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Wu Y, Zheng Z, Thibaut L, Goddard ME, Wray NR, Visscher PM, Zeng J. Genome-wide fine-mapping improves identification of causal variants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.18.24310667. [PMID: 39072021 PMCID: PMC11275676 DOI: 10.1101/2024.07.18.24310667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [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: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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Yap CF, Morris AP. Methods for multiancestry genome-wide association study meta-analysis. Ann Hum Genet 2024. [PMID: 39022911 DOI: 10.1111/ahg.12572] [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: 05/30/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/20/2024]
Abstract
Genome-wide association studies (GWAS) have significantly enhanced our understanding of the genetic basis of complex diseases. Despite the technological advancements, gaps in our understanding remain, partly due to small effect sizes and inadequate coverage of genetic variation. Multiancestry GWAS meta-analysis (MAGMA) addresses these challenges by integrating genetic data from diverse populations, thereby increasing power to detect loci and improving fine-mapping resolution to identify causal variants across different ancestry groups. This review provides an overview of the protocols, statistical methods, and software of MAGMA, as well as highlighting some challenges associated with this approach.
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Affiliation(s)
- Chuan Fu Yap
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
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Matthews LJ, Zhang Z, Martschenko DO. Schoolhouse risk: Can we mitigate the polygenic Pygmalion effect? Acta Psychol (Amst) 2024; 248:104403. [PMID: 39003994 DOI: 10.1016/j.actpsy.2024.104403] [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: 12/15/2023] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND Although limited in predictive accuracy, polygenic scores (PGS) for educational outcomes are currently available to the public via direct-to-consumer genetic testing companies. Further, there is a growing movement to apply PGS in educational settings via 'precision education.' Prior scholarship highlights the potentially negative impacts of such applications, as disappointing results may give rise a "polygenic Pygmalion effect." In this paper two studies were conducted to identify factors that may mitigate or exacerbate negative impacts of PGS. METHODS Two studies were conducted. In each, 1188 students were randomized to one of four conditions: Low-percentile polygenic score for educational attainment (EA-PGS), Low EA-PGS + Mitigating information, Low EA-PGS + Exacerbating information, or Control. Regression analyses were used to examine differences between conditions. RESULTS In Study 1, participants randomized to Control reported significantly higher on the Rosenberg Self-Esteem Scale (RSES), Competence Scale (CS), Academic Efficacy Scale (AES) and Educational Potential Scale (EPS). CS was significantly higher in the Low EA-PGS + Mitigating information condition. CS and AES were significantly lower in the Low EA-PGS + Exacerbating information condition compared to the Low EA-PGS + Mitigating information condition. In Study 2, participants randomized to Control reported significantly higher CS and AES. Pairwise comparisons did not show significant differences in CS and AES. Follow-up pairwise comparisons using Tukey P-value correction did not find significant associations between non-control conditions. CONCLUSION These studies replicated the polygenic Pygmalion effect yet were insufficiently powered to detect significant effects of mitigating contextual information. Regardless of contextual information, disappointing EA-PGS results were significantly associated with lower assessments of self-esteem, competence, academic efficacy, and educational potential.
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Affiliation(s)
- Lucas J Matthews
- Columbia University, Department of Medical Humanities & Ethics, New York, NY, United States; The Hastings Center, New York, NY, United States.
| | - Zhijun Zhang
- New York State Psychiatric Institute, Department of Mental Health and Data Science, New York, NY, United States.
| | - Daphne O Martschenko
- Stanford Center for Biomedical Ethics and Department of Pediatrics, Stanford University; Stanford, CA, United States.
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Caria CA, Faà V, Porcu S, Marongiu MF, Poddie D, Perseu L, Meloni A, Vaccargiu S, Ristaldi MS. Post-GWAS Validation of Target Genes Associated with HbF and HbA 2 Levels. Cells 2024; 13:1185. [PMID: 39056767 PMCID: PMC11274989 DOI: 10.3390/cells13141185] [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: 05/21/2024] [Revised: 07/05/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Genome-Wide Association Studies (GWASs) have identified a huge number of variants associated with different traits. However, their validation through in vitro and in vivo studies often lags well behind their identification. For variants associated with traits or diseases of biomedical interest, this gap delays the development of possible therapies. This issue also impacts beta-hemoglobinopathies, such as beta-thalassemia and sickle cell disease (SCD). The definitive cures for these diseases are currently bone marrow transplantation and gene therapy. However, limitations regarding their effective use restrict their worldwide application. Great efforts have been made to identify whether modulators of fetal hemoglobin (HbF) and, to a lesser extent, hemoglobin A2 (HbA2) are possible therapeutic targets. Herein, we performed the post-GWAS in vivo validation of two genes, cyclin D3 (CCND3) and nuclear factor I X (NFIX), previously associated with HbF and HbA2 levels. The absence of Ccnd3 expression in vivo significantly increased g (HbF) and d (HbA2) globin gene expression. Our data suggest that CCND3 is a possible therapeutic target in sickle cell disease. We also confirmed the association of Nfix with γ-globin gene expression and present data suggesting a possible role for Nfix in regulating Kruppel-like transcription factor 1 (Klf1), a master regulator of hemoglobin switching. This study contributes to filling the gap between GWAS variant identification and target validation for beta-hemoglobinopathies.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Maria Serafina Ristaldi
- Istituto di Ricerca Genetica e Biomedica, Cittadella Universitaria di Monserrato, SS 554, Bivio Sestu Km 4,500, 09042 Cagliari, Italy; (C.A.C.); (V.F.); (S.P.); (M.F.M.); (D.P.); (L.P.); (A.M.); (S.V.)
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Mayerhofer L, Nes RB, Yu B, Ayorech Z, Lan X, Ystrom E, Røysamb E. Stability and change in maternal wellbeing and illbeing from pregnancy to three years postpartum. Qual Life Res 2024:10.1007/s11136-024-03730-z. [PMID: 38992240 DOI: 10.1007/s11136-024-03730-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/27/2024] [Indexed: 07/13/2024]
Abstract
PURPOSE Motherhood affects women's mental health, encompassing aspects of both wellbeing and illbeing. This study investigated stability and change in wellbeing (i.e., relationship satisfaction and positive affect) and illbeing (i.e., depressive and anxiety symptoms) from pregnancy to three years postpartum. We further investigated the mutual and dynamic relations between these constructs over time and the role of genetic propensities in their time-invariant stability. DATA AND METHODS This four-wave longitudinal study included 83,124 women from the Norwegian Mother, Father, and Child Cohort Study (MoBa) linked to the Medical Birth Registry of Norway. Data were collected during pregnancy (30 weeks) and at 6, 18 and 36 months postpartum. Wellbeing and illbeing were based on the Relationship Satisfaction Scale, the Differential Emotions Scale and Hopkins Symptoms Checklist-8. Genetics were measured by the wellbeing spectrum polygenic index. Analyses were based on random intercept cross-lagged panel models using R. RESULTS All four outcomes showed high stability and were mutually interconnected over time, with abundant cross-lagged predictions. The period of greatest instability was from pregnancy to 6 months postpartum, followed by increasing stability. Prenatal relationship satisfaction played a crucial role in maternal mental health postpartum. Women's genetic propensity to wellbeing contributed to time-invariant stability of all four constructs. CONCLUSION Understanding the mutual relationship between different aspects of wellbeing and illbeing allows for identifying potential targets for health promotion interventions. Time-invariant stability was partially explained by genetics. Maternal wellbeing and illbeing develop in an interdependent way from pregnancy to 36 months postpartum.
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Affiliation(s)
| | - Ragnhild Bang Nes
- PROMENTA Research Center, Oslo University, Oslo, Norway
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Philosophy, Classics, and History of Arts and Ideas, University of Oslo, Oslo, Norway
| | - Baeksan Yu
- Gwangju National University of Education, Gwangju, South Korea
| | - Ziada Ayorech
- PROMENTA Research Center, Oslo University, Oslo, Norway
| | - Xiaoyu Lan
- PROMENTA Research Center, Oslo University, Oslo, Norway
| | - Eivind Ystrom
- PROMENTA Research Center, Oslo University, Oslo, Norway
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Espen Røysamb
- PROMENTA Research Center, Oslo University, Oslo, Norway
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
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Waldron C, Zafar MA, Ma D, Zhang H, Dykas D, Ziganshin BA, Popa A, Jha A, Kwan JM, Elefteriades JA. Somatic Variants Acquired Later in Life Associated with Thoracic Aortic Aneurysms: JAK2 V617F. Genes (Basel) 2024; 15:883. [PMID: 39062663 PMCID: PMC11276600 DOI: 10.3390/genes15070883] [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: 05/16/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
The JAK2 V617F somatic variant is a well-known driver of myeloproliferative neoplasms (MPN) associated with an increased risk for athero-thrombotic cardiovascular disease. Recent studies have demonstrated its role in the development of thoracic aortic aneurysm (TAA). However, limited clinical information and level of JAK2 V617F burden have been provided for a comprehensive evaluation of potential confounders. A retrospective genotype-first study was conducted to identify carriers of the JAK2 V617F variant from an internal exome sequencing database in Yale DNA Diagnostics Lab. Additionally, the overall incidence of somatic variants in the JAK2 gene across various tissue types in the healthy population was carried out based on reanalysis of SomaMutDB and data from the UK Biobank (UKBB) cohort to compare our dataset to the population prevalence of the variant. In our database of 12,439 exomes, 594 (4.8%) were found to have a thoracic aortic aneurysm (TAA), and 12 (0.049%) were found to have a JAK2 V617F variant. Among the 12 JAK2 V617F variant carriers, five had a TAA (42%), among whom four had an ascending TAA and one had a descending TAA, with a variant allele fraction ranging from 11.2% to 20%. Among these five patients, 60% were female, and average age at diagnosis was 70 (49-79). The mean ascending aneurysm size was 5.05 cm (range 4.6-5.5 cm), and four patients had undergone surgical aortic replacement or repair. UKBB data revealed a positive correlation between the JAK2 V617F somatic variant and aortic valve disease (effect size 0.0086, p = 0.85) and TAA (effect size = 0.004, p = 0.92), although not statistically significant. An unexpectedly high prevalence of TAA in our dataset (5/594, 0.84%) is greater than the prevalence reported before for the general population, supporting its association with TAA. JAK2 V617F may contribute a meaningful proportion of otherwise unexplained aneurysm patients. Additionally, it may imply a potential JAK2-specific disease mechanism in the developmental of TAA, which suggests a possible target of therapy that warrants further investigation.
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Affiliation(s)
- Christina Waldron
- Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT 06510, USA; (C.W.); (M.A.Z.); (B.A.Z.)
| | - Mohammad A. Zafar
- Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT 06510, USA; (C.W.); (M.A.Z.); (B.A.Z.)
| | - Deqiong Ma
- DNA Diagnostics Lab, Yale University School of Medicine, New Haven, CT 06510, USA; (D.M.); (H.Z.); (D.D.)
| | - Hui Zhang
- DNA Diagnostics Lab, Yale University School of Medicine, New Haven, CT 06510, USA; (D.M.); (H.Z.); (D.D.)
| | - Daniel Dykas
- DNA Diagnostics Lab, Yale University School of Medicine, New Haven, CT 06510, USA; (D.M.); (H.Z.); (D.D.)
| | - Bulat A. Ziganshin
- Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT 06510, USA; (C.W.); (M.A.Z.); (B.A.Z.)
| | - Andreea Popa
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
| | - Alokkumar Jha
- Centre for Neurogenetics, Weill Cornell Medicine, New York, NY 10021, USA;
| | - Jennifer M. Kwan
- Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, CT 06510, USA;
| | - John A. Elefteriades
- Aortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT 06510, USA; (C.W.); (M.A.Z.); (B.A.Z.)
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8
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Gu S, Huang Q, Jie Y, Sun C, Wen C, Yang N. Transcriptomic and epigenomic landscapes of muscle growth during the postnatal period of broilers. J Anim Sci Biotechnol 2024; 15:91. [PMID: 38961455 PMCID: PMC11223452 DOI: 10.1186/s40104-024-01049-w] [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: 02/04/2024] [Accepted: 05/12/2024] [Indexed: 07/05/2024] Open
Abstract
BACKGROUND Broilers stand out as one of the fastest-growing livestock globally, making a substantial contribution to animal meat production. However, the molecular and epigenetic mechanisms underlying the rapid growth and development of broiler chickens are still unclear. This study aims to explore muscle development patterns and regulatory networks during the postnatal rapid growth phase of fast-growing broilers. We measured the growth performance of Cornish (CC) and White Plymouth Rock (RR) over a 42-d period. Pectoral muscle samples from both CC and RR were randomly collected at day 21 after hatching (D21) and D42 for RNA-seq and ATAC-seq library construction. RESULTS The consistent increase in body weight and pectoral muscle weight across both breeds was observed as they matured, with CC outpacing RR in terms of weight at each stage of development. Differential expression analysis identified 398 and 1,129 genes in the two dimensions of breeds and ages, respectively. A total of 75,149 ATAC-seq peaks were annotated in promoter, exon, intron and intergenic regions, with a higher number of peaks in the promoter and intronic regions. The age-biased genes and breed-biased genes of RNA-seq were combined with the ATAC-seq data for subsequent analysis. The results spotlighted the upregulation of ACTC1 and FDPS at D21, which were primarily associated with muscle structure development by gene cluster enrichment. Additionally, a noteworthy upregulation of MUSTN1, FOS and TGFB3 was spotted in broiler chickens at D42, which were involved in cell differentiation and muscle regeneration after injury, suggesting a regulatory role of muscle growth and repair. CONCLUSIONS This work provided a regulatory network of postnatal broiler chickens and revealed ACTC1 and MUSTN1 as the key responsible for muscle development and regeneration. Our findings highlight that rapid growth in broiler chickens triggers ongoing muscle damage and subsequent regeneration. These findings provide a foundation for future research to investigate the functional aspects of muscle development.
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Affiliation(s)
- Shuang Gu
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Qiang Huang
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Yuchen Jie
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
| | - Congjiao Sun
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
- Sanya Institute of China Agricultural University, Hainan, 572025, China
| | - Chaoliang Wen
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China
- Sanya Institute of China Agricultural University, Hainan, 572025, China
| | - Ning Yang
- State Key Laboratory of Animal Biotech Breeding and Frontier Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China.
- National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Department of Animal Genetics and Breeding, College of Animal Science and Technology China Agricultural University, Beijing, 100193, China.
- Sanya Institute of China Agricultural University, Hainan, 572025, China.
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Chen BD, Lee C, Tapia AL, Reiner AP, Tang H, Kooperberg C, Manson JE, Li Y, Raffield LM. Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study. Genet Epidemiol 2024. [PMID: 38940271 DOI: 10.1002/gepi.22578] [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: 09/11/2023] [Revised: 05/26/2024] [Accepted: 06/13/2024] [Indexed: 06/29/2024]
Abstract
In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.
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Affiliation(s)
- Brian D Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chanhwa Lee
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Amanda L Tapia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Fu G, Yu S, Wu K, Yang M, Altaf MA, Wu Z, Deng Q, Lu X, Fu H, Wang Z, Cheng S. Genome-wide association study and candidate gene identification for agronomic traits in 182 upward-growing fruits of C. frutescens and C. annuum. Sci Rep 2024; 14:14691. [PMID: 38926509 PMCID: PMC11208541 DOI: 10.1038/s41598-024-65332-6] [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: 02/20/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
Pepper agronomic traits serve as pivotal indicators for characterizing germplasm attributes and correlations. It is important to study differential genotypic variation through phenotypic differences of target traits. Whole genome resequencing was used to sequence the whole genome among different individuals of species with known reference genomes and annotations, and based on this, differential analyses of individuals or populations were carried out to identify SNPs for agronomic traits related to pepper. This study conducted a genome-wide association study encompassing 26 key agronomic traits in 182 upward-growing fruits of C. frutescens and C. annuum. The population structure (phylogenetics, population structure, population principal component analysis, genetic relationship) and linkage disequilibrium analysis were realized to ensure the accuracy and reliability of GWAS results, and the optimal statistical model was determined. A total of 929 SNPs significantly associated with 26 agronomic traits, were identified, alongside the detection of 519 candidate genes within 100 kb region adjacent to these SNPs. Additionally, through gene annotation and expression pattern scrutiny, genes such as GAUT1, COP10, and DDB1 correlated with fruit traits in Capsicum frutescens and Capsicum annuum were validated via qRT-PCR. In the CH20 (Capsicum annuum) and YB-4 (Capsicum frutescens) cultivars, GAUT1 and COP10 were cloned with cDNA lengths of 1065 bp and 561 bp, respectively, exhibiting only a small number of single nucleotide variations and nucleotide deletions. This validation provides a robust reference for molecular marker-assisted breeding of pepper agronomic traits, offering both genetic resources and theoretical foundations for future endeavors in molecular marker-assisted breeding for pepper.
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Affiliation(s)
- Genying Fu
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Shuang Yu
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Kun Wu
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Mengxian Yang
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Muhammad Ahsan Altaf
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
| | - Zhuo Wu
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Qin Deng
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Xu Lu
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
| | - Huizhen Fu
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
| | - Zhiwei Wang
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China
| | - Shanhan Cheng
- Key Laboratory for Quality Regulation of Horticultural Crops of Hainan Province, School of Breeding and Multiplication (Sanya Institute of Breeding and Multiplication), Hainan University, Sanya, 572025, China.
- Key Laboratory for Quality Regulation of Tropical Horticultural Crops of Hainan Province, School of Tropical Agriculture and Forestry, Hainan University, Haikou, 570228, China.
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11
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Wang J, Gu R, Kong X, Luan S, Luo YLL. Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110986. [PMID: 38430953 DOI: 10.1016/j.pnpbp.2024.110986] [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] [Received: 09/26/2023] [Revised: 01/30/2024] [Accepted: 02/28/2024] [Indexed: 03/05/2024]
Abstract
Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.
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Affiliation(s)
- Jiaqi Wang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Ruolei Gu
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Department of Psychiatry of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchundong Road, Hangzhou 310016, China
| | - Shenghua Luan
- Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China
| | - Yu L L Luo
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, 16 Lincui Road, Beijing 100101, China.
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12
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Esquivel Gaytan A, Bomer N, Grote Beverborg N, van der Meer P. 404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research. Eur J Heart Fail 2024; 26:1313-1323. [PMID: 38741225 DOI: 10.1002/ejhf.3268] [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] [Received: 11/23/2023] [Revised: 03/15/2024] [Accepted: 04/14/2024] [Indexed: 05/16/2024] Open
Abstract
The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.
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Affiliation(s)
- Antonio Esquivel Gaytan
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Nils Bomer
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Niels Grote Beverborg
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter van der Meer
- Department of Cardiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
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13
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Palti Y, Vallejo RL, Purcell MK, Gao G, Shewbridge KL, Long RL, Setzke C, Fragomeni BO, Cheng H, Martin KE, Naish KA. Genome-wide association analysis of the resistance to infectious hematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate effect quantitative trait loci. Front Genet 2024; 15:1394656. [PMID: 38854430 PMCID: PMC11162110 DOI: 10.3389/fgene.2024.1394656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/30/2024] [Indexed: 06/11/2024] Open
Abstract
Infectious hematopoietic necrosis (IHN) is a disease of salmonid fish that is caused by the IHN virus (IHNV), which can cause substantial mortality and economic losses in rainbow trout aquaculture and fisheries enhancement hatchery programs. In a previous study on a commercial rainbow trout breeding line that has undergone selection, we found that genetic resistance to IHNV is controlled by the oligogenic inheritance of several moderate and many small effect quantitative trait loci (QTL). Here we used genome wide association analyses in two different commercial aquaculture lines that were naïve to previous exposure to IHNV to determine whether QTL were shared across lines, and to investigate whether there were major effect loci that were still segregating in the naïve lines. A total of 1,859 and 1,768 offspring from two commercial aquaculture strains were phenotyped for resistance to IHNV and genotyped with the rainbow trout Axiom 57K SNP array. Moderate heritability values (0.15-0.25) were estimated. Two statistical methods were used for genome wide association analyses in the two populations. No major QTL were detected despite the naïve status of the two lines. Further, our analyses confirmed an oligogenic architecture for genetic resistance to IHNV in rainbow trout. Overall, 17 QTL with notable effect (≥1.9% of the additive genetic variance) were detected in at least one of the two rainbow trout lines with at least one of the two statistical methods. Five of those QTL were mapped to overlapping or adjacent chromosomal regions in both lines, suggesting that some loci may be shared across commercial lines. Although some of the loci detected in this GWAS merit further investigation to better understand the biological basis of IHNV disease resistance across populations, the overall genetic architecture of IHNV resistance in the two rainbow trout lines suggests that genomic selection may be a more effective strategy for genetic improvement in this trait.
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Affiliation(s)
- Yniv Palti
- National Center for Cool and Cold Water Aquaculture, USDA-ARS, Kearneysville, WV, United States
| | - Roger L. Vallejo
- National Center for Cool and Cold Water Aquaculture, USDA-ARS, Kearneysville, WV, United States
| | - Maureen K. Purcell
- US Geological Survey, Western Fisheries Research Center, Seattle, WA, United States
| | - Guangtu Gao
- National Center for Cool and Cold Water Aquaculture, USDA-ARS, Kearneysville, WV, United States
| | - Kristy L. Shewbridge
- National Center for Cool and Cold Water Aquaculture, USDA-ARS, Kearneysville, WV, United States
| | - Roseanna L. Long
- National Center for Cool and Cold Water Aquaculture, USDA-ARS, Kearneysville, WV, United States
| | - Christopher Setzke
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, United States
| | - Breno O. Fragomeni
- Department of Animal Science, University of Connecticut, Storrs, CT, United States
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, United States
| | | | - Kerry A. Naish
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, United States
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Kim SB, Kang JH, Cheon M, Kim DJ, Lee BC. Stacked neural network for predicting polygenic risk score. Sci Rep 2024; 14:11632. [PMID: 38773257 PMCID: PMC11109142 DOI: 10.1038/s41598-024-62513-1] [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: 06/17/2023] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies.
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Affiliation(s)
- Sun Bin Kim
- Genoplan Korea Inc., Seoul, Republic of Korea
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15
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Mukhopadhyay S, Dixit P, Khanom N, Sanghera G, McGurk KA. The Genetic Factors Influencing Cardiomyopathies and Heart Failure across the Allele Frequency Spectrum. J Cardiovasc Transl Res 2024:10.1007/s12265-024-10520-y. [PMID: 38771459 DOI: 10.1007/s12265-024-10520-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024]
Abstract
Heart failure (HF) remains a major cause of mortality and morbidity worldwide. Understanding the genetic basis of HF allows for the development of disease-modifying therapies, more appropriate risk stratification, and personalised management of patients. The advent of next-generation sequencing has enabled genome-wide association studies; moving beyond rare variants identified in a Mendelian fashion and detecting common DNA variants associated with disease. We summarise the latest GWAS and rare variant data on mixed and refined HF aetiologies, and cardiomyopathies. We describe the recent understanding of the functional impact of titin variants and highlight FHOD3 as a novel cardiomyopathy-associated gene. We describe future directions of research in this field and how genetic data can be leveraged to improve the care of patients with HF.
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Affiliation(s)
- Srinjay Mukhopadhyay
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
- School of Medicine, Cardiff University, Wales, UK
| | - Prithvi Dixit
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
| | - Najiyah Khanom
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
| | - Gianluca Sanghera
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
| | - Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK.
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
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16
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Ramírez-Luzuriaga MJ, Kobes S, Hsueh WC, Baier LJ, Hanson RL. Novel signals and polygenic score for height are associated with pubertal growth traits in Southwestern American Indians. Hum Mol Genet 2024; 33:981-990. [PMID: 38483351 DOI: 10.1093/hmg/ddae030] [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: 10/13/2023] [Revised: 02/02/2024] [Accepted: 02/16/2024] [Indexed: 05/20/2024] Open
Abstract
Most genetic variants associated with adult height have been identified through large genome-wide association studies (GWASs) in European-ancestry cohorts. However, it is unclear how these variants influence linear growth during adolescence. This study uses anthropometric and genotypic data from a longitudinal study conducted in an American Indian community in Arizona between 1965-2007. Growth parameters (i.e. height, velocity, and timing of growth spurt) were derived from the Preece-Baines growth model, a parametric growth curve fitted to longitudinal height data, in 787 participants with height measurements spanning the whole period of growth. Heritability estimates suggested that genetic factors could explain 25% to 71% of the variance of pubertal growth traits. We performed a GWAS of growth parameters, testing their associations with 5 077 595 imputed or directly genotyped variants. Six variants associated with height at peak velocity (P < 5 × 10-8, adjusted for sex, birth year and principal components). Implicated genes include NUDT3, previously associated with adult height, and PACSIN1. Two novel variants associated with duration of growth spurt (P < 5 × 10-8) in LOC105375344, an uncharacterized gene with unknown function. We finally examined the association of growth parameters with a polygenic score for height derived from 9557 single nucleotide polymorphisms (SNPs) identified in the GIANT meta-analysis for which genotypic data were available for the American Indian study population. Height polygenic score was correlated with the magnitude and velocity of height growth that occurred before and at the peak of the adolescent growth spurt, indicating overlapping genetic architecture, with no influence on the timing of adolescent growth.
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Affiliation(s)
- Maria J Ramírez-Luzuriaga
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 E indian School Rd, Phoenix, AZ 85014, United States
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 E indian School Rd, Phoenix, AZ 85014, United States
| | - Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 E indian School Rd, Phoenix, AZ 85014, United States
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 E indian School Rd, Phoenix, AZ 85014, United States
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 1550 E indian School Rd, Phoenix, AZ 85014, United States
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17
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Liang J, Liu G, Wang W, Xue H. Causal relationships between gut microbiota and lymphoma: a bidirectional Mendelian randomization study. Front Cell Infect Microbiol 2024; 14:1374775. [PMID: 38803568 PMCID: PMC11128559 DOI: 10.3389/fcimb.2024.1374775] [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: 01/22/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
Background Multiple studies have suggested a possible connection between the gut microbiota and the development of lymphoma, though the exact nature of this relationship remains unclear. This study aimed to explore whether a causal association exists between gut microbiota and lymphoma. Methods A bidirectional two-sample Mendelian randomization (MR) approach was conducted to investigate potential causal effects between gut microbiota and various lymphoma subtypes. The primary method employed for MR analysis was inverse variance weighted (IVW), supplemented by additional methods including MR-Egger, weighted median, and weighted mode approaches. The Cochrane Q test, MR-PRESSO global test and MR-Egger intercept test were performed to assess pleiotropy and heterogeneity. Furthermore, a reverse MR analysis was performed to explore potential reverse causal effect. Results The primary MR analysis identified 36 causal relationships between genetic liabilities in gut microbiota and different lymphoma subtypes. Neither the MR-PRESSO test nor the MR-Egger regression detected any pleiotropy, and Cochran's Q test indicated no significant heterogeneity. Conclusions Our MR analysis revealed substantial causal associations between gut microbiota and lymphoma, offering new insights into lymphoma prevention and management microbiota.
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Affiliation(s)
- Jing Liang
- Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Gengqiu Liu
- Department of Thoracic Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Wenqing Wang
- Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Hongman Xue
- Pediatric Hematology Laboratory, Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China
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18
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de Oliveira LF, Veroneze R, Sousa KRS, Mulim HA, Araujo AC, Huang Y, Johnson JS, Brito LF. Genomic regions, candidate genes, and pleiotropic variants associated with physiological and anatomical indicators of heat stress response in lactating sows. BMC Genomics 2024; 25:467. [PMID: 38741036 DOI: 10.1186/s12864-024-10365-4] [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: 02/12/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses. RESULTS A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively. CONCLUSIONS Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.
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Affiliation(s)
- Letícia Fernanda de Oliveira
- Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
| | - Renata Veroneze
- Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
| | - Katiene Régia Silva Sousa
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
- Department of Oceanography and Limnology, Federal University of Maranhão, São Luís, MA, Brazil
| | - Henrique A Mulim
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
| | | | | | - Jay S Johnson
- USDA-ARS Livestock Behavior Research Unit, West Lafayette, IN, USA
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA.
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19
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Zhou Z, Leng H. Deciphering the causal relationship between plasma and cerebrospinal fluid metabolites and glioblastoma multiforme: a Mendelian Randomization study. Aging (Albany NY) 2024; 16:8306-8319. [PMID: 38742944 PMCID: PMC11131984 DOI: 10.18632/aging.205818] [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: 12/26/2023] [Accepted: 04/10/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is one of the most aggressive and fatal brain cancers. The study of metabolites could be crucial for understanding GBM's biology and reveal new treatment strategies. METHODS The GWAS data for GBM were sourced from the FinnGen database. A total of 1400 plasma metabolites were collected from the GWAS Catalog dataset. The cerebrospinal fluid (CSF) metabolites data were collected from subsets of participants in the WADRC and WRAP studies. We utilized the inverse variance weighting (IVW) method as the primary tool to explore the causal relationship between metabolites in plasma and CSF and glioblastoma, ensuring the exclusion of instances with horizontal pleiotropy. Additionally, four supplementary analytical methods were applied to reinforce our findings. Aberrant results were identified and omitted based on the outcomes of the leave-one-out sensitivity analysis. Conclusively, a reverse Mendelian Randomization analysis was also conducted to further substantiate our results. RESULTS The study identified 69 plasma metabolites associated with GBM. Of these, 40 metabolites demonstrated a significant positive causal relationship with GBM, while 29 exhibited a significant negative causal association. Notably, Trimethylamine N-oxide (TMAO) levels in plasma, not CSF, were found to be a significant exposure factor for GBM (OR = 3.1627, 95% CI = (1.6347, 6.1189), P = 0.0006). The study did not find a reverse causal relationship between GBM and plasma TMAO levels. CONCLUSIONS This research has identified 69 plasma metabolites potentially associated with the incidence of GBM, among which TMAO stands out as a promising candidate for an early detectable biomarker for GBM.
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Affiliation(s)
- Zhiwei Zhou
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
| | - Haibin Leng
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
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20
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Xu H, Ma Y, Long Y, Liu R, Cheng Z, Xie X, Han X, Wang X. Uterine leiomyoma causes an increase in systolic blood pressure: a two-sample Mendelian randomization study. Front Endocrinol (Lausanne) 2024; 15:1373724. [PMID: 38800482 PMCID: PMC11116641 DOI: 10.3389/fendo.2024.1373724] [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: 01/20/2024] [Accepted: 04/24/2024] [Indexed: 05/29/2024] Open
Abstract
Objectives Hypertension and hypertensive disorders of pregnancy (HDP) are common diseases in women at different stages, which affect women's physical and mental health, and the impact of the latter on the offspring cannot not be ignored. Observational studies have investigated the correlation between uterine leiomyoma (UL) and the above conditions, but the relationship remains unclear. In this study, we employed two-sample Mendelian randomization (MR) analysis to assess the association between UL and hypertension, HDP, as well as blood pressure. Methods We collected genetic association data of UL (35,474 cases), hypertension (129,909 cases), HDP (gestational hypertension with 8,502 cases, pre-eclampsia with 6,663 cases and eclampsia with 452cases), systolic blood pressure (SBP) and diastolic blood pressure (DBP) (both 757,601 participants) from published available genome-wide association studies (GWAS). The single nucleotide polymorphisms (SNPs) associated with UL phenotype were used as instrumental variables, and hypertension, three sub-types of HDP, SBP and DBP were used as outcomes. The inverse-variance weighted (IVW) method was employed as the primary method of causal inference. Heterogeneity was assessed using Cochran's Q test, and sensitivity analyses were conducted using MR-Egger regression and MR pleiotropy residual sum and outlier (MR-PRESSO) tests to evaluate the pleiotropy of instrumental variables. PhenoScanner search was used to remove confounding SNP. Robustness and reliability of the results were assessed using methods such as the weighted median and weighted mode. Results The IVW analysis revealed a positive correlation between genetically predicted UL and SBP [odds ratio (OR)= 1.67, 95% confidence interval (CI):1.24~2.25, P = 0.0007], and no statistical association was found between UL and hypertension, HDP, or DBP. The MR-Egger regression suggested that the above causal relationships were not affected by horizontal pleiotropy. The weighted median method and weighted model produced similar results to the IVW. Conclusion Based on large-scale population GWAS data, our MR analysis suggested a causal relationship between UL and SBP. Therefore, women with UL, especially pregnant women, should pay attention to monitoring their blood pressure levels. For patients with hypertension who already have UL, interventions for UL may serve as potential therapeutic methods for managing blood pressure.
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Affiliation(s)
- Hui Xu
- Obstetrics and Gynecology Department, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
- The First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuxia Ma
- College of Acupuncture, Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yi Long
- Shandong Provincial Traditional Chinese Medicine Data Center Management Office, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ren Liu
- Medical Affairs Office, The Fifth Affiliated Hospital Sun Yat-sen University, Zhuhai, China
| | - Ziyang Cheng
- The First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiuzhen Xie
- Obstetrics and Gynecology Department, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xingjun Han
- Disease Prevention Center, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xuan Wang
- Disease Prevention Center, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
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21
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Minikel EV, Painter JL, Dong CC, Nelson MR. Refining the impact of genetic evidence on clinical success. Nature 2024; 629:624-629. [PMID: 38632401 PMCID: PMC11096124 DOI: 10.1038/s41586-024-07316-0] [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: 07/05/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024]
Abstract
The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.
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Affiliation(s)
| | - Jeffery L Painter
- JiveCast, Raleigh, NC, USA
- GlaxoSmithKline, Research Triangle Park, NC, USA
| | | | - Matthew R Nelson
- Deerfield Management Company LP, New York, NY, USA.
- Genscience LLC, New York, NY, USA.
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22
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Farbehi N, Neavin DR, Cuomo ASE, Studer L, MacArthur DG, Powell JE. Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases. Nat Genet 2024; 56:758-766. [PMID: 38741017 DOI: 10.1038/s41588-024-01731-9] [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: 01/24/2023] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.
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Affiliation(s)
- Nona Farbehi
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
| | - Drew R Neavin
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Anna S E Cuomo
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
| | - Lorenz Studer
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
- The Center for Stem Cell Biology and Developmental Biology Program, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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23
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Toli EA, Kemppainen P, Bounas A, Sotiropoulos K. Genetic insight into a polygenic trait using a novel genome-wide association approach in a wild amphibian population. Mol Ecol 2024; 33:e17344. [PMID: 38597332 DOI: 10.1111/mec.17344] [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: 06/21/2023] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024]
Abstract
Body size variation is central in the evolution of life-history traits in amphibians, but the underlying genetic architecture of this complex trait is still largely unknown. Herein, we studied the genetic basis of body size and fecundity of the alternative morphotypes in a wild population of the Greek smooth newt (Lissotriton graecus). By combining a genome-wide association approach with linkage disequilibrium network analysis, we were able to identify clusters of highly correlated loci thus maximizing sequence data for downstream analysis. The putatively associated variants explained 12.8% to 44.5% of the total phenotypic variation in body size and were mapped to genes with functional roles in the regulation of gene expression and cell cycle processes. Our study is the first to provide insights into the genetic basis of complex traits in newts and provides a useful tool to identify loci potentially involved in fitness-related traits in small data sets from natural populations in non-model species.
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Affiliation(s)
- Elisavet-Aspasia Toli
- Molecular Ecology & Conservation Genetics Lab, Department of Biological Applications & Technology, University of Ioannina, Ioannina, Greece
| | - Petri Kemppainen
- Area of Ecology and Biodiversity, School of Biological Sciences, University of Hong Kong, Hong Kong City, Hong Kong SAR
- Ecological Genetics Research Unit, Organismal and Evolutionary Biology Programme, University of Helsinki, Helsinki, Finland
| | - Anastasios Bounas
- Molecular Ecology & Conservation Genetics Lab, Department of Biological Applications & Technology, University of Ioannina, Ioannina, Greece
| | - Konstantinos Sotiropoulos
- Molecular Ecology & Conservation Genetics Lab, Department of Biological Applications & Technology, University of Ioannina, Ioannina, Greece
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24
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Bhardwaj S, Togla O, Mumtaz S, Yadav N, Tiwari J, Muansangi L, Illa SK, Wani YM, Mukherjee S, Mukherjee A. Comparative assessment of the effective population size and linkage disequilibrium of Karan Fries cattle revealed viable population dynamics. Anim Biosci 2024; 37:795-806. [PMID: 37946419 PMCID: PMC11065711 DOI: 10.5713/ab.23.0263] [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: 07/15/2023] [Revised: 08/21/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
OBJECTIVE Karan Fries (KF), a high-producing composite cattle was developed through crossing indicine Tharparkar cows with taurine bulls (Holstein Friesian, Brown Swiss, and Jersey), to increase the milk yield across India. This composite cattle population must maintain sufficient genetic diversity for long-term development and breed improvement in the coming years. The level of linkage disequilibrium (LD) measures the influence of population genetic forces on the genomic structure and provides insights into the evolutionary history of populations, while the decay of LD is important in understanding the limits of genome-wide association studies for a population. Effective population size (Ne) which is genomically based on LD accumulated over the course of previous generations, is a valuable tool for e valuation of the genetic diversity and level of inbreeding. The present study was undertaken to understand KF population dynamics through the estimation of Ne and LD for the longterm sustainability of these breeds. METHODS The present study included 96 KF samples genotyped using Illumina HDBovine array to estimate the effective population and examine the LD pattern. The genotype data were also obtained for other crossbreds (Santa Gertrudis, Brangus, and Beefmaster) and Holstein Friesian cattle for comparison purposes. RESULTS The average LD between single nucleotide polymorphisms (SNPs) was r2 = 0.13 in the present study. LD decay (r2 = 0.2) was observed at 40 kb inter-marker distance, indicating a panel with 62,765 SNPs was sufficient for genomic breeding value estimation in KF cattle. The pedigree-based Ne of KF was determined to be 78, while the Ne estimates obtained using LD-based methods were 52 (SNeP) and 219 (genetic optimization for Ne estimation), respectively. CONCLUSION KF cattle have an Ne exceeding the FAO's minimum recommended level of 50, which was desirable. The study also revealed significant population dynamics of KF cattle and increased our understanding of devising suitable breeding strategies for longterm sustainable development.
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Affiliation(s)
- Shivam Bhardwaj
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Oshin Togla
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Shabahat Mumtaz
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Nistha Yadav
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
- Department of Animal Genetics and Breeding, CVAS, RAJUVAS, Bikaner 334001, Rajasthan,
India
| | - Jigyasha Tiwari
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Lal Muansangi
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Satish Kumar Illa
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
- Livestock Research Station, Garividi Sri Venkateswara Veterinary University, Andhra Pradesh 535101,
India
| | - Yaser Mushtaq Wani
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Sabyasachi Mukherjee
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
| | - Anupama Mukherjee
- Animal Genetics and Breeding Division, ICAR- National Dairy Research Institute (NDRI), Karnal 132001, Haryana,
India
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25
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O’Brien NLV, Holland B, Engelstädter J, Ortiz-Barrientos D. The distribution of fitness effects during adaptive walks using a simple genetic network. PLoS Genet 2024; 20:e1011289. [PMID: 38787919 PMCID: PMC11156440 DOI: 10.1371/journal.pgen.1011289] [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: 01/31/2024] [Revised: 06/06/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024] Open
Abstract
The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.
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Affiliation(s)
- Nicholas L. V. O’Brien
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Barbara Holland
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, University of Tasmania, Hobart, Tasmania, Australia
| | - Jan Engelstädter
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Daniel Ortiz-Barrientos
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
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26
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Gedik H, Peterson R, Chatzinakos C, Dozmorov MG, Vladimirov V, Riley BP, Bacanu SA. A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.14.24305811. [PMID: 38699366 PMCID: PMC11065030 DOI: 10.1101/2024.04.14.24305811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.
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27
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Shen J, Jiang L, Wang K, Wang A, Chen F, Newcombe PJ, Haiman CA, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction. Genet Epidemiol 2024. [PMID: 38606643 DOI: 10.1002/gepi.22562] [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: 09/27/2023] [Revised: 02/27/2024] [Accepted: 03/27/2024] [Indexed: 04/13/2024]
Abstract
Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.
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Affiliation(s)
- Jiayi Shen
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Lai Jiang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Kan Wang
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Anqi Wang
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Fei Chen
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Paul J Newcombe
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher A Haiman
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Division of Biostatistics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Population and Public Health Science, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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28
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Speed D, Evans DM. Estimating disease heritability from complex pedigrees allowing for ascertainment and covariates. Am J Hum Genet 2024; 111:680-690. [PMID: 38490208 PMCID: PMC11023822 DOI: 10.1016/j.ajhg.2024.02.010] [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: 07/08/2023] [Revised: 02/04/2024] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
We propose TetraHer, a method for estimating the liability heritability of binary phenotypes. TetraHer has five key features. First, it can be applied to data from complex pedigrees that contain multiple types of relationships. Second, it can correct for ascertainment of cases or controls. Third, it can accommodate covariates. Fourth, it can model the contribution of common environment. Fifth, it produces a likelihood that can be used for significance testing. We first demonstrate the validity of TetraHer on simulated data. We then use TetraHer to estimate liability heritability for 229 codes from the tenth International Classification of Diseases (ICD-10). We identify 107 codes with significant heritability (p < 0.05/229), which can be used in future analyses for investigating the genetic architecture of human diseases.
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Affiliation(s)
- Doug Speed
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
| | - David M Evans
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia; Frazer Institute, University of Queensland, Brisbane, QLD, Australia; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
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29
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Akaishi T, Misu T, Takahashi T, Fujihara K, Fujimori J, Nakashima I, Aoki M. Stochastic models for the onset and disease course of multiple sclerosis. Clin Neurol Neurosurg 2024; 239:108224. [PMID: 38447482 DOI: 10.1016/j.clineuro.2024.108224] [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: 06/23/2023] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVE Exact causes and mechanisms regulating the onset and progression in many chronic diseases, including multiple sclerosis (MS), remain uncertain. Until now, the potential role of random process based on stochastic models in the temporal course of chronic diseases remains largely unevaluated. Therefore, the present study investigated the applicability of stochastic models for the onset and disease course of MS. METHODS Stochastic models with random temporal process in disease activity, underlying clinical relapse and/or subclinical brain atrophy, were developed. The models incorporated parameters regarding the distribution of temporal changes in disease activity and the drift constant. RESULTS By adjusting the parameters (temporal change dispersion and drift constant) and the threshold for the onset of disease, the stochastic disease progression models could reproduce various types of subsequent disease course, such as clinically isolated syndrome (monophasic), relapsing-remitting MS, primary-progressive MS, and secondary-progressive MS. Furthermore, the disease prevalence and distribution of onset age could be also reproduced with stochastic models by adjusting the parameters. The models could further explain why approximately half of the patients with relapsing-remitting MS will eventually experience a transition to secondary-progressive MS. CONCLUSION Stochastic models with random temporal changes in disease activity could reproduce the characteristic onset age distribution and disease course forms in MS. Further studies by using real-world data to underscore the significance of random process in the occurrence and progression of MS are warranted.
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Affiliation(s)
- Tetsuya Akaishi
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Tatsuro Misu
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Toshiyuki Takahashi
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Neurology, National Hospital Organization Yonezawa National Hospital, Yonezawa, Japan
| | - Kazuo Fujihara
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan; Multiple Sclerosis Therapeutics, Fukushima Medical University, Fukushima, Japan
| | - Juichi Fujimori
- Department of Neurology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Ichiro Nakashima
- Department of Neurology, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Masashi Aoki
- Department of Neurology, Tohoku University Graduate School of Medicine, Sendai, Japan
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30
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Hu J, Weber JN, Fuess LE, Steinel NC, Bolnick DI, Wang M. A spectral framework to map QTLs affecting joint differential networks of gene co-expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.29.587398. [PMID: 38585912 PMCID: PMC10996691 DOI: 10.1101/2024.03.29.587398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype → expression → phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype → network → phenotype mechanism. Here, we develop a network-based method, called snQTL, to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.
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Affiliation(s)
- Jiaxin Hu
- Department of Statistics, University of Wisconsin-Madison
| | - Jesse N. Weber
- Department of Integrative Biology, University of Wisconsin-Madison
| | | | | | - Daniel I. Bolnick
- Department of Ecology and Evolutionary Biology, University of Connecticut
| | - Miaoyan Wang
- Department of Statistics, University of Wisconsin-Madison
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31
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Jiang Y, Li H, Li Z, Du S, Zhang R, Zhao Y, Christiani DC, Shen S, Chen F. A cross-trait study of lung cancer and its related respiratory diseases based on large-scale exome sequencing population. Transl Lung Cancer Res 2024; 13:512-525. [PMID: 38601445 PMCID: PMC11002514 DOI: 10.21037/tlcr-24-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/27/2024] [Indexed: 04/12/2024]
Abstract
Background Genome-wide association studies (GWASs) explain the genetic susceptibility between diseases and common variants. Nevertheless, with the appearance of large-scale sequencing profiles, we could explore the rare coding variants in disease pathogenesis. Methods We estimated the genetic correlation of nine respiratory diseases and lung cancer in the UK Biobank (UKB) by linkage disequilibrium score regression (LDSC). Then, we performed exome-wide association studies at single-variant level and gene-level for lung cancer and lung cancer-related respiratory diseases using the whole-exome sequencing (WES) data of 427,934 European participants. Cross-trait meta-analysis was conducted by association analysis based on subsets (ASSET) to identify the pleiotropic variants, while in-silico functional analysis was performed to explore their function. Causal mediation analysis was used to explore whether these pleiotropic variants lead to lung cancer is mediated by affecting the chronic respiratory diseases. Results Five respiratory diseases [emphysema, pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and fibrosis] were genetically correlated with lung cancer. We identified 102 significant independent variants at single-variant levels for lung cancer and five lung cancer-related diseases. 15:78590583:G>A (missense variant in CHRNA5) was shared in lung cancer, emphysema, and COPD. Meanwhile, 14 significant genes and 87 suggestive genes were identified in gene-based association tests, including HSD3B7 (lung cancer), SRSF2 (pneumonia), TNXB (asthma), TERT (fibrosis), MOSPD3 (emphysema). Based on the cross-trait meta-analysis, we detected 145 independent pleiotropic variants. We further identified abundant pathways with significant enrichment effects, demonstrating that these pleiotropic genes were functional. Meanwhile, the proportion of mediation effects of these variants ranged from 6 to 23 (emphysema: 23%; COPD: 20%; pneumonia: 20%; fibrosis: 7%; asthma: 6%) through these five respiratory diseases to the incidence of lung cancer. Conclusions The identified shared genetic variants, genes, biological pathways, and potential intermediate causal pathways provide a basis for further exploration of the relationship between lung cancer and respiratory diseases.
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Affiliation(s)
- Yunke Jiang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Hongru Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zaiming Li
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Sha Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ruyang Zhang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing, China
| | - David C. Christiani
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Pulmonary and Critical Care Division, Massachusetts General Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Sipeng Shen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing, China
| | - Feng Chen
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- China International Cooperation Center of Environment and Human Health, Nanjing Medical University, Nanjing, China
- Key Laboratory of Biomedical Big Data of Nanjing Medical University, Nanjing, China
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32
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Oh EY, Han KM, Kim A, Kang Y, Tae WS, Han MR, Ham BJ. Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study. Transl Psychiatry 2024; 14:141. [PMID: 38461185 PMCID: PMC10924915 DOI: 10.1038/s41398-024-02849-4] [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: 09/15/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.
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Affiliation(s)
- Eun-Young Oh
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Mi-Ryung Han
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea.
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea.
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Chen Y, Zou J. GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.16.562533. [PMID: 37905130 PMCID: PMC10614824 DOI: 10.1101/2023.10.16.562533] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
There has been significant recent progress in leveraging large-scale gene expression data to develop foundation models for single-cell biology. Models such as Geneformer and scGPT implicitly learn gene and cellular functions from the gene expression profiles of millions of cells, which requires extensive data curation and resource-intensive training. Here we explore a much simpler alternative by leveraging ChatGPT embeddings of genes based on literature. Our proposal, GenePT, uses NCBI text descriptions of individual genes with GPT-3.5 to generate gene embeddings. From there, GenePT generates single-cell embeddings in two ways: (i) by averaging the gene embeddings, weighted by each gene's expression level; or (ii) by creating a sentence embedding for each cell, using gene names ordered by the expression level. Without the need for dataset curation and additional pretraining, GenePT is efficient and easy to use. On many downstream tasks used to evaluate recent single-cell foundation models - e.g., classifying gene properties and cell types - GenePT achieves comparable, and often better, performance than Geneformer and other models. GenePT demonstrates that large language model embedding of literature is a simple and effective path for biological foundation models.
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Affiliation(s)
- Yiqun Chen
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, 94305, CA, USA
- Department of Computer Science, Stanford University, Stanford, 94305, CA, USA
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Zhou H, McPeek MS. Overcoming the "feast or famine" effect: improved interaction testing in genome-wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580168. [PMID: 38405994 PMCID: PMC10888770 DOI: 10.1101/2024.02.13.580168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In genetic association analysis of complex traits, detection of interaction (either GxG or GxE) can help to elucidate the genetic architecture and biological mechanisms underlying the trait. Detection of interaction in a genome-wide association study (GWAS) can be methodologically challenging for various reasons, including a high burden of multiple comparisons when testing for epistasis between all possible pairs of a set of genomewide variants, as well as heteroscedasticity effects occurring in the presence of GxG or GxE interaction. In this paper, we address the problem of an even more striking phenomenon that we call the "feast or famine" effect that occurs when testing interaction in a genomewide context. As we verify, even in a simplified setting in which there is no interaction at all (and so no heteroscedasticity), in a GWAS to detect GxG or GxE interaction with a fixed genetic variant or environmental factor, the distribution of the genome-wide p-values under the null hypothesis is not the i.i.d. uniform one that is commonly assumed. Using standard methods, even if all SNPs are independent, some GWASs will have systematically underinflated p-values ("feast"), and others will have systematically overinflated p-values ("famine"), which can lead to false detection of interaction, reduced power, inconsistent results across studies, and failure to replicate true signal. This startling phenomenon is specific to detection of interaction in a GWAS, and it may partly explain why such detection has so far proved challenging and difficult to replicate. We show theoretically that the key cause of this phenomenon is which variables are conditioned on in the analysis, and this suggests an approach to correct the problem by changing the way the conditioning is done. Using this insight, we have developed the TINGA method to adjust the interaction test statistics to make their p-values closer to uniform under the null hypothesis. In simulations we show that TINGA both controls type 1 error and improves power. TINGA allows for covariates and population structure through use of a linear mixed model and accounts for heteroscedasticity. We apply TINGA to detection of epistasis in a study of flowering time in Arabidopsis thaliana.
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Affiliation(s)
- Huanlin Zhou
- Department of Statistics, The University of Chicago, Chicago, Illinois, U.S.A
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, Illinois, U.S.A
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, U.S.A
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Caudal A, Snyder MP, Wu JC. Harnessing human genetics and stem cells for precision cardiovascular medicine. CELL GENOMICS 2024; 4:100445. [PMID: 38359791 PMCID: PMC10879032 DOI: 10.1016/j.xgen.2023.100445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 09/22/2023] [Accepted: 10/25/2023] [Indexed: 02/17/2024]
Abstract
Human induced pluripotent stem cell (iPSC) platforms are valuable for biomedical and pharmaceutical research by providing tissue-specific human cells that retain patients' genetic integrity and display disease phenotypes in a dish. Looking forward, combining iPSC phenotyping platforms with genomic and screening technologies will continue to pave new directions for precision medicine, including genetic prediction, visualization, and treatment of heart disease. This review summarizes the recent use of iPSC technology to unpack the influence of genetic variants in cardiovascular pathology. We focus on various state-of-the-art genomic tools for cardiovascular therapies-including the expansion of genetic toolkits for molecular interrogation, in vitro population studies, and function-based drug screening-and their current applications in patient- and genome-edited iPSC platforms that are heralding new avenues for cardiovascular research.
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Affiliation(s)
- Arianne Caudal
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Greenstone Biosciences, Palo Alto, CA 94304, USA.
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Arunachalam V, Lea R, Hoy W, Lee S, Mott S, Savige J, Mathews JD, McMorran BJ, Nagaraj SH. Novel genetic markers for chronic kidney disease in a geographically isolated population of Indigenous Australians: Individual and multiple phenotype genome-wide association study. Genome Med 2024; 16:29. [PMID: 38347632 PMCID: PMC10860247 DOI: 10.1186/s13073-024-01299-3] [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] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is highly prevalent among Indigenous Australians, especially those in remote regions. The Tiwi population has been isolated from mainland Australia for millennia and exhibits unique genetic characteristics that distinguish them from other Indigenous and non-Indigenous populations. Notably, the rate of end-stage renal disease is up to 20 times greater in this population compared to non-Indigenous populations. Despite the identification of numerous genetic loci associated with kidney disease through GWAS, the Indigenous population such as Tiwi remains severely underrepresented and the increased prevalence of CKD in this population may be due to unique disease-causing alleles/genes. METHODS We used albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) to estimate the prevalence of kidney disease in the Tiwi population (N = 492) in comparison to the UK Biobank (UKBB) (N = 134,724) database. We then performed an exploratory factor analysis to identify correlations among 10 CKD-related phenotypes and identify new multi-phenotype factors. We subsequently conducted a genome-wide association study (GWAS) on all single and multiple phenotype factors using mixed linear regression models, adjusted for age, sex, population stratification, and genetic relatedness between individuals. RESULTS Based on ACR, 20.3% of the population was at severely increased risk of CKD progression and showed elevated levels of ACR compared to the UKBB population independent of HbA1c. A GWAS of ACR revealed novel association loci in the genes MEG3 (chr14:100812018:T:A), RAB36 (rs11704318), and TIAM2 (rs9689640). Additionally, multiple phenotypes GWAS of ACR, eGFR, urine albumin, and serum creatinine identified a novel variant that mapped to the gene MEIS2 (chr15:37218869:A:G). Most of the identified variants were found to be either absent or rare in the UKBB population. CONCLUSIONS Our study highlights the Tiwi population's predisposition towards elevated ACR, and the collection of novel genetic variants associated with kidney function. These associations may prove valuable in the early diagnosis and treatment of renal disease in this underrepresented population. Additionally, further research is needed to comprehensively validate the functions of the identified variants/genes.
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Affiliation(s)
- Vignesh Arunachalam
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rodney Lea
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Wendy Hoy
- Centre of chronic disease, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Simon Lee
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Susan Mott
- Centre of chronic disease, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Judith Savige
- Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - John D Mathews
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Brendan J McMorran
- National Centre for Indigenous Genomics, The John Curtin of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia.
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Sheng C, Zeng Q, Huang W, Liao M, Yang P. Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes. PLoS One 2024; 19:e0296729. [PMID: 38335213 PMCID: PMC10857568 DOI: 10.1371/journal.pone.0296729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/18/2023] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development. METHODS In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods. RESULTS 38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed. CONCLUSION We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.
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Affiliation(s)
- Chang Sheng
- Department of Vascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qin Zeng
- National Health Commission Key Laboratory of Nanobiological Technology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Weihua Huang
- Department of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - Mingmei Liao
- National Health Commission Key Laboratory of Nanobiological Technology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Pu Yang
- Department of Vascular Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Tan X, Zhang J, Dong J, Huang M, Li Q, Wang H, Bai L, Cui M, Zhou Z, Yang S, Wang D. Whole-genome variants dataset of 209 local chickens from China. Sci Data 2024; 11:169. [PMID: 38316816 PMCID: PMC10844214 DOI: 10.1038/s41597-024-02995-w] [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: 03/24/2023] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Compared to commercial chickens, local breeds exhibit better in meat quality and flavour, but the productivity (e.g., growth rate, body weight) of local chicken breeds is rather low. Genetic analysis based on whole-genome sequencing contributes to elucidating the genetic markers or putative candidate genes related to some economic traits, facilitating the improvement of production performance, the acceleration of breeding progress, and the conservation of genetic resources. Here, a total of 209 local chickens from 13 breeds were investigated, and the observation of approximately 91.4% high-quality sequences (Q30 > 90%) and a mapping rate over 99% for each individual indicated good results of this study, as confirmed by a genome coverage of 97.6%. Over 19 million single nucleotide polymorphisms (SNPs) and 1.98 million insertion-deletions (InDels) were identified using the reference genome (GRCg7b), further contributing to the public database. This dataset provides valuable resources for studying genetic diversity and adaptation and for the cultivation of new chicken breeds/lines.
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Affiliation(s)
- Xiaodong Tan
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Jiawen Zhang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Jie Dong
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Minjie Huang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Qinghai Li
- Animal Husbandry Institute, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, China
| | - Huanhuan Wang
- Animal Husbandry Institute, Hangzhou Academy of Agricultural Sciences, Hangzhou, 310024, China
| | - Lijuan Bai
- Zhejiang Animal Husbandry Technology Extension and Breeding Livestock and Poultry Monitoring Station, Hangzhou, 310020, China
| | - Ming Cui
- Zhejiang Animal Husbandry Technology Extension and Breeding Livestock and Poultry Monitoring Station, Hangzhou, 310020, China
| | - Zhenzhen Zhou
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Shuyuan Yang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China
| | - Deqian Wang
- Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China.
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Brown MA. Polygenic risk scores. Semin Arthritis Rheum 2024; 64S:152330. [PMID: 38042619 DOI: 10.1016/j.semarthrit.2023.152330] [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: 05/29/2023] [Accepted: 09/23/2023] [Indexed: 12/04/2023]
Abstract
Polygenic risk scores (PRS) estimate an individual's genetic risk for a disease or trait compared to a matched population. For many rheumatic diseases PRS have been developed that have discriminatory capacity better than some widely used biomarkers, and in some cases are the most discriminatory tests available. These PRS assessments do not rely on the disease being present or its activity level, making them highly valuable for predicting disease development or enabling early diagnosis. However, most PRS to date have been developed in research settings, and in populations of European-ancestry. Further studies are required to assess their utility in clinical settings, in relation to existing tests, and in non-European populations. Such studies are underway, and it is likely in the near future these tests will become widely available, with significant benefits for the practice of medicine.
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Affiliation(s)
- Matthew A Brown
- Chief Scientific Officer, Genomics England, Charterhouse Square, London, England, United Kingdom; Professor of Medicine, King's College London, London, England, United Kingdom.
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40
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Zhang X, Jiang W, Zhao H. Integration of expression QTLs with fine mapping via SuSiE. PLoS Genet 2024; 20:e1010929. [PMID: 38271473 PMCID: PMC10846745 DOI: 10.1371/journal.pgen.1010929] [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: 08/22/2023] [Revised: 02/06/2024] [Accepted: 01/06/2024] [Indexed: 01/27/2024] Open
Abstract
Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.
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Affiliation(s)
- Xiangyu Zhang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Wei Jiang
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University, New Haven, Connecticut, United States of America
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Das P, Majumder R, Sen N, Nandi SK, Ghosh A, Mandal M, Basak P. A computational analysis to evaluate deleterious SNPs of GSK3β, a multifunctional and regulatory protein, for metabolism, wound healing, and migratory processes. Int J Biol Macromol 2024; 256:128262. [PMID: 37989431 DOI: 10.1016/j.ijbiomac.2023.128262] [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: 08/18/2023] [Revised: 10/04/2023] [Accepted: 11/17/2023] [Indexed: 11/23/2023]
Abstract
This study focused on GSK-3β, a critical serine/threonine kinase with diverse cellular functions. However, there is limited understanding of the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on its structure and function. Through an exhaustive in-silico investigation 12 harmful nsSNPs were predicted from a pool of 172 acquired from the NCBI dbSNP database using 12 established tools that detects deleterious SNPs. Consistently, these nsSNPs were discovered in locations with high levels of conservation. Notably, the three harmful nsSNPs F67C, A83T, and T138I were situated in the active/binding site of GSK-3β, which may affect the protein's capacity to bind to substrates and other proteins. Molecular dynamics simulations revealed that the F67C and T138I mutants had stable structures, indicating rigidness, whereas the A83T mutant was unstable. Analysis of secondary structures revealed different modifications in all mutant forms, which may affect the stability, functioning, and interactions of the protein. These mutations appear to alter the structural dynamics of GSK-3β, which may have functional ramifications, such as the formation of novel secondary structures and variations in coil-to-helix transitions. In conclusion, this study illuminates the possible structural and functional ramifications of these GSK-3 nsSNPs, revealing how protein compactness, stiffness, and interactions may affect biological activities.
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Affiliation(s)
- Pratik Das
- School of Bioscience and Engineering, Jadavpur University, Kolkata, India
| | - Ranabir Majumder
- Cancer Biology Lab, School of Medical Science & Technology, Indian Institute of Technology Kharagpur, India
| | - Nandita Sen
- Molecular biology wing, Dept of Biotechnology, PES University, Bangalore, India
| | - Samit Kumar Nandi
- Department of Veterinary Surgery & Radiology, West Bengal University of Animal and Fishery Sciences, Kolkata, India
| | - Arabinda Ghosh
- Department of Computational Biology and Biotechnology, Mahapurusha Srimanta Sankaradeva Viswavidyalaya, Guwahati Unit, Guwahati, Assam, India
| | - Mahitosh Mandal
- Cancer Biology Lab, School of Medical Science & Technology, Indian Institute of Technology Kharagpur, India
| | - Piyali Basak
- School of Bioscience and Engineering, Jadavpur University, Kolkata, India.
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Duncan L, Deisseroth K. Are novel treatments for brain disorders hiding in plain sight? Neuropsychopharmacology 2024; 49:276-281. [PMID: 37422511 PMCID: PMC10700299 DOI: 10.1038/s41386-023-01636-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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 07/10/2023]
Abstract
Psychiatric diseases are strongly influenced by genetics, but genetically guided treatments have been slow to develop, and precise molecular mechanisms remain mysterious. Although individual locations in the genome tend to not contribute powerfully to psychiatric disease incidence, genome-wide association studies (GWAS) have now successfully linked hundreds of specific genetic loci to psychiatric disorders [1-3]. Here, building upon results from well-powered GWAS of four phenotypes relevant to psychiatry, we motivate an exploratory workflow leading from GWAS screening, through causal testing in animal models using methods such as optogenetics, to new therapies in human beings. We focus on schizophrenia and the dopamine D2 receptor (DRD2), hot flashes and the neurokinin B receptor (TACR3), cigarette smoking and receptors bound by nicotine (CHRNA5, CHRNA3, CHRNB4), and alcohol use and enzymes that help to break down alcohol (ADH1B, ADH1C, ADH7). A single genomic locus may not powerfully determine disease at the level of the population, but the same locus may nevertheless represent a potent treatment target suitable for population-wide therapeutic approaches.
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Affiliation(s)
- Laramie Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
| | - Karl Deisseroth
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Department of Bioengineering and Howard Hughes Medial Institute, Stanford University, Stanford, CA, USA.
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Grunert M, Dorn C, Dopazo A, Sánchez-Cabo F, Vázquez J, Rickert-Sperling S, Lara-Pezzi E. Technologies to Study Genetics and Molecular Pathways. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1441:435-458. [PMID: 38884724 DOI: 10.1007/978-3-031-44087-8_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Over the last few decades, the study of congenital heart disease (CHD) has benefited from various model systems and the development of molecular biological techniques enabling the analysis of single gene as well as global effects. In this chapter, we first describe different models including CHD patients and their families, animal models ranging from invertebrates to mammals, and various cell culture systems. Moreover, techniques to experimentally manipulate these models are discussed. Second, we introduce cardiac phenotyping technologies comprising the analysis of mouse and cell culture models, live imaging of cardiogenesis, and histological methods for fixed hearts. Finally, the most important and latest molecular biotechniques are described. These include genotyping technologies, different applications of next-generation sequencing, and the analysis of transcriptome, epigenome, proteome, and metabolome. In summary, the models and technologies presented in this chapter are essential to study the function and development of the heart and to understand the molecular pathways underlying CHD.
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Affiliation(s)
- Marcel Grunert
- Cardiovascular Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- DiNAQOR AG, Schlieren, Switzerland
| | - Cornelia Dorn
- Cardiovascular Genetics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ana Dopazo
- Genomics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Fátima Sánchez-Cabo
- Bioinformatics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | - Jésus Vázquez
- Proteomics Unit, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain
| | | | - Enrique Lara-Pezzi
- Myocardial Homeostasis and Cardiac Injury Programme, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain.
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44
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Yoshida H. Dissecting the Immune System through Gene Regulation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1444:219-235. [PMID: 38467983 DOI: 10.1007/978-981-99-9781-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.
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Affiliation(s)
- Hideyuki Yoshida
- YCI Laboratory for Immunological Transcriptomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
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45
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Thami PK, Choga WT, Dandara C, O’Brien SJ, Essex M, Gaseitsiwe S, Chimusa ER. Whole genome sequencing reveals population diversity and variation in HIV-1 specific host genes. Front Genet 2023; 14:1290624. [PMID: 38179408 PMCID: PMC10765519 DOI: 10.3389/fgene.2023.1290624] [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: 09/07/2023] [Accepted: 11/20/2023] [Indexed: 01/06/2024] Open
Abstract
HIV infection continues to be a major global public health issue. The population heterogeneity in susceptibility or resistance to HIV-1 and progression upon infection is attributable to, among other factors, host genetic variation. Therefore, identifying population-specific variation and genetic modifiers of HIV infectivity can catapult the invention of effective strategies against HIV-1 in African populations. Here, we investigated whole genome sequences of 390 unrelated HIV-positive and -negative individuals from Botswana. We report 27.7 million single nucleotide variations (SNVs) in the complete genomes of Botswana nationals, of which 2.8 million were missing in public databases. Our population structure analysis revealed a largely homogenous structure in the Botswana population. Admixture analysis showed elevated components shared between the Botswana population and the Niger-Congo (65.9%), Khoe-San (32.9%), and Europeans (1.1%) ancestries in the population of Botswana. Statistical significance of the mutational burden of deleterious and loss-of-function variants per gene against a null model was estimated. The most deleterious variants were enriched in five genes: ACTRT2 (the Actin Related Protein T2), HOXD12 (homeobox D12), ABCB5 (ATP binding cassette subfamily B member 5), ATP8B4 (ATPase phospholipid transporting 8B4) and ABCC12 (ATP Binding Cassette Subfamily C Member 12). These genes are enriched in the glycolysis and gluconeogenesis (p < 2.84e-6) pathways and therefore, may contribute to the emerging field of immunometabolism in which therapy against HIV-1 infection is being evaluated. Published transcriptomic evidence supports the role of the glycolysis/gluconeogenesis pathways in the regulation of susceptibility to HIV, and that cumulative effects of genetic modifiers in glycolysis/gluconeogenesis pathways may potentially have effects on the expression and clinical variability of HIV-1. Identified genes and pathways provide novel avenues for other interventions, with the potential for informing the design of new therapeutics.
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Affiliation(s)
- Prisca K. Thami
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Wonderful T. Choga
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- UCT/SAMRC Platform for Pharmacogenomics Research and Translation (PREMED) Unit, South African Medical Research Council, Cape Town, South Africa
| | - Stephen J. O’Brien
- Laboratory of Genomics Diversity, Center for Computer Technologies, ITMO University, St. Petersburg, Russia
- Guy Harvey Oceanographic Center Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Myron Essex
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health AIDS Initiative, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Simani Gaseitsiwe
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health AIDS Initiative, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Emile R. Chimusa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle, United Kingdom
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46
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Ghosal S, Schatz MC, Venkataraman A. BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.24.534116. [PMID: 36993396 PMCID: PMC10055416 DOI: 10.1101/2023.03.24.534116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We introduce a novel framework BEATRICE to identify putative causal variants from GWAS summary statistics (https://github.com/sayangsep/Beatrice-Finemapping). Identifying causal variants is challenging due to their sparsity and to highly correlated variants in the nearby regions. To account for these challenges, our approach relies on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to simultaneously sample from the space of causal configurations. We use these samples to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework across different numbers of causal variants and different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. Using this simulated data, we perform a comparative analysis against two state-of-the-art baseline methods for fine-mapping. We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. Thus, BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.
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Affiliation(s)
- Sayan Ghosal
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
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47
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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48
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Mo C, Ye Z, Pan Y, Zhang Y, Wu Q, Bi C, Liu S, Mitchell B, Kochunov P, Hong LE, Ma T, Chen S. An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping. Mol Cell Neurosci 2023; 127:103895. [PMID: 37634742 PMCID: PMC11128188 DOI: 10.1016/j.mcn.2023.103895] [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: 01/19/2023] [Revised: 08/21/2023] [Accepted: 08/24/2023] [Indexed: 08/29/2023] Open
Abstract
In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-related genetic loci (e.g., nicotinic acetylcholine receptor - nAChR subunit genes) are among the most replicable genetic findings. Although GWAS results have reported tens of thousands of SNPs within these loci, further analysis (e.g., fine-mapping) is required to identify the causal variants. However, it is computationally challenging for existing fine-mapping methods to reliably identify causal variants from thousands of candidate SNPs based on the posterior inclusion probability. To address this challenge, we propose a new method to select SNPs by jointly modeling the SNP-wise inference results and the underlying structured network patterns of the linkage disequilibrium (LD) matrix. We use adaptive dense subgraph extraction method to recognize the latent network patterns of the LD matrix and then apply group LASSO to select causal variant candidates. We applied this new method to the UK biobank data to identify the causal variant candidates for nicotine addiction. Eighty-one nicotine addiction-related SNPs (i.e.,-log(p) > 50) of nAChR were selected, which are highly correlated (average r2>0.8) although they are physically distant (e.g., >200 kilobase away) and from various genes. These findings revealed that distant SNPs from different genes can show higher LD r2 than their neighboring SNPs, and jointly contribute to a complex trait like nicotine addiction.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Yuan Zhang
- Department of Statistics, College of Arts and Sciences, Ohio State University, Columbus, Ohio, United States
| | - Qiong Wu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Braxton Mitchell
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, United States
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49
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Khatiwada A, Yilmaz AS, Wolf BJ, Pietrzak M, Chung D. multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results. PLoS Comput Biol 2023; 19:e1011686. [PMID: 38060592 PMCID: PMC10729974 DOI: 10.1371/journal.pcbi.1011686] [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: 02/13/2023] [Revised: 12/19/2023] [Accepted: 11/13/2023] [Indexed: 12/20/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.
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Affiliation(s)
- Aastha Khatiwada
- Department of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado, United States of America
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
| | - Bethany J. Wolf
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, United States of America
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50
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Lemay MA, de Ronne M, Bélanger R, Belzile F. k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean. THE PLANT GENOME 2023; 16:e20374. [PMID: 37596724 DOI: 10.1002/tpg2.20374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/19/2023] [Indexed: 08/20/2023]
Abstract
Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.
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Affiliation(s)
- Marc-André Lemay
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
| | - Maxime de Ronne
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
| | - Richard Bélanger
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
| | - François Belzile
- Département de phytologie, Université Laval, Québec, QC, Canada
- Institut de biologie intégrative et des systèmes, Université Laval, Québec, QC, Canada
- Centre de recherche et d'innovation sur les végétaux, Université Laval, Québec, QC, Canada
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