mulong Du
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Natalia Udaltsova
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Robert S Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Robert W Haile
- Samuel Oschin Comprehensive Cancer Institute, CEDARS-SINAI, Los Angeles, CA, USA
| | - Rosita Vandenputtelaar
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Sébastien Küry
- Nantes Université, CHU Nantes, Service de Génétique Médicale, F-44000, Nantes, France
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain
| | - Shoichiro Tsugane
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Soo Chin Lee
- National University Cancer Institute, Singapore, Singapore
| | - Stefanie Brezina
- .Center for Cancer Research, Medical University Vienna, Vienna, Austria
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sun Ha Jee
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Susan Vadaparampil
- Departments of Epidemiology and Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Xiao-Ou Shu
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul National University Cancer Research Institute, Seoul, South Korea
| | - Zsofia K Stadler
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Reinier Meester
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jane C Figueiredo
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Iris Lansdorp Vogelaar
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Malcolm G Dunlop
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, U, Germany
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Reseach, London, SW7 3RP, UK
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, 98195, USA
| | - Ian P Tomlinson
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Department of Gastroenterology, Kaiser Permanente Medical Center, San Francisco, CA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
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Zhang H, Zhan J, Jin J, Zhang J, Lu W, Zhao R, Ahearn TU, Yu Z, O'Connell J, Jiang Y, Chen T, Okuhara D, Garcia-Closas M, Lin X, Koelsch BL, Chatterjee N. A new method for multiancestry polygenic prediction improves performance across diverse populations. Nat Genet 2023; 55:1757-1768. [PMID: 37749244 PMCID: PMC10923245 DOI: 10.1038/s41588-023-01501-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/16/2023] [Indexed: 09/27/2023]
Abstract
Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.
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Affiliation(s)
- Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | | | - Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Wenxuan Lu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Thomas U Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Zhi Yu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tony Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | | | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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Zhang T, Klei L, Liu P, Chouldechova A, Roeder K, G'Sell M, Devlin B. Evaluating and Improving Health Equity and Fairness of Polygenic Scores. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559051. [PMID: 37790341 PMCID: PMC10542523 DOI: 10.1101/2023.09.22.559051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Polygenic scores (PGS) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWAS, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum. In the simulation settings we explore, Joint-Lassosum provides more accurate PGS compared with other methods, especially when measured in terms of fairness. Like all PGS methods, Joint-Lassosum requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how Joint-Lassosum can help mitigate fairness-related harms that might result from the use of PGS scores in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWAS for different ancestries, Joint-Lassosum is an effective approach for enhancing portability and reducing predictive bias.
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Jin J, Zhan J, Zhang J, Zhao R, O’Connell J, Jiang Y, Buyske S, Gignoux C, Haiman C, Kenny EE, Kooperberg C, North K, Koelsch BL, Wojcik G, Zhang H, Chatterjee N. MUSSEL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.12.536510. [PMID: 37090648 PMCID: PMC10120638 DOI: 10.1101/2023.04.12.536510] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. MUSSEL conducts Bayesian hierarchical modeling under a MUltivariate Spike-and-Slab model for effect-size distribution and incorporates an Ensemble Learning step using super learner to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Steven Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ, USA
| | - Christopher Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eimear E. Kenny
- Icahn Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | | | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Haoyu Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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36
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Lee WJ, Cheng H, Whitney BM, Nance RM, Britton SR, Jordahl K, Lindstrom S, Ruderman SA, Kitahata MM, Saag MS, Willig AL, Burkholder G, Eron JJ, Kovacic JC, Björkegren JLM, Mathews WC, Cachay E, Feinstein MJ, Budoff M, Hunt PW, Moore RD, Keruly J, McCaul ME, Chander G, Webel A, Mayer KH, Delaney JA, Crane PK, Martinez C, Crane HM, Hao K, Peter I. Polygenic risk scores point toward potential genetic mechanisms of type 2 myocardial infarction in people with HIV. Int J Cardiol 2023; 383:15-23. [PMID: 37149004 PMCID: PMC10247524 DOI: 10.1016/j.ijcard.2023.04.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 04/03/2023] [Accepted: 04/30/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND People with human immunodeficiency virus (HIV) infection (PWH) are at higher risk of myocardial infarction (MI) than those without HIV. About half of MIs in PWH are type 2 (T2MI), resulting from mismatch between myocardial oxygen supply and demand, in contrast to type 1 MI (T1MI), which is due to primary plaque rupture or coronary thrombosis. Despite worse survival and rising incidence in the general population, evidence-based treatment recommendations for T2MI are lacking. We used polygenic risk scores (PRS) to explore genetic mechanisms of T2MI compared to T1MI in PWH. METHODS We derived 115 PRS for MI-related traits in 9541 PWH enrolled in the Centers for AIDS Research Network of Integrated Clinical Systems cohort with adjudicated T1MI and T2MI. We applied multivariate logistic regression analyses to determine the association with T1MI and T2MI. Based on initial findings, we performed gene set enrichment analysis of the top variants composing PRS associated with T2MI. RESULTS We found that T1MI was strongly associated with PRS for cardiovascular disease, lipid profiles, and metabolic traits. In contrast, PRS for alcohol dependence and cholecystitis, significantly enriched in energy metabolism pathways, were predictive of T2MI risk. The association remained after the adjustment for actual alcohol consumption. CONCLUSIONS We demonstrate distinct genetic traits associated with T1MI and T2MI among PWH further highlighting their etiological differences and supporting the role of energy regulation in T2MI pathogenesis.
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Affiliation(s)
- Won Jun Lee
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York, USA
| | - Haoxiang Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York, USA
| | - Bridget M Whitney
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA
| | - Robin M Nance
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA
| | - Sierra R Britton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York, USA; Department of Population Health Sciences, Weill Cornell Medical College of Cornell University, New York, NY, USA
| | - Kristina Jordahl
- Department of Epidemiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Stephanie A Ruderman
- Department of Epidemiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Mari M Kitahata
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA
| | - Michael S Saag
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Amanda L Willig
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Greer Burkholder
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Joseph J Eron
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, NY, New York, USA; Victor Chang Cardiac Research Institute, Darlinghurst, Australia; St Vincent's Clinical School, University of NSW, Australia
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York, USA; Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Karolinska Universitetssjukhuset, Huddinge, Sweden
| | | | - Edward Cachay
- Department of Medicine, University of California San Diego, San Diego, CA, USA
| | - Matthew J Feinstein
- Department of Medicine, Northwestern University Feinberg School of Medicine, Evanston, IL, USA
| | - Mathew Budoff
- Deparment of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Peter W Hunt
- Division of Experimental Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Richard D Moore
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jeanne Keruly
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Mary E McCaul
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Geetanjali Chander
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA; Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Allison Webel
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA; School of Nursing, University of Washington, Seattle, WA, USA
| | | | - Joseph A Delaney
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA; College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Manitoba, Canada
| | - Paul K Crane
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA
| | - Claudia Martinez
- Department of Medicine, Division of Cardiology, University of Miami Miller School of Medicine, Florida, USA
| | - Heidi M Crane
- Department of Medicine, University of Washington School of Public Health, Seattle, WA, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York, USA
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY, New York, USA.
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37
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Bocher O, Gilly A, Park YC, Zeggini E, Morris AP. Bridging the diversity gap: Analytical and study design considerations for improving the accuracy of trans-ancestry genetic prediction. HGG ADVANCES 2023; 4:100214. [PMID: 37448981 PMCID: PMC10336686 DOI: 10.1016/j.xhgg.2023.100214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Genetic prediction of common complex disease risk is an essential component of precision medicine. Currently, genome-wide association studies (GWASs) are mostly composed of European-ancestry samples and resulting polygenic scores (PGSs) have been shown to poorly transfer to other ancestries partly due to heterogeneity of allelic effects between populations. Fixed-effects (FETA) and random-effects (RETA) trans-ancestry meta-analyses do not model such ancestry-related heterogeneity, while ancestry-specific (AS) scores may suffer from low power due to low sample sizes. In contrast, trans-ancestry meta-regression (TAMR) builds ancestry-aware PGS that account for more complex trans-ancestry architectures. Here, we examine the predictive performance of these four PGSs under multiple genetic architectures and ancestry configurations. We show that the predictive performance of FETA and RETA is strongly affected by cross-ancestry genetic heterogeneity, while AS PGS performance decreases in under-represented target populations. TAMR PGS is also impacted by heterogeneity but maintains good prediction performance in most situations, especially in ancestry-diverse scenarios. In simulations of human complex traits, TAMR scores currently explain 25% more phenotypic variance than AS in triglyceride levels and 33% more phenotypic variance than FETA in type 2 diabetes in most non-European populations. Importantly, a high proportion of non-European-ancestry individuals is needed to reach prediction levels that are comparable in those populations to the one observed in European-ancestry studies. Our results highlight the need to rebalance the ancestral composition of GWAS to enable accurate prediction in non-European-ancestry groups, and demonstrate the relevance of meta-regression approaches for compensating some of the current population biases in GWAS.
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Affiliation(s)
| | | | | | - Eleftheria Zeggini
- ITG, Helmholtz Zentrum München, Munich, Germany
- Technical University of Munich, Munich, Germany
- Klinikum Rechts der Isar, Munich, Germany
| | - Andrew P. Morris
- ITG, Helmholtz Zentrum München, Munich, Germany
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
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38
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Lehmann B, Mackintosh M, McVean G, Holmes C. Optimal strategies for learning multi-ancestry polygenic scores vary across traits. Nat Commun 2023; 14:4023. [PMID: 37419925 PMCID: PMC10328935 DOI: 10.1038/s41467-023-38930-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/22/2023] [Indexed: 07/09/2023] Open
Abstract
Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.
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Affiliation(s)
- Brieuc Lehmann
- Department of Statistical Science, University College London, London, UK.
| | | | - Gil McVean
- Big Data Institute, University of Oxford, Oxford, UK
| | - Chris Holmes
- The Alan Turing Institute, London, UK
- Big Data Institute, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
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39
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Darst BF, Shen J, Madduri RK, Rodriguez AA, Xiao Y, Sheng X, Saunders EJ, Dadaev T, Brook MN, Hoffmann TJ, Muir K, Wan P, Le Marchand L, Wilkens L, Wang Y, Schleutker J, MacInnis RJ, Cybulski C, Neal DE, Nordestgaard BG, Nielsen SF, Batra J, Clements JA, Cancer BioResource AP, Grönberg H, Pashayan N, Travis RC, Park JY, Albanes D, Weinstein S, Mucci LA, Hunter DJ, Penney KL, Tangen CM, Hamilton RJ, Parent MÉ, Stanford JL, Koutros S, Wolk A, Sørensen KD, Blot WJ, Yeboah ED, Mensah JE, Lu YJ, Schaid DJ, Thibodeau SN, West CM, Maier C, Kibel AS, Cancel-Tassin G, Menegaux F, John EM, Grindedal EM, Khaw KT, Ingles SA, Vega A, Rosenstein BS, Teixeira MR, Kogevinas M, Cannon-Albright L, Huff C, Multigner L, Kaneva R, Leach RJ, Brenner H, Hsing AW, Kittles RA, Murphy AB, Logothetis CJ, Neuhausen SL, Isaacs WB, Nemesure B, Hennis AJ, Carpten J, Pandha H, De Ruyck K, Xu J, Razack A, Teo SH, Newcomb LF, Fowke JH, Neslund-Dudas C, Rybicki BA, Gamulin M, Usmani N, Claessens F, Gago-Dominguez M, Castelao JE, Townsend PA, Crawford DC, Petrovics G, Casey G, Roobol MJ, Hu JF, Berndt SI, Van Den Eeden SK, Easton DF, Chanock SJ, Cook MB, Wiklund F, Witte JS, Eeles RA, Kote-Jarai Z, Watya S, Gaziano JM, Justice AC, Conti DV, Haiman CA. Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry. Am J Hum Genet 2023; 110:1200-1206. [PMID: 37311464 PMCID: PMC10357473 DOI: 10.1016/j.ajhg.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/15/2023] Open
Abstract
Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.
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Affiliation(s)
- Burcu F Darst
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Jiayi Shen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yukai Xiao
- Argonne National Laboratory, Lemont, IL, USA
| | - Xin Sheng
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Peggy Wan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Lynne Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Ying Wang
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | | | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, VIC, Australia
| | - Cezary Cybulski
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - David E Neal
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headington, Oxford, UK; University of Cambridge, Department of Oncology, Addenbrooke's Hospital, Cambridge, UK; Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Sune F Nielsen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Jyotsna Batra
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia; Translational Research Institute, Brisbane, QLD, Australia
| | - Judith A Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia; Translational Research Institute, Brisbane, QLD, Australia
| | - Australian Prostate Cancer BioResource
- Translational Research Institute, Brisbane, QLD, Australia; Australian Prostate Cancer Research Centre-Qld, Queensland University of Technology, Brisbane, QLD, Australia; Prostate Cancer Research Program, Monash University, Melbourne, VIC, Australia; Dame Roma Mitchell Cancer Centre, University of Adelaide, Adelaide, SA, Australia; Chris O'Brien Lifehouse and The Kinghorn Cancer Centre, Sydney, NSW, Australia
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Nora Pashayan
- University College London, Department of Applied Health Research, London, UK; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephanie Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - David J Hunter
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kathryn L Penney
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Robert J Hamilton
- Department of Surgical Oncology, Princess Margaret Cancer Centre, Toronto, ON, Canada; Department of Surgery (Urology), University of Toronto, Toronto, ON, Canada
| | - Marie-Élise Parent
- Epidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique, Laval, QC, Canada; Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, QC, Canada
| | - Janet L Stanford
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Alicja Wolk
- Division of Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Karina D Sørensen
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus N, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark
| | - William J Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; International Epidemiology Institute, Rockville, MD, USA
| | - Edward D Yeboah
- University of Ghana Medical School, Accra, Ghana; Korle Bu Teaching Hospital, Accra, Ghana
| | - James E Mensah
- University of Ghana Medical School, Accra, Ghana; Korle Bu Teaching Hospital, Accra, Ghana
| | - Yong-Jie Lu
- Centre for Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, London, UK
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Catharine M West
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Radiotherapy Related Research, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | | | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Géraldine Cancel-Tassin
- CeRePP, Tenon Hospital, Paris, France; Sorbonne Universite, GRC 5 Predictive Onco-urology, Tenon Hospital, Paris, France
| | - Florence Menegaux
- "Exposome and Heredity", CESP (UMR 1018), Faculté de Médecine, Université Paris-Saclay, Inserm, Gustave Roussy, Villejuif, France
| | - Esther M John
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA; Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, UK
| | - Sue A Ingles
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ana Vega
- Fundación Pública Galega Medicina Xenómica, Santiago De Compostela, Spain; Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago De Compostela, Spain; Centro de Investigación en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Barry S Rosenstein
- Department of Radiation Oncology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal; Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Manolis Kogevinas
- ISGlobal, Barcelona, Spain; IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Lisa Cannon-Albright
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Chad Huff
- Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Luc Multigner
- University Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail), Rennes, France
| | - Radka Kaneva
- Molecular Medicine Center, Department of Medical Chemistry and Biochemistry, Medical University of Sofia, Sofia, Bulgaria
| | - Robin J Leach
- Department of Urology, Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Ann W Hsing
- Department of Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Rick A Kittles
- Division of Health Equities, Department of Population Sciences, City of Hope, Duarte, CA, USA
| | - Adam B Murphy
- Department of Urology, Northwestern University, Chicago, IL, USA
| | - Christopher J Logothetis
- The University of Texas M. D. Anderson Cancer Center, Department of Genitourinary Medical Oncology, Houston, TX, USA
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, USA
| | - William B Isaacs
- James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institution, Baltimore, MD, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Anselm J Hennis
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY, USA; Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados
| | - John Carpten
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hardev Pandha
- Faculty of Health and Medical Sciences, The University of Surrey, Guildford, Surrey, UK
| | - Kim De Ruyck
- Ghent University, Faculty of Medicine and Health Sciences, Basic Medical Sciences, Gent, Belgium
| | - Jianfeng Xu
- Program for Personalized Cancer Care and Department of Surgery, NorthShore University HealthSystem, Evanston, IL, USA
| | - Azad Razack
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Soo-Hwang Teo
- Cancer Research Malaysia (CRM), Outpatient Centre, Subang Jaya Medical Centre, Subang Jaya, Selangor, Malaysia
| | - Lisa F Newcomb
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Urology, University of Washington, Seattle, WA, USA
| | - Jay H Fowke
- Division of Epidemiology, Department of Preventive Medicine, The University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Benjamin A Rybicki
- Department of Public Health Sciences, Henry Ford Hospital, Detroit, MI, USA
| | - Marija Gamulin
- Department of Oncology, University Hospital Centre Zagreb, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Nawaid Usmani
- Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, Canada; Division of Radiation Oncology, Cross Cancer Institute, Edmonton, AB, Canada
| | - Frank Claessens
- Molecular Endocrinology Laboratory, Department of Cellular and Molecular Medicine, Leuven, Belgium
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigacion Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, Servicio Galego de Saúde, SERGAS, Santiago de Compostela, Spain; University of California San Diego, Moores Cancer Center, La Jolla, CA, USA
| | - Jose Esteban Castelao
- Genetic Oncology Unit, CHUVI Hospital, Complexo Hospitalario Universitario de Vigo, Instituto de Investigación Biomédica Galicia Sur (IISGS), Vigo (Pontevedra), Spain
| | - Paul A Townsend
- Division of Cancer Sciences, Manchester Cancer Research Centre, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Health Innovation Manchester, University of Manchester, Manchester, UK; The University of Surrey, Guildford, Surrey, UK
| | - Dana C Crawford
- Case Western Reserve University, Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Cleveland, OH, USA
| | - Gyorgy Petrovics
- Center for Prostate Disease Research, Uniformed Services University, Bethesda, MD, USA
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Monique J Roobol
- 109 Department of Urology, Erasmus University Medical Center, Cancer Institute, Rotterdam, the Netherlands
| | - Jennifer F Hu
- The University of Miami School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephen K Van Den Eeden
- Division of Research, Kaiser Permanente, Northern California, Oakland, CA, USA; Department of Urology, University of California San Francisco, San Francisco, CA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael B Cook
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - John S Witte
- Department of Epidemiology and Population Health, Department of Biomedical Data Science, Stanford Cancer Institute, Stanford, CA, USA
| | - Rosalind A Eeles
- The Institute of Cancer Research, Sutton, London, UK; Royal Marsden NHS Foundation Trust, London, UK
| | | | - Stephen Watya
- School of Public Health, Makerere University College of Health Sciences, Kampala, Uganda
| | - John M Gaziano
- VA Boston Healthcare System, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Amy C Justice
- VA Connecticut Healthcare System, West Haven, CT, USA; Yale School of Medicine, New Haven, CT, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Farooqi R, Kooner JS, Zhang W. Associations between polygenic risk score and covid-19 susceptibility and severity across ethnic groups: UK Biobank analysis. BMC Med Genomics 2023; 16:150. [PMID: 37386504 PMCID: PMC10311902 DOI: 10.1186/s12920-023-01584-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability. METHODS We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2). RESULTS Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups. CONCLUSIONS Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.
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Affiliation(s)
- Raabia Farooqi
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
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41
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Lu H, Zhang S, Jiang Z, Zeng P. Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations. Brief Bioinform 2023:bbad232. [PMID: 37332016 DOI: 10.1093/bib/bbad232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/06/2023] [Accepted: 06/04/2023] [Indexed: 06/20/2023] Open
Abstract
Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.
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Affiliation(s)
- Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Shuo Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhou Jiang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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Jeon S, Lo YC, Morimoto LM, Metayer C, Ma X, Wiemels JL, de Smith AJ, Chiang CW. Evaluating Genomic Polygenic Risk Scores for Childhood Acute Lymphoblastic Leukemia in Latinos. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.08.23291167. [PMID: 37398036 PMCID: PMC10312899 DOI: 10.1101/2023.06.08.23291167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The utility of polygenic risk score (PRS) models has not been comprehensively evaluated for childhood acute lymphoblastic leukemia (ALL), the most common type of cancer in children. Previous PRS models for ALL were based on significant loci observed in genome-wide association studies (GWAS), even though genomic PRS models have been shown to improve prediction performance for a number of complex diseases. In the United States, Latino (LAT) children have the highest risk of ALL, but the transferability of PRS models to LAT children has not been studied. In this study we constructed and evaluated genomic PRS models based on either non-Latino white (NLW) GWAS or a multi-ancestry GWAS. We found that the best PRS models performed similarly between held-out NLW and LAT samples (PseudoR 2 = 0.086 ± 0.023 in NLW vs. 0.060 ± 0.020 in LAT), and can be improved for LAT if we performed GWAS in LAT-only (PseudoR 2 = 0.116 ± 0.026) or multi-ancestry samples (PseudoR 2 = 0.131 ± 0.025). However, the best genomic models currently do not have better prediction accuracy than a conventional model using all known ALL-associated loci in the literature (PseudoR 2 = 0.166 ± 0.025), which includes loci from GWAS populations that we could not access to train genomic PRS models. Our results suggest that larger and more inclusive GWAS may be needed for genomic PRS to be useful for ALL. Moreover, the comparable performance between populations may suggest a more oligo-genic architecture for ALL, where some large effect loci may be shared between populations. Future PRS models that move away from the infinite causal loci assumption may further improve PRS for ALL.
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Affiliation(s)
- Soyoung Jeon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ying Chu Lo
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Libby M. Morimoto
- Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Catherine Metayer
- Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA
| | - Xiaomei Ma
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Joseph L. Wiemels
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adam J. de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W.K. Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn N, Arias J, Belbin G, Below JE, Berndt S, Chung W, Cimino JJ, Clayton EW, Connolly JJ, Crosslin D, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth R, Ge T, Glessner JT, Gordon A, Guiducci C, Hakonarson H, Harden M, Harr M, Hirschhorn J, Hoggart C, Hsu L, Irvin R, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos R, Luo Y, Malolepsza E, Manolio T, Martin LJ, McCarthy L, Meigs JB, Mersha TB, Mosley J, Namjou B, Pai N, Pesce LL, Peters U, Peterson J, Prows CA, Puckelwartz MJ, Rehm H, Roden D, Rosenthal EA, Rowley R, Sawicki KT, Schaid D, Schmidlen T, Smit R, Smith J, Smoller JW, Thomas M, Tiwari H, Toledo D, Vaitinadin NS, Veenstra D, Walunas T, Wang Z, Wei WQ, Weng C, Wiesner G, Xianyong Y, Kenny E. Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.25.23290535. [PMID: 37333246 PMCID: PMC10275001 DOI: 10.1101/2023.05.25.23290535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Li Hsu
- Fred Hutchinson Cancer Center and University of Washington
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ulrike Peters
- Fred Hutchinson Cancer Center and University of Washington
| | | | | | | | | | - Dan Roden
- Vanderbilt University Medical Center
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Kurniansyah N, Goodman MO, Khan AT, Wang J, Feofanova E, Bis JC, Wiggins KL, Huffman JE, Kelly T, Elfassy T, Guo X, Palmas W, Lin HJ, Hwang SJ, Gao Y, Young K, Kinney GL, Smith JA, Yu B, Liu S, Wassertheil-Smoller S, Manson JE, Zhu X, Chen YDI, Lee IT, Gu CC, Lloyd-Jones DM, Zöllner S, Fornage M, Kooperberg C, Correa A, Psaty BM, Arnett DK, Isasi CR, Rich SS, Kaplan RC, Redline S, Mitchell BD, Franceschini N, Levy D, Rotter JI, Morrison AC, Sofer T. Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups. Nat Commun 2023; 14:3202. [PMID: 37268629 PMCID: PMC10238525 DOI: 10.1038/s41467-023-38990-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.
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Affiliation(s)
- Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Alyna T Khan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jiongming Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Elena Feofanova
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kerri L Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Tali Elfassy
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Walter Palmas
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Henry J Lin
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Shih-Jen Hwang
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Yan Gao
- The Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Kendra Young
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Gregory L Kinney
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Medicine, Brown University, Providence, RI, USA
| | - Sylvia Wassertheil-Smoller
- Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - JoAnn E Manson
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Te Lee
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City, 40705, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - C Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Adolfo Correa
- Departments of Medicine and Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, SC, USA
| | - Carmen R Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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Aw A, Jin LC, Ioannidis N, Song YS. The Impact of Stability Considerations on Genetic Fine-Mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.11.536456. [PMID: 37090514 PMCID: PMC10120703 DOI: 10.1101/2023.04.11.536456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal components (i.e., residualization). Despite its widespread use, however, residualization may not completely remove all sources of confounding. Here, we propose a complementary stability-guided approach that does not rely on residualization, which identifies consistently fine-mapped variants across different genetic backgrounds or environments. We demonstrate the utility of this approach by applying it to fine-map eQTLs in the GEUVADIS data. Using 378 different functional annotations of the human genome, including recent deep learning-based annotations (e.g., Enformer), we compare enrichments of these annotations among variants for which the stability and traditional residualization-based fine-mapping approaches agree against those for which they disagree, and find that the stability approach enhances the power of traditional fine-mapping methods in identifying variants with functional impact. Finally, in cases where the two approaches report distinct variants, our approach identifies variants comparably enriched for functional annotations. Our findings suggest that the stability principle, as a conceptually simple device, complements existing approaches to fine-mapping, reinforcing recent advocacy of evaluating cross-population and cross-environment portability of biological findings. To support visualization and interpretation of our results, we provide a Shiny app, available at: https://alan-aw.shinyapps.io/stability_v0/.
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Affiliation(s)
- Alan Aw
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
| | | | - Nilah Ioannidis
- Center for Computational Biology, University of California, Berkeley
- Computer Science Division, University of California, Berkeley
| | - Yun S. Song
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Computer Science Division, University of California, Berkeley
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Adam Y, Sadeeq S, Kumuthini J, Ajayi O, Wells G, Solomon R, Ogunlana O, Adetiba E, Iweala E, Brors B, Adebiyi E. Polygenic Risk Score in African populations: progress and challenges. F1000Res 2023; 11:175. [PMID: 37273966 PMCID: PMC10233318 DOI: 10.12688/f1000research.76218.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/10/2023] [Indexed: 06/06/2023] Open
Abstract
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Suraju Sadeeq
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Judit Kumuthini
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Olabode Ajayi
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Gordon Wells
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Rotimi Solomon
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Olubanke Ogunlana
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Emmanuel Adetiba
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Electrical & Information Engineering (EIE), Covenant University, Ota, Ogun State, 112212, Nigeria
- HRA, Institute for Systems Science, Durban University of Technology, Durban, South Africa
| | - Emeka Iweala
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Benedikt Brors
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
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Adam Y, Sadeeq S, Kumuthini J, Ajayi O, Wells G, Solomon R, Ogunlana O, Adetiba E, Iweala E, Brors B, Adebiyi E. Polygenic Risk Score in African populations: progress and challenges. F1000Res 2023; 11:175. [PMID: 37273966 PMCID: PMC10233318 DOI: 10.12688/f1000research.76218.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/10/2023] [Indexed: 11/23/2023] Open
Abstract
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Suraju Sadeeq
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Judit Kumuthini
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Olabode Ajayi
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Gordon Wells
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Rotimi Solomon
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Olubanke Ogunlana
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Emmanuel Adetiba
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Electrical & Information Engineering (EIE), Covenant University, Ota, Ogun State, 112212, Nigeria
- HRA, Institute for Systems Science, Durban University of Technology, Durban, South Africa
| | - Emeka Iweala
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Benedikt Brors
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
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Elam KK, Lemery-Chalfant K, Chassin L. A gene-environment cascade theoretical framework of developmental psychopathology. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2023; 132:287-296. [PMID: 36201798 PMCID: PMC10076453 DOI: 10.1037/abn0000546] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Previous theories have emphasized genetic effects "inside the skin" via endophenotypes within the broader developmental psychopathology theory. Expanding on the mechanisms of gene-environment correlation, we propose a new integrative framework emphasizing how genetic effects "outside the skin" (Reiss & Leve, 2007) accumulate due to individual variation in social information processing in negative environments and sociocultural contexts as part of developmental cascades to psychopathology. In this gene-environment cascade theoretical framework, genetic predisposition for psychopathology, as well as stable traits and behaviors, can lead to negative environments via gene-environment correlations that can be exacerbated or buffered by an individual's social information processing. Moreover, these "environments" range from dyadic social relationships to broader sociocultural contexts. Over time, these processes exacerbate one another as part of developmental cascades, resulting in accumulating risk for psychopathology. By focusing on gene-environment correlations and integrating disparate social-emotional, cognitive, and sociocultural research domains, this framework delineates key processes by which early genetic predisposition can contribute to developmentally distinct and accumulating risk for psychopathology over the life course. Implications for intervention and methodological advances that facilitate testing models are presented. This new framework moves the field further away from genetic determinism by informing targets of early psychosocial prevention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Kit K Elam
- Department of Applied Health Science, Indiana University Bloomington
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Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
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Yang G, Mishra M, Perera MA. Multi-Omics Studies in Historically Excluded Populations: The Road to Equity. Clin Pharmacol Ther 2023; 113:541-556. [PMID: 36495075 DOI: 10.1002/cpt.2818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Minoli A Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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