1
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Xu H, Zhang G, Chen J. A novel method for cell deconvolution using DNA methylation in PCA space. BMC Genomics 2024; 25:798. [PMID: 39179972 PMCID: PMC11344294 DOI: 10.1186/s12864-024-10652-0] [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: 12/28/2023] [Accepted: 07/22/2024] [Indexed: 08/26/2024] Open
Abstract
BACKGROUND In this study, we present a novel method for reference-based cell deconvolution using data from DNA methylation arrays. Different from existing methods like IDOL-Ext, which operate on probe-level data, our approach represents features in the principal component analysis (PCA) space for cell type deconvolution. RESULTS Our method's accuracy in estimating cell compositions is validated across various public datasets, including blood samples from glioma patients. It demonstrates precision comparable to IDOL-Ext, with R2 values ranging from 0.73 to 0.99 for most cell types, while offering improved discrimination between similar cell types, particularly T cell subtypes in glioma patient samples (R2 0.42-0.75 vs. 0.36-0.66 for IDOL-Ext). However, both methods showed lower accuracy for certain cell types, such as memory CD8 T cells in glioma patients (R2 0.42 vs. 0.36 for IDOL-Ext), highlighting the challenges in distinguishing closely related cell populations. We have made this method available as an R package "BloodCellDecon" on GitHub. CONCLUSIONS Our study confirms the efficacy of cell type deconvolution in PCA space. The results indicate wide-ranging applicability and potential for adaptation to other forms of genomic data.
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Affiliation(s)
- Huan Xu
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Ge Zhang
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center and March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, OH, USA
| | - Jing Chen
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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2
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Hong SC, Muyas F, Cortés-Ciriano I, Hormoz S. scAI-SNP: a method for inferring ancestry from single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594208. [PMID: 38798590 PMCID: PMC11118306 DOI: 10.1101/2024.05.14.594208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.
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Affiliation(s)
- Sung Chul Hong
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Sahand Hormoz
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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3
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Stoneman HR, Price A, Trout NS, Lamont R, Tifour S, Pozdeyev N, Crooks K, Lin M, Rafaels N, Gignoux CR, Marker KM, Hendricks AE. Characterizing substructure via mixture modeling in large-scale genetic summary statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577805. [PMID: 38766180 PMCID: PMC11100604 DOI: 10.1101/2024.01.29.577805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Genetic summary data are broadly accessible and highly useful including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into groups masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and identifies potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse publicly available summary data resulting in improved and more equitable research.
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Affiliation(s)
- Hayley R Stoneman
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Adelle Price
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
| | - Nikole Scribner Trout
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
| | - Riley Lamont
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
| | - Souha Tifour
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
| | - Nikita Pozdeyev
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Pathology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Meng Lin
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher R Gignoux
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Katie M Marker
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Audrey E Hendricks
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA
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4
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Lee S, Hecker J, Hahn G, Mullin K, Lutz SM, Tanzi RE, Lange C, Prokopenko D. On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries. Alzheimers Dement 2024; 20:3397-3405. [PMID: 38563508 PMCID: PMC11095441 DOI: 10.1002/alz.13796] [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: 11/17/2023] [Revised: 02/14/2024] [Accepted: 02/23/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.
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Affiliation(s)
- Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate school, Dankook University, Yongin-si, Gyeonggi-do, South Korea
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Georg Hahn
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kristina Mullin
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Sharon M Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
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Grinde KE, Browning BL, Reiner AP, Thornton TA, Browning SR. Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587682. [PMID: 38617337 PMCID: PMC11014513 DOI: 10.1101/2024.04.02.587682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/24/2024]
Abstract
Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.
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Affiliation(s)
- Kelsey E. Grinde
- Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota, 55105, USA
| | - Brian L. Browning
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, 98195, USA
| | - Alexander P. Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Timothy A. Thornton
- Regeneron Genetics Center, Tarrytown, New York, 10591, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, Washington, 98195, USA
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6
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Padilla-Iglesias C, Derkx I. Hunter-gatherer genetics research: Importance and avenues. EVOLUTIONARY HUMAN SCIENCES 2024; 6:e15. [PMID: 38516374 PMCID: PMC10955370 DOI: 10.1017/ehs.2024.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/17/2024] [Accepted: 02/02/2024] [Indexed: 03/23/2024] Open
Abstract
Major developments in the field of genetics in the past few decades have revolutionised notions of what it means to be human. Although currently only a few populations around the world practise a hunting and gathering lifestyle, this mode of subsistence has characterised members of our species since its very origins and allowed us to migrate across the planet. Therefore, the geographical distribution of hunter-gatherer populations, dependence on local ecosystems and connections to past populations and neighbouring groups have provided unique insights into our evolutionary origins. However, given the vulnerable status of hunter-gatherers worldwide, the development of the field of anthropological genetics requires that we reevaluate how we conduct research with these communities. Here, we review how the inclusion of hunter-gatherer populations in genetics studies has advanced our understanding of human origins, ancient population migrations and interactions as well as phenotypic adaptations and adaptability to different environments, and the important scientific and medical applications of these advancements. At the same time, we highlight the necessity to address yet unresolved questions and identify areas in which the field may benefit from improvements.
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Affiliation(s)
| | - Inez Derkx
- Department of Evolutionary Anthropology, University of Zurich, Zurich, Switzerland
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7
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [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: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, 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; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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8
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Wu Y, Hao X, Zhu K, Zheng C, Guan F, Zeng P, Wang T. Long-term adverse influence of smoking during pregnancy on height and body size of offspring at ten years old in the UK Biobank cohort. SSM Popul Health 2023; 24:101506. [PMID: 37692834 PMCID: PMC10492214 DOI: 10.1016/j.ssmph.2023.101506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/12/2023] Open
Abstract
Background To explore the long-term relationship between maternal smoking during pregnancy and early childhood growth in the UK Biobank cohort. Methods To estimate the effect of maternal smoking during pregnancy on offspring height and body size at ten years old, we performed binary logistic analyses and reported odds ratios (OR) as well as 95% confidence intervals (95%CIs). We also implemented the cross-contextual comparison study to examine whether such influence could be repeatedly observed among three different ethnicities in the UK Biobank cohort (n = 22,140 for White, n = 7094 for South Asian, and n = 5000 for Black). In particular, we conducted the sibling cohort study in White sibling cohort (n = 9953 for height and n = 7239 for body size) to control for unmeasured familial confounders. Results We discovered that children whose mothers smoked during pregnancy had greater risk of being shorter or plumper at age ten in the full UK Biobank White cohort, with 15.3% (95% CIs: 13.0%∼17.7%) higher risk for height and 32.4% (95%CIs: 29.5%∼35.4%) larger risk for body size. Similar associations were identified in the South Asian and Black ethnicities. These associations were robust and remained significant in the White sibling cohort (12.6% [95%CIs: 5.0%∼20.3%] for height and 36.1% [95%CIs: 26.3%∼45.9%] for body size) after controlling for family factors. Conclusion This study robustly confirms that maternal smoking during pregnancy can promote height deficit and obesity for offspring at ten years old. Our findings strongly encourage mothers to quit smoking during pregnancy for improving growth and development of offspring.
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Affiliation(s)
- Yuxuan Wu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Xingjie Hao
- Department of Biostatistics and Epidemiology, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Kexuan Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Chu Zheng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Fengjun Guan
- Department of Pediatrics, Affiliated Hospital of 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
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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9
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Gichoya JW, Thomas K, Celi LA, Safdar N, Banerjee I, Banja JD, Seyyed-Kalantari L, Trivedi H, Purkayastha S. AI pitfalls and what not to do: mitigating bias in AI. Br J Radiol 2023; 96:20230023. [PMID: 37698583 PMCID: PMC10546443 DOI: 10.1259/bjr.20230023] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 09/13/2023] Open
Abstract
Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications increases, we are learning the failures of these models and how they can perpetuate bias. With these new lessons, we need to prioritize bias evaluation and mitigation for radiology applications; all the while not ignoring the impact of changes in the larger enterprise AI deployment which may have downstream impact on performance of AI models. In this paper, we provide an updated review of known pitfalls causing AI bias and discuss strategies for mitigating these biases within the context of AI deployment in the larger healthcare enterprise. We describe these pitfalls by framing them in the larger AI lifecycle from problem definition, data set selection and curation, model training and deployment emphasizing that bias exists across a spectrum and is a sequela of a combination of both human and machine factors.
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Affiliation(s)
| | - Kaesha Thomas
- Department of Radiology, Emory University, Atlanta, United States
| | | | - Nabile Safdar
- Department of Radiology, Emory University, Atlanta, United States
| | - Imon Banerjee
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, United States
| | - John D Banja
- Emory University Center for Ethics, Emory University, Atlanta, United States
| | - Laleh Seyyed-Kalantari
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, North York, United States
| | - Hari Trivedi
- Department of Radiology, Emory University, Atlanta, United States
| | - Saptarshi Purkayastha
- School of Informatics and Computing, Indiana University Purdue University, Indianapolis, United States
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10
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Mantes AD, Montserrat DM, Bustamante CD, Giró-i-Nieto X, Ioannidis AG. Neural ADMIXTURE for rapid genomic clustering. NATURE COMPUTATIONAL SCIENCE 2023; 3:621-629. [PMID: 37600116 PMCID: PMC10438426 DOI: 10.1038/s43588-023-00482-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/06/2023] [Indexed: 08/22/2023]
Abstract
Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by calculating multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.
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Affiliation(s)
- Albert Dominguez Mantes
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, United States
- Signal Theory and Communications Department, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland
| | - Daniel Mas Montserrat
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, United States
| | | | - Xavier Giró-i-Nieto
- Signal Theory and Communications Department, Universitat Politècnica de Catalunya, Barcelona, Catalonia, Spain
| | - Alexander G. Ioannidis
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, United States
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, United States
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11
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Grandi E, Navedo MF, Saucerman JJ, Bers DM, Chiamvimonvat N, Dixon RE, Dobrev D, Gomez AM, Harraz OF, Hegyi B, Jones DK, Krogh-Madsen T, Murfee WL, Nystoriak MA, Posnack NG, Ripplinger CM, Veeraraghavan R, Weinberg S. Diversity of cells and signals in the cardiovascular system. J Physiol 2023; 601:2547-2592. [PMID: 36744541 PMCID: PMC10313794 DOI: 10.1113/jp284011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/19/2023] [Indexed: 02/07/2023] Open
Abstract
This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.
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Affiliation(s)
- Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Manuel F. Navedo
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Donald M. Bers
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Nipavan Chiamvimonvat
- Department of Pharmacology, University of California Davis, Davis, CA, USA
- Department of Internal Medicine, University of California Davis, Davis, CA, USA
| | - Rose E. Dixon
- Department of Physiology and Membrane Biology, University of California Davis, Davis, CA, USA
| | - Dobromir Dobrev
- Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montréal, Canada
- Department of Molecular Physiology & Biophysics, Baylor College of Medicine, Houston, TX, USA
| | - Ana M. Gomez
- Signaling and Cardiovascular Pathophysiology-UMR-S 1180, INSERM, Université Paris-Saclay, Orsay, France
| | - Osama F. Harraz
- Department of Pharmacology, Larner College of Medicine, and Vermont Center for Cardiovascular and Brain Health, University of Vermont, Burlington, VT, USA
| | - Bence Hegyi
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - David K. Jones
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Trine Krogh-Madsen
- Department of Physiology & Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Walter Lee Murfee
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Matthew A. Nystoriak
- Department of Medicine, Division of Environmental Medicine, Center for Cardiometabolic Science, University of Louisville, Louisville, KY, 40202, USA
| | - Nikki G. Posnack
- Department of Pediatrics, Department of Pharmacology and Physiology, The George Washington University, Washington, DC, USA
- Sheikh Zayed Institute for Pediatric and Surgical Innovation, Children’s National Heart Institute, Children’s National Hospital, Washington, DC, USA
| | | | - Rengasayee Veeraraghavan
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
- Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University – Wexner Medical Center, Columbus, OH, USA
| | - Seth Weinberg
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
- Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University – Wexner Medical Center, Columbus, OH, USA
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12
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Jacobs BM, Schalk L, Dunne A, Scalfari A, Nandoskar A, Gran B, Mein CA, Sellers C, Spilker C, Rog D, Visentin E, Bezzina EL, Uzochukwu E, Tallantyre E, Wozniak E, Sacre E, Hassan-Smith G, Ford HL, Harris J, Bradley J, Breedon J, Brooke J, Kreft KL, Tuite Dalton K, George K, Papachatzaki M, O'Malley M, Peter M, Mattoscio M, Rhule N, Evangelou N, Vinod N, Quinn O, Shamji R, Kaimal R, Boulton R, Tanveer R, Middleton R, Murray R, Bellfield R, Hoque S, Patel S, Raj S, Gumus S, Mitchell S, Sawcer S, Arun T, Pogreban T, Brown TL, Begum T, Antoine V, Rashid W, Noyce AJ, Silber E, Morris H, Giovannoni G, Dobson R. ADAMS project: a genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis based in the UK. BMJ Open 2023; 13:e071656. [PMID: 37197821 DOI: 10.1136/bmjopen-2023-071656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/19/2023] Open
Abstract
PURPOSE Genetic studies of multiple sclerosis (MS) susceptibility and severity have focused on populations of European ancestry. Studying MS genetics in other ancestral groups is necessary to determine the generalisability of these findings. The genetic Association study in individuals from Diverse Ancestral backgrounds with Multiple Sclerosis (ADAMS) project aims to gather genetic and phenotypic data on a large cohort of ancestrally-diverse individuals with MS living in the UK. PARTICIPANTS Adults with self-reported MS from diverse ancestral backgrounds. Recruitment is via clinical sites, online (https://app.mantal.co.uk/adams) or the UK MS Register. We are collecting demographic and phenotypic data using a baseline questionnaire and subsequent healthcare record linkage. We are collecting DNA from participants using saliva kits (Oragene-600) and genotyping using the Illumina Global Screening Array V.3. FINDINGS TO DATE As of 3 January 2023, we have recruited 682 participants (n=446 online, n=55 via sites, n=181 via the UK MS Register). Of this initial cohort, 71.2% of participants are female, with a median age of 44.9 years at recruitment. Over 60% of the cohort are non-white British, with 23.5% identifying as Asian or Asian British, 16.2% as Black, African, Caribbean or Black British and 20.9% identifying as having mixed or other backgrounds. The median age at first symptom is 28 years, and median age at diagnosis is 32 years. 76.8% have relapsing-remitting MS, and 13.5% have secondary progressive MS. FUTURE PLANS Recruitment will continue over the next 10 years. Genotyping and genetic data quality control are ongoing. Within the next 3 years, we aim to perform initial genetic analyses of susceptibility and severity with a view to replicating the findings from European-ancestry studies. In the long term, genetic data will be combined with other datasets to further cross-ancestry genetic discoveries.
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Affiliation(s)
- Benjamin M Jacobs
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Luisa Schalk
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Angie Dunne
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | - Antonio Scalfari
- Centre of Neuroscience, Department of Medicine, Imperial College London, London, UK
| | | | - Bruno Gran
- Department of Neurology, Nottingham University Hospitals NHS Trust, Mental Health and Clinical Neuroscience Academic Unit, University of Nottingham School of Medicine, Nottingham, UK
| | - Charles A Mein
- Barts and the London Genome Centre, Queen Mary University of London, London, UK
| | - Charlotte Sellers
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Cord Spilker
- Bradford Teaching Hospital Foundation Trust, Bradford, UK
| | - David Rog
- Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Trust, Manchester, UK
| | - Elisa Visentin
- Research and Innovation, Queen's Hospital, BHRUT, London, UK
| | | | - Emeka Uzochukwu
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Emma Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Department of Clinical Neurology, University Hospital of Wales, Cardiff, UK
| | - Eva Wozniak
- Barts and the London Genome Centre, Queen Mary University of London, London, UK
| | - Eve Sacre
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | | | - Helen L Ford
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | - Jade Harris
- Northern Care Alliance NHS Trust, Manchester, UK
| | | | - Joshua Breedon
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Karim L Kreft
- Department of Neurology, University Hospital of Wales, Cardiff, UK
| | | | - Katila George
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Martin O'Malley
- Leeds Centre for Neurosciences, Leeds teaching Hospitals NHS Trust, Leeds, UK
| | - Michelle Peter
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Miriam Mattoscio
- Department of neuroscience, Queen's Hospital, BHRUT NHS Trust, Romford, UK
| | - Neisha Rhule
- Queen Elizabeth Hospital (Lewisham and Greenwich NHS Trust), London, UK
| | - Nikos Evangelou
- Department of Neurology, Nottingham University Hospitals NHS Trust; Mental Health and Clinical Neuroscience Academic Unit, University of Nottingham School of Medicine, Nottingham, UK
| | | | - Outi Quinn
- Bradford Teaching Hospital Foundation Trust, Bradford, UK
| | - Ramya Shamji
- Research and Innovation, Queen's Hospital, BHRUT, London, UK
| | - Rashmi Kaimal
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Rebecca Boulton
- Department of Neurology, Nottingham University Hospitals NHS Trust; Mental Health and Clinical Neuroscience Academic Unit, University of Nottingham School of Medicine, Nottingham, UK
| | - Riffat Tanveer
- Lancashire Teaching Hospital NHS Foundation Trust, Preston, UK
| | - Rod Middleton
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Roxanne Murray
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Ruth Bellfield
- Bradford Teaching Hospital Foundation Trust, Bradford, UK
| | - Sadid Hoque
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Shakeelah Patel
- Lancashire Teaching Hospital NHS Foundation Trust, Preston, UK
| | - Sonia Raj
- Lancashire Teaching Hospital NHS Foundation Trust, Preston, UK
| | - Stephanie Gumus
- Mid and South Essex NHS Foundation Trust, Southend-on-Sea, UK
| | | | - Stephen Sawcer
- University of Cambridge, Department of Clinical Neuroscience, Addenbrookes Hospital, Hills Road, Cambridge, UK
| | - Tarunya Arun
- University Hospitals of Coventry and Warwickshire, Coventry, UK
| | | | - Terri-Louise Brown
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Thamanna Begum
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Waqar Rashid
- St George's University Hospitals NHS Foundation Trust, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Eli Silber
- Kings College Hospital and Lewisham and Greenwich NHS Trusts, London, UK
| | - Huw Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK
| | - Gavin Giovannoni
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
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13
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Levin MG, Tsao NL, Singhal P, Liu C, Vy HMT, Paranjpe I, Backman JD, Bellomo TR, Bone WP, Biddinger KJ, Hui Q, Dikilitas O, Satterfield BA, Yang Y, Morley MP, Bradford Y, Burke M, Reza N, Charest B, Judy RL, Puckelwartz MJ, Hakonarson H, Khan A, Kottyan LC, Kullo I, Luo Y, McNally EM, Rasmussen-Torvik LJ, Day SM, Do R, Phillips LS, Ellinor PT, Nadkarni GN, Ritchie MD, Arany Z, Cappola TP, Margulies KB, Aragam KG, Haggerty CM, Joseph J, Sun YV, Voight BF, Damrauer SM. Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure. Nat Commun 2022; 13:6914. [PMID: 36376295 PMCID: PMC9663424 DOI: 10.1038/s41467-022-34216-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/17/2022] [Indexed: 11/16/2022] Open
Abstract
Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.
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Affiliation(s)
- Michael G Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Noah L Tsao
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Pankhuri Singhal
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Chang Liu
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ha My T Vy
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ishan Paranjpe
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Tiffany R Bellomo
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - William P Bone
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kiran J Biddinger
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Qin Hui
- Emory University School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Ozan Dikilitas
- Departments of Internal Medicine and Cardiovascular Medicine, and Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, MN, USA
| | | | - Yifan Yang
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael P Morley
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Megan Burke
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nosheen Reza
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Renae L Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Megan J Puckelwartz
- Department of Pharmacology, Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Leah C Kottyan
- Department of Pediatrics, Division of Human Genetics and Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Iftikhar Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elizabeth M McNally
- Center for Genetic Medicine, Bluhm Cardiovascular Institute, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sharlene M Day
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, BioMe Phenomics Center, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, Decatur, GA, USA
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zoltan Arany
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas P Cappola
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kenneth B Margulies
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Krishna G Aragam
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics and Heart Institute, Geisinger, Danville, PA, USA
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yan V Sun
- Emory University School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Benjamin F Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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14
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Privé F, Arbel J, Aschard H, Vilhjálmsson BJ. Identifying and correcting for misspecifications in GWAS summary statistics and polygenic scores. HGG ADVANCES 2022; 3:100136. [PMID: 36105883 PMCID: PMC9465343 DOI: 10.1016/j.xhgg.2022.100136] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/11/2022] [Indexed: 11/18/2022] Open
Abstract
Publicly available genome-wide association studies (GWAS) summary statistics exhibit uneven quality, which can impact the validity of follow-up analyses. First, we present an overview of possible misspecifications that come with GWAS summary statistics. Then, in both simulations and real-data analyses, we show that additional information such as imputation INFO scores, allele frequencies, and per-variant sample sizes in GWAS summary statistics can be used to detect possible issues and correct for misspecifications in the GWAS summary statistics. One important motivation for us is to improve the predictive performance of polygenic scores built from these summary statistics. Unfortunately, owing to the lack of reporting standards for GWAS summary statistics, this additional information is not systematically reported. We also show that using well-matched linkage disequilibrium (LD) references can improve model fit and translate into more accurate prediction. Finally, we discuss how to make polygenic score methods such as lassosum and LDpred2 more robust to these misspecifications to improve their predictive power.
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Affiliation(s)
- Florian Privé
- National Centre for Register-Based Research, Aarhus University, 8210 Aarhus, Denmark
| | - Julyan Arbel
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, 75015 Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Bjarni J. Vilhjálmsson
- National Centre for Register-Based Research, Aarhus University, 8210 Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark
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