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Pazokitoroudi A, Liu Z, Dahl A, Zaitlen N, Rosset S, Sankararaman S. A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits. Am J Hum Genet 2024; 111:1462-1480. [PMID: 38866020 DOI: 10.1016/j.ajhg.2024.05.015] [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: 09/20/2023] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/14/2024] Open
Abstract
Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.
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Affiliation(s)
- Ali Pazokitoroudi
- Department of Computer Science, UCLA, Los Angeles, CA, USA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Zhengtong Liu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Andrew Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Noah Zaitlen
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Saharon Rosset
- Department of Statistics, Tel-Aviv University, Tel-Aviv, Israel
| | - Sriram Sankararaman
- Department of Computer Science, UCLA, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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Leask MP, Crișan TO, Ji A, Matsuo H, Köttgen A, Merriman TR. The pathogenesis of gout: molecular insights from genetic, epigenomic and transcriptomic studies. Nat Rev Rheumatol 2024:10.1038/s41584-024-01137-1. [PMID: 38992217 DOI: 10.1038/s41584-024-01137-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
The pathogenesis of gout involves a series of steps beginning with hyperuricaemia, followed by the deposition of monosodium urate crystal in articular structures and culminating in an innate immune response, mediated by the NLRP3 inflammasome, to the deposited crystals. Large genome-wide association studies (GWAS) of serum urate levels initially identified the genetic variants with the strongest effects, mapping mainly to genes that encode urate transporters in the kidney and gut. Other GWAS highlighted the importance of uncommon genetic variants. More recently, genetic and epigenetic genome-wide studies have revealed new pathways in the inflammatory process of gout, including genetic associations with epigenomic modifiers. Epigenome-wide association studies are also implicating epigenomic remodelling in gout, which perhaps regulates the responsiveness of the innate immune system to monosodium urate crystals. Notably, genes implicated in gout GWAS do not include those encoding components of the NLRP3 inflammasome itself, but instead include genes encoding molecules involved in its regulation. Knowledge of the molecular mechanisms underlying gout has advanced through the translation of genetic associations into specific molecular mechanisms. Notable examples include ABCG2, HNF4A, PDZK1, MAF and IL37. Current genetic studies are dominated by participants of European ancestry; however, studies focusing on other population groups are discovering informative population-specific variants associated with gout.
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Affiliation(s)
- Megan P Leask
- Department of Physiology, University of Otago, Dunedin, Aotearoa, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tania O Crișan
- Department of Medical Genetics, "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Aichang Ji
- Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Tony R Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.
- Department of Microbiology and Immunology, University of Otago, Dunedin, Aotearoa, New Zealand.
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Frenkel M, Raman S. Discovering mechanisms of human genetic variation and controlling cell states at scale. Trends Genet 2024; 40:587-600. [PMID: 38658256 DOI: 10.1016/j.tig.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/26/2024]
Abstract
Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, USA; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA; Department of Bacteriology, University of Wisconsin, Madison, WI, USA; Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA.
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Chakrabarty A, Chakraborty S, Nandi D, Basu A. Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans. Proc Natl Acad Sci U S A 2024; 121:e2404364121. [PMID: 38833469 PMCID: PMC11181031 DOI: 10.1073/pnas.2404364121] [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: 03/01/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024] Open
Abstract
Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.
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Affiliation(s)
- Anasuya Chakrabarty
- Biotechnology Research Innovation Council-National Institute of Biomedical Genomics, Kalyani741251, West Bengal, India
| | - Saikat Chakraborty
- Biotechnology Research Innovation Council-National Institute of Biomedical Genomics, Kalyani741251, West Bengal, India
- Biostatistics Division, Global Capability Center, GlaxoSmithKline India Global Service Private Limited, Bangalore560037, India
| | - Diptarup Nandi
- Biotechnology Research Innovation Council-National Institute of Biomedical Genomics, Kalyani741251, West Bengal, India
- School of Arts and Sciences, Azim Premji University, Bengaluru562125, Karnataka, India
| | - Analabha Basu
- Biotechnology Research Innovation Council-National Institute of Biomedical Genomics, Kalyani741251, West Bengal, India
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Cusack SE, Maihofer AX, Bustamante D, Amstadter AB, Duncan LE. Genetic influences on testosterone and PTSD. J Psychiatr Res 2024; 174:8-11. [PMID: 38598976 PMCID: PMC11102285 DOI: 10.1016/j.jpsychires.2024.04.002] [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: 11/16/2023] [Revised: 02/25/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
Females are twice as likely to experience PTSD as compared to males. Although sex differences in prevalence are well-established, little is known about why such sex differences occur. Biological factors that vary with sex, including sex hormone production, may contribute to these differences. Considerable evidence links sex hormones, such as testosterone, to PTSD risk though less is known about the shared genetic underpinnings. The objective of the present study was to test for genetic relationships between testosterone and PTSD. To do so, we used summary statistics from large, publicly available genetic consortia to conduct linkage disequilibrium score regression to estimate the genetic correlations between PTSD and testosterone in males and females, and two-sample, bi-directional Mendelian randomization to examine potential causal relationships of testosterone on PTSD and the reverse. Heritability estimates of testosterone were significantly higher in males (0.17, SE = 0.02) than females (0.11, SE = 0.01; z = 2.46, p = 00.01). The correlation between testosterone and PTSD was negative in males (rg = -0.11, SE = 0.02, p = 6.7 x 10-6), but not significant in females (rg = 0.002, SE = 0.03, p = 0.95). MR analyses found no evidence of a causal effect of testosterone on PTSD or the reverse. Findings are consistent with phenotypic literature suggesting a relationship between testosterone and PTSD that may be sex-specific. This work provides early evidence of a relationship between testosterone and PTSD genotypically and suggests an avenue for future research that will enable a better understanding of disparities in PTSD.
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Affiliation(s)
- Shannon E Cusack
- Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, USA.
| | - Adam X Maihofer
- University of California San Diego, Department of Psychiatry, La Jolla, CA, USA; Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Daniel Bustamante
- Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, USA
| | - Ananda B Amstadter
- Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, USA
| | - Laramie E Duncan
- Stanford University, Department of Psychiatry and Behavioral Sciences, USA
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Weine E, Smith SP, Knowlton RK, Harpak A. Tradeoffs in Modeling Context Dependency in Complex Trait Genetics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.21.545998. [PMID: 38370664 PMCID: PMC10871201 DOI: 10.1101/2023.06.21.545998] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures or social settings. However, it is often unclear if the extent of context dependency, or Gene-by-Environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a tradeoff between bias and variance parameters. In particular, We derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, we argue that for complex traits, the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained "top hits" alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.
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Fishman R, Kralj-Fišer S, Marglit S, Koren L, Vortman Y. Fathers and sons, mothers and daughters: Sex-specific genetic architecture for fetal testosterone in a wild mammal. Horm Behav 2024; 161:105525. [PMID: 38452612 DOI: 10.1016/j.yhbeh.2024.105525] [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: 07/11/2023] [Revised: 01/13/2024] [Accepted: 02/29/2024] [Indexed: 03/09/2024]
Abstract
Testosterone plays a critical role in mediating fitness-related traits in many species. Although it is highly responsive to environmental and social conditions, evidence from several species show a heritable component to its individual variation. Despite the known effects that in utero testosterone exposure have on adult fitness, the heritable component of individual testosterone variation in fetuses is mostly unexplored. Furthermore, testosterone has sex-differential effects on fetal development, i.e., a specific level may be beneficial for male fetuses but detrimental for females, producing sexual conflict. Such sexual conflict may be resolved by the evolution of a sex-specific genetic architecture of the trait. Here, we quantified fetal testosterone levels in a wild species, free-ranging nutrias (Myocastor coypus) using hair-testing and estimated testosterone heritability between parent and offspring from the same and opposite sex. We found that in utero accumulated hair testosterone levels were heritable between parents and offspring of the same sex. Moreover, there was a low additive genetic covariance between the sexes, and a low cross-sex genetic correlation, suggesting a potential for sex-specific trait evolution, expressed early on, in utero.
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Affiliation(s)
- Ruth Fishman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel(1); The Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel.
| | - Simona Kralj-Fišer
- Scientific and Research Centre of the Slovenian Academy of Sciences and Arts, Jovan Hadži Institute of Biology, Evolutionary Zoology Laboratory, Ljubljana, Slovenia.
| | - Sivan Marglit
- Hula Research Center, Department of Animal Sciences, Tel-Hai College, Upper Galilee 1220800, Israel
| | - Lee Koren
- The Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel.
| | - Yoni Vortman
- Hula Research Center, Department of Animal Sciences, Tel-Hai College, Upper Galilee 1220800, Israel; MIGAL-Galilee Research Institute, 11016 Kiryat Shmona, Israel
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Ponomarenko I, Pasenov K, Churnosova M, Sorokina I, Aristova I, Churnosov V, Ponomarenko M, Reshetnikova Y, Reshetnikov E, Churnosov M. Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer. Biomedicines 2024; 12:818. [PMID: 38672173 PMCID: PMC11048332 DOI: 10.3390/biomedicines12040818] [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: 03/05/2024] [Revised: 03/30/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence of obesity, were divided into two groups: obese (119 BC; 253 controls) and non-obese (239 BC; 887 controls). Genotyping of nine SHBG-associated single nucleotide polymorphisms (SNP)-rs17496332 PRMT6, rs780093 GCKR, rs10454142 PPP1R21, rs3779195 BAIAP2L1, rs440837 ZBTB10, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs8023580 NR2F2, and rs12150660 SHBG-was executed, and the BC-risk impact of these loci was analyzed by logistic regression separately in each group of obese/non-obese women. We found that the BC-risk effect correlated by GWAS with the SHBG-level polymorphism rs10454142 PPP1R21 depends on the presence/absence of obesity. The SHBG-lowering allele C rs10454142 PPP1R21 has a risk value for BC in obese women (allelic model: CvsT, OR = 1.52, 95%CI = 1.10-2.11, and pperm = 0.013; additive model: CCvsTCvsTT, OR = 1.71, 95%CI = 1.15-2.62, and pperm = 0.011; dominant model: CC + TCvsTT, OR = 1.95, 95%CI = 1.13-3.37, and pperm = 0.017) and is not associated with the disease in women without obesity. SNP rs10454142 PPP1R21 and 10 proxy SNPs have adipose-specific regulatory effects (epigenetic modifications of promoters/enhancers, DNA interaction with 51 transcription factors, eQTL/sQTL effects on five genes (PPP1R21, RP11-460M2.1, GTF2A1L, STON1-GTF2A1L, and STON1), etc.), can be "likely cancer driver" SNPs, and are involved in cancer-significant pathways. In conclusion, our study detected an obesity-dependent association of the rs10454142 PPP1R21 with BC in women.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia; (I.P.); (K.P.); (M.C.); (I.S.); (I.A.); (V.C.); (M.P.); (Y.R.); (E.R.)
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Lin B, Paterson AD, Sun L. Better together against genetic heterogeneity: A sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank. PLoS Genet 2024; 20:e1011221. [PMID: 38656964 PMCID: PMC11073786 DOI: 10.1371/journal.pgen.1011221] [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: 12/05/2023] [Revised: 05/06/2024] [Accepted: 03/11/2024] [Indexed: 04/26/2024] Open
Abstract
Genetic effects can be sex-specific, particularly for traits such as testosterone, a sex hormone. While sex-stratified analysis provides easily interpretable sex-specific effect size estimates, the presence of sex-differences in SNP effect implies a SNP×sex interaction. This suggests the usage of the often overlooked joint test, testing for an SNP's main and SNP×sex interaction effects simultaneously. Notably, even without individual-level data, the joint test statistic can be derived from sex-stratified summary statistics through an omnibus meta-analysis. Utilizing the available sex-stratified summary statistics of the UK Biobank, we performed such omnibus meta-analyses for 290 quantitative traits. Results revealed that this approach is robust to genetic effect heterogeneity and can outperform the traditional sex-stratified or sex-combined main effect-only tests. Therefore, we advocate using the omnibus meta-analysis that captures both the main and interaction effects. Subsequent sex-stratified analysis should be conducted for sex-specific effect size estimation and interpretation.
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Affiliation(s)
- Boxi Lin
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Andrew D. Paterson
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lei Sun
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
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Waller C, Ho A, Batzler A, Geske J, Karpyak V, Biernacka J, Winham S. Genetic correlations of alcohol consumption and alcohol use disorder with sex hormone levels in females and males. RESEARCH SQUARE 2024:rs.3.rs-3944066. [PMID: 38464231 PMCID: PMC10925434 DOI: 10.21203/rs.3.rs-3944066/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and these complex traits are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is associated with alcohol consumption and alcohol use disorder, as well as levels of steroid sex hormones and their binding proteins. Methods To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin. Results For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with total testosterone in males (rg = 0.084, p = 0.007) and trends toward positive genetic correlation with bioavailable testosterone (rg = 0.060, p = 0.084) and SHBG in males (rg = 0.056, p = 0.086) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015); however in females, we observed positive genetic correlation with SHBG (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032). For alcohol dependence, we observed a trend toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Several of these genetic correlations differed between females and males and were not in the same direction as the corresponding phenotypic associations. Conclusions Findings suggest that shared genetic effects may contribute to positive associations of alcohol consumption with albumin in both sexes, as well as positive associations between alcohol consumption and bioavailable testosterone and between alcohol dependence and SHBG in males. However, relative contributions of heritable and environmental factors to associations between alcohol-use traits and sex-hormone levels may differ by sex, with genetic factors contributing more in males and environmental factors contributing more in females.
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Ponomarenko I, Pasenov K, Churnosova M, Sorokina I, Aristova I, Churnosov V, Ponomarenko M, Reshetnikov E, Churnosov M. Sex-Hormone-Binding Globulin Gene Polymorphisms and Breast Cancer Risk in Caucasian Women of Russia. Int J Mol Sci 2024; 25:2182. [PMID: 38396861 PMCID: PMC10888713 DOI: 10.3390/ijms25042182] [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: 01/21/2024] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
In our work, the associations of GWAS (genome-wide associative studies) impact for sex-hormone-binding globulin (SHBG)-level SNPs with the risk of breast cancer (BC) in the cohort of Caucasian women of Russia were assessed. The work was performed on a sample of 1498 women (358 BC patients and 1140 control (non BC) subjects). SHBG correlated in previously GWAS nine polymorphisms such as rs780093 GCKR, rs17496332 PRMT6, rs3779195 BAIAP2L1, rs10454142 PPP1R21, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs440837 ZBTB10, rs12150660 SHBG, and rs8023580 NR2F2 have been genotyped. BC risk effects of allelic and non-allelic SHBG-linked gene SNPs interactions were detected by regression analysis. The risk genetic factor for BC developing is an SHBG-lowering allele variant C rs10454142 PPP1R21 ([additive genetic model] OR = 1.31; 95%CI = 1.08-1.65; pperm = 0.024; power = 85.26%), which determines 0.32% of the cancer variance. Eight of the nine studied SHBG-related SNPs have been involved in cancer susceptibility as part of nine different non-allelic gene interaction models, the greatest contribution to which is made by rs10454142 PPP1R21 (included in all nine models, 100%) and four more SNPs-rs7910927 JMJD1C (five models, 55.56%), rs17496332 PRMT6 (four models, 44.44%), rs780093 GCKR (four models, 44.44%), and rs440837 ZBTB10 (four models, 44.44%). For SHBG-related loci, pronounced functionality in the organism (including breast, liver, fibroblasts, etc.) was predicted in silico, having a direct relationship through many pathways with cancer pathophysiology. In conclusion, our results demonstrated the involvement of SHBG-correlated genes polymorphisms in BC risk in Caucasian women in Russia.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia; (I.P.); (K.P.); (M.C.); (I.S.); (I.A.); (V.C.); (M.P.); (E.R.)
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12
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Kim D, Song J, Mancuso N, Mangul S, Jung J, Jang W. Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis. Arthritis Res Ther 2024; 26:47. [PMID: 38336809 PMCID: PMC10858498 DOI: 10.1186/s13075-024-03280-2] [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: 09/29/2023] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. METHODS To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk. RESULTS We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks. CONCLUSION Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.
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Affiliation(s)
- Daeun Kim
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea
| | - Jaeseung Song
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Serghei Mangul
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
| | - Junghyun Jung
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA.
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
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13
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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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14
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Matkarimov BT, Saparbaev MK. Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible. PeerJ 2023; 11:e16671. [PMID: 38107580 PMCID: PMC10725672 DOI: 10.7717/peerj.16671] [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: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023] Open
Abstract
Background Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits. Methods Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes. Results The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions. Conclusions According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.
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Affiliation(s)
- Bakhyt T. Matkarimov
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- L.N.Gumilev Eurasian National University, Astana, Kazakhstan
| | - Murat K. Saparbaev
- Groupe «Mechanisms of DNA Repair and Carcinogenesis», CNRS UMR9019, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Al-Farabi Kazakh National University, Almaty, Kazakhstan
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15
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McGrath IM, Montgomery GW, Mortlock S. Polygenic risk score phenome-wide association study reveals an association between endometriosis and testosterone. BMC Med 2023; 21:482. [PMID: 38049874 PMCID: PMC10696845 DOI: 10.1186/s12916-023-03184-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Endometriosis affects 1 in 9 women, yet it is poorly understood with long diagnostic delays, invasive diagnoses, and poor treatment outcomes. Characterised by the presence of endometrial-like tissue outside of the uterus, its main symptoms are pain and infertility. Endometriosis often co-occurs with other conditions, which may provide insights into the origins of endometriosis. METHODS Here a polygenic risk score phenome-wide association study of endometriosis was conducted in the UK Biobank to investigate the pleiotropic effects of a genetic liability to endometriosis. The relationship between the polygenic risk score for endometriosis and health conditions, blood and urine biomarkers and reproductive factors were investigated separately in females, males and females without an endometriosis diagnosis. The relationship between endometriosis and the blood and urine biomarkers was further investigated using genetic correlation and Mendelian randomisation approaches to identify causal relationships. RESULTS Multiple health conditions, blood and urine biomarkers and reproductive factors were associated with genetic liability to endometriosis in each group, indicating many endometriosis comorbidities are not dependent on the physical manifestation of endometriosis. Differences in the associated traits between males and females highlighted the importance of sex-specific pathways in the overlap of endometriosis with many other traits. Notably, an association of genetic liability to endometriosis with lower testosterone levels was identified. Follow-up analysis utilising Mendelian randomisation approaches suggested lower testosterone may be causal for both endometriosis and clear cell ovarian cancer. CONCLUSIONS This study highlights the diversity of the pleiotropic effects of genetic risk to endometriosis irrespective of a diagnosis of endometriosis. A key finding was the identification of a causal effect of the genetic liability to lower testosterone on endometriosis using Mendelian randomisation.
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Affiliation(s)
- Isabelle M McGrath
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Grant W Montgomery
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Sally Mortlock
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
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16
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Ratajczak F, Joblin M, Hildebrandt M, Ringsquandl M, Falter-Braun P, Heinig M. Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases. Nat Commun 2023; 14:7206. [PMID: 37938585 PMCID: PMC10632370 DOI: 10.1038/s41467-023-42975-z] [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: 03/27/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.
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Affiliation(s)
- Florin Ratajczak
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Munich, Neuherberg, Germany
| | | | | | | | - Pascal Falter-Braun
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Munich, Neuherberg, Germany.
- Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
| | - Matthias Heinig
- Institute of Computational Biology (ICB), Helmholtz Munich, Neuherberg, Germany.
- Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- German Centre for Cardiovascular Research (DZHK), Munich Heart Association, Partner Site Munich, Berlin, Germany.
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17
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Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [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: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
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Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
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18
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Bhati M, Mapel XM, Lloret-Villas A, Pausch H. Structural variants and short tandem repeats impact gene expression and splicing in bovine testis tissue. Genetics 2023; 225:iyad161. [PMID: 37655920 PMCID: PMC10627265 DOI: 10.1093/genetics/iyad161] [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: 06/05/2023] [Revised: 06/05/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023] Open
Abstract
Structural variants (SVs) and short tandem repeats (STRs) are significant sources of genetic variation. However, the impacts of these variants on gene regulation have not been investigated in cattle. Here, we genotyped and characterized 19,408 SVs and 374,821 STRs in 183 bovine genomes and investigated their impact on molecular phenotypes derived from testis transcriptomes. We found that 71% STRs were multiallelic. The vast majority (95%) of STRs and SVs were in intergenic and intronic regions. Only 37% SVs and 40% STRs were in high linkage disequilibrium (LD) (R2 > 0.8) with surrounding SNPs/insertions and deletions (Indels), indicating that SNP-based association testing and genomic prediction are blind to a nonnegligible portion of genetic variation. We showed that both SVs and STRs were more than 2-fold enriched among expression and splicing QTL (e/sQTL) relative to SNPs/Indels and were often associated with differential expression and splicing of multiple genes. Deletions and duplications had larger impacts on splicing and expression than any other type of SV. Exonic duplications predominantly increased gene expression either through alternative splicing or other mechanisms, whereas expression- and splicing-associated STRs primarily resided in intronic regions and exhibited bimodal effects on the molecular phenotypes investigated. Most e/sQTL resided within 100 kb of the affected genes or splicing junctions. We pinpoint candidate causal STRs and SVs associated with the expression of SLC13A4 and TTC7B and alternative splicing of a lncRNA and CAPP1. We provide a catalog of STRs and SVs for taurine cattle and show that these variants contribute substantially to gene expression and splicing variation.
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Affiliation(s)
- Meenu Bhati
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | | | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
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19
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Szakats S, McAtamney A, Cross H, Wilson MJ. Sex-biased gene and microRNA expression in the developing mouse brain is associated with neurodevelopmental functions and neurological phenotypes. Biol Sex Differ 2023; 14:57. [PMID: 37679839 PMCID: PMC10486049 DOI: 10.1186/s13293-023-00538-3] [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: 09/30/2022] [Accepted: 08/18/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Sex differences pose a challenge and an opportunity in biomedical research. Understanding how sex chromosomes and hormones affect disease-causing mechanisms will shed light on the mechanisms underlying predominantly idiopathic sex-biased neurodevelopmental disorders such as ADHD, schizophrenia, and autism. Gene expression is a crucial conduit for the influence of sex on developmental processes; therefore, this study focused on sex differences in gene expression and the regulation of gene expression. The increasing interest in microRNAs (miRNAs), small, non-coding RNAs, for their contribution to normal and pathological neurodevelopment prompted us to test how miRNA expression differs between the sexes in the developing brain. METHODS High-throughput sequencing approaches were used to identify transcripts, including miRNAs, that showed significantly different expression between male and female brains on day 15.5 of development (E15.5). RESULTS Robust sex differences were identified for some genes and miRNAs, confirming the influence of biological sex on RNA. Many miRNAs that exhibit the greatest differences between males and females have established roles in neurodevelopment, implying that sex-biased expression may drive sex differences in developmental processes. In addition to highlighting sex differences for individual miRNAs, gene ontology analysis suggested several broad categories in which sex-biased RNAs might act to establish sex differences in the embryonic mouse brain. Finally, mining publicly available SNP data indicated that some sex-biased miRNAs reside near the genomic regions associated with neurodevelopmental disorders. CONCLUSIONS Together, these findings reinforce the importance of cataloguing sex differences in molecular biology research and highlight genes, miRNAs, and pathways of interest that may be important for sexual differentiation in the mouse and possibly the human brain.
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Affiliation(s)
- Susanna Szakats
- Developmental Genomics Laboratory, Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, 9054, New Zealand
| | - Alice McAtamney
- Developmental Genomics Laboratory, Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, 9054, New Zealand
| | - Hugh Cross
- Developmental Genomics Laboratory, Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, 9054, New Zealand
| | - Megan J Wilson
- Developmental Genomics Laboratory, Department of Anatomy, School of Biomedical Sciences, University of Otago, P.O. Box 56, Dunedin, 9054, New Zealand.
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20
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Brown AA, Fernandez-Tajes JJ, Hong MG, Brorsson CA, Koivula RW, Davtian D, Dupuis T, Sartori A, Michalettou TD, Forgie IM, Adam J, Allin KH, Caiazzo R, Cederberg H, De Masi F, Elders PJM, Giordano GN, Haid M, Hansen T, Hansen TH, Hattersley AT, Heggie AJ, Howald C, Jones AG, Kokkola T, Laakso M, Mahajan A, Mari A, McDonald TJ, McEvoy D, Mourby M, Musholt PB, Nilsson B, Pattou F, Penet D, Raverdy V, Ridderstråle M, Romano L, Rutters F, Sharma S, Teare H, 't Hart L, Tsirigos KD, Vangipurapu J, Vestergaard H, Brunak S, Franks PW, Frost G, Grallert H, Jablonka B, McCarthy MI, Pavo I, Pedersen O, Ruetten H, Walker M, Adamski J, Schwenk JM, Pearson ER, Dermitzakis ET, Viñuela A. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits. Nat Commun 2023; 14:5062. [PMID: 37604891 PMCID: PMC10442420 DOI: 10.1038/s41467-023-40569-3] [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: 05/12/2021] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.
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Affiliation(s)
- Andrew A Brown
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Juan J Fernandez-Tajes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Caroline A Brorsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert W Koivula
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, United Kingdom
| | - David Davtian
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Théo Dupuis
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Ambra Sartori
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Theodora-Dafni Michalettou
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom
| | - Ian M Forgie
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Jonathan Adam
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Kristine H Allin
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert Caiazzo
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Henna Cederberg
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Petra J M Elders
- Department of General Practice, Amsterdam UMC- location Vumc, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Giuseppe N Giordano
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mark Haid
- Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Torben Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Tue H Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Alison J Heggie
- Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cédric Howald
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Tarja Kokkola
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, 35127, Italy
| | - Timothy J McDonald
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, United Kingdom
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Miranda Mourby
- Nuffield Department of Population Health, Centre for Health, Law and Emerging Technologies (HeLEX), University of Oxford, Oxford, OX2 7DD, United Kingdom
| | - Petra B Musholt
- Global Development, Sanofi-Aventis Deutschland GmbH, Hoechst Industrial Park, Frankfurt am Main, 65926, Germany
| | - Birgitte Nilsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Francois Pattou
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Deborah Penet
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Violeta Raverdy
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | | | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Femke Rutters
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
| | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Food Chemistry and Molecular and Sensory Science, Technical University of Munich, München, Germany
| | - Harriet Teare
- Centre for Health Law and Emerging Technologies, Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom
| | - Leen 't Hart
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology section, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Jagadish Vangipurapu
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
- Steno Diabetes Center Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Paul W Franks
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Gary Frost
- Nutrition and Dietetics Research Group, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Bernd Jablonka
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- GENENTECH, 1 DNA Way, San Francisco, CA, 94080, USA
| | - Imre Pavo
- Eli Lilly Regional Operations Ges.m.b.H, Vienna, 1030, Austria
| | - Oluf Pedersen
- Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Hartmut Ruetten
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, United Kingdom
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Experimental Genetics, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Ewan R Pearson
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland.
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland.
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland.
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom.
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21
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Bajpai VK, Swigut T, Mohammed J, Naqvi S, Arreola M, Tycko J, Kim TC, Pritchard JK, Bassik MC, Wysocka J. A genome-wide genetic screen uncovers determinants of human pigmentation. Science 2023; 381:eade6289. [PMID: 37561850 PMCID: PMC10901463 DOI: 10.1126/science.ade6289] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 06/28/2023] [Indexed: 08/12/2023]
Abstract
Skin color, one of the most diverse human traits, is determined by the quantity, type, and distribution of melanin. In this study, we leveraged the light-scattering properties of melanin to conduct a genome-wide screen for regulators of melanogenesis. We identified 169 functionally diverse genes that converge on melanosome biogenesis, endosomal transport, and gene regulation, of which 135 represented previously unknown associations with pigmentation. In agreement with their melanin-promoting function, the majority of screen hits were up-regulated in melanocytes from darkly pigmented individuals. We further unraveled functions of KLF6 as a transcription factor that regulates melanosome maturation and pigmentation in vivo, and of the endosomal trafficking protein COMMD3 in modulating melanosomal pH. Our study reveals a plethora of melanin-promoting genes, with broad implications for human variation, cell biology, and medicine.
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Affiliation(s)
- Vivek K. Bajpai
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- School of Chemical, Biological, and Materials Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Tomek Swigut
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jaaved Mohammed
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Martin Arreola
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Josh Tycko
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Tayne C. Kim
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jonathan K. Pritchard
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Michael C. Bassik
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Chemistry, Engineering, and Medicine for Human Health (ChEM-H), Stanford University, Stanford, CA 94305, USA
- Program in Cancer Biology, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
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22
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Vosberg DE, Pausova Z, Paus T. The genetics of a "femaleness/maleness" score in cardiometabolic traits in the UK biobank. Sci Rep 2023; 13:9109. [PMID: 37277458 DOI: 10.1038/s41598-023-36132-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 05/30/2023] [Indexed: 06/07/2023] Open
Abstract
We recently devised continuous "sex-scores" that sum up multiple quantitative traits, weighted by their respective sex-difference effect sizes, as an approach to estimating polyphenotypic "maleness/femaleness" within each binary sex. To identify the genetic architecture underlying these sex-scores, we conducted sex-specific genome-wide association studies (GWASs) in the UK Biobank cohort (females: n = 161,906; males: n = 141,980). As a control, we also conducted GWASs of sex-specific "sum-scores", simply aggregating the same traits, without weighting by sex differences. Among GWAS-identified genes, while sum-score genes were enriched for genes differentially expressed in the liver in both sexes, sex-score genes were enriched for genes differentially expressed in the cervix and across brain tissues, particularly for females. We then considered single nucleotide polymorphisms with significantly different effects (sdSNPs) between the sexes for sex-scores and sum-scores, mapping to male-dominant and female-dominant genes. Here, we identified brain-related enrichment for sex-scores, especially for male-dominant genes; these findings were present but weaker for sum-scores. Genetic correlation analyses of sex-biased diseases indicated that both sex-scores and sum-scores were associated with cardiometabolic, immune, and psychiatric disorders.
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Affiliation(s)
- Daniel E Vosberg
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
- Research Institute of the Hospital for Sick Children, Toronto, ON, Canada
| | - Zdenka Pausova
- Research Institute of the Hospital for Sick Children, Toronto, ON, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Tomáš Paus
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada.
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada.
- ECOGENE-21, Chicoutimi, QC, Canada.
- Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
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23
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Zhu C, Ming MJ, Cole JM, Edge MD, Kirkpatrick M, Harpak A. Amplification is the primary mode of gene-by-sex interaction in complex human traits. CELL GENOMICS 2023; 3:100297. [PMID: 37228747 PMCID: PMC10203050 DOI: 10.1016/j.xgen.2023.100297] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 12/15/2022] [Accepted: 03/13/2023] [Indexed: 05/27/2023]
Abstract
Sex differences in complex traits are suspected to be in part due to widespread gene-by-sex interactions (GxSex), but empirical evidence has been elusive. Here, we infer the mixture of ways in which polygenic effects on physiological traits covary between males and females. We find that GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects ("amplification") rather than in the identity of causal variants. Amplification patterns account for sex differences in trait variance. In some cases, testosterone may mediate amplification. Finally, we develop a population-genetic test linking GxSex to contemporary natural selection and find evidence of sexually antagonistic selection on variants affecting testosterone levels. Our results suggest that amplification of polygenic effects is a common mode of GxSex that may contribute to sex differences and fuel their evolution.
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Affiliation(s)
- Carrie Zhu
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Matthew J. Ming
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Jared M. Cole
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Michael D. Edge
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Mark Kirkpatrick
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Arbel Harpak
- Department of Population Health, The University of Texas at Austin, Austin, TX, USA
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
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24
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Ivanova T, Churnosova M, Abramova M, Plotnikov D, Ponomarenko I, Reshetnikov E, Aristova I, Sorokina I, Churnosov M. Sex-Specific Features of the Correlation between GWAS-Noticeable Polymorphisms and Hypertension in Europeans of Russia. Int J Mol Sci 2023; 24:ijms24097799. [PMID: 37175507 PMCID: PMC10178435 DOI: 10.3390/ijms24097799] [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: 03/25/2023] [Revised: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of the study was directed at studying the sex-specific features of the correlation between genome-wide association studies (GWAS)-noticeable polymorphisms and hypertension (HTN). In two groups of European subjects of Russia (n = 1405 in total), such as men (n = 821 in total: n = 564 HTN, n = 257 control) and women (n = 584 in total: n = 375 HTN, n = 209 control), the distribution of ten specially selected polymorphisms (they have confirmed associations of GWAS level with blood pressure (BP) parameters and/or HTN in Europeans) has been considered. The list of studied loci was as follows: (PLCE1) rs932764 A > G, (AC026703.1) rs1173771 G > A, (CERS5) rs7302981 G > A, (HFE) rs1799945 C > G, (OBFC1) rs4387287 C > A, (BAG6) rs805303 G > A, (RGL3) rs167479 T > G, (ARHGAP42) rs633185 C > G, (TBX2) rs8068318 T > C, and (ATP2B1) rs2681472 A > G. The contribution of individual loci and their inter-locus interactions to the HTN susceptibility with bioinformatic interpretation of associative links was evaluated separately in men's and women's cohorts. The men-women differences in involvement in the disease of the BP/HTN-associated GWAS SNPs were detected. Among women, the HTN risk has been associated with HFE rs1799945 C > G (genotype GG was risky; ORGG = 11.15 ppermGG = 0.014) and inter-locus interactions of all 10 examined SNPs as part of 26 intergenic interactions models. In men, the polymorphism BAG6 rs805303 G > A (genotype AA was protective; ORAA = 0.30 ppermAA = 0.0008) and inter-SNPs interactions of eight loci in only seven models have been founded as HTN-correlated. HTN-linked loci and strongly linked SNPs were characterized by pronounced polyvector functionality in both men and women, but at the same time, signaling pathways of HTN-linked genes/SNPs in women and men were similar and were represented mainly by immune mechanisms. As a result, the present study has demonstrated a more pronounced contribution of BP/HTN-associated GWAS SNPs to the HTN susceptibility (due to weightier intergenic interactions) in European women than in men.
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Affiliation(s)
- Tatiana Ivanova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Abramova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Denis Plotnikov
- Genetic Epidemiology Lab, Kazan State Medical University, 420012 Kazan, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
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25
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Solé-Navais P, Flatley C, Steinthorsdottir V, Vaudel M, Juodakis J, Chen J, Laisk T, LaBella AL, Westergaard D, Bacelis J, Brumpton B, Skotte L, Borges MC, Helgeland Ø, Mahajan A, Wielscher M, Lin F, Briggs C, Wang CA, Moen GH, Beaumont RN, Bradfield JP, Abraham A, Thorleifsson G, Gabrielsen ME, Ostrowski SR, Modzelewska D, Nohr EA, Hypponen E, Srivastava A, Talbot O, Allard C, Williams SM, Menon R, Shields BM, Sveinbjornsson G, Xu H, Melbye M, Lowe W, Bouchard L, Oken E, Pedersen OB, Gudbjartsson DF, Erikstrup C, Sørensen E, Lie RT, Teramo K, Hallman M, Juliusdottir T, Hakonarson H, Ullum H, Hattersley AT, Sletner L, Merialdi M, Rifas-Shiman SL, Steingrimsdottir T, Scholtens D, Power C, West J, Nyegaard M, Capra JA, Skogholt AH, Magnus P, Andreassen OA, Thorsteinsdottir U, Grant SFA, Qvigstad E, Pennell CE, Hivert MF, Hayes GM, Jarvelin MR, McCarthy MI, Lawlor DA, Nielsen HS, Mägi R, Rokas A, Hveem K, Stefansson K, Feenstra B, Njolstad P, Muglia LJ, Freathy RM, Johansson S, Zhang G, Jacobsson B. Genetic effects on the timing of parturition and links to fetal birth weight. Nat Genet 2023; 55:559-567. [PMID: 37012456 PMCID: PMC10101852 DOI: 10.1038/s41588-023-01343-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 02/22/2023] [Indexed: 04/05/2023]
Abstract
The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal-fetal relationship between gestational duration and birth weight.
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Affiliation(s)
- Pol Solé-Navais
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden.
| | - Christopher Flatley
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | | | - Marc Vaudel
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Julius Juodakis
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Jing Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Abigail L LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Methods and Analysis, Statistics Denmark, Copenhagen, Denmark
| | - Jonas Bacelis
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Line Skotte
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Maria C Borges
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Øyvind Helgeland
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Matthias Wielscher
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Frederick Lin
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Catherine Briggs
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
| | - Gunn-Helen Moen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Australia
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Abin Abraham
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Dominika Modzelewska
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Ellen A Nohr
- Research Unit of Gynecology and Obstetrics, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Elina Hypponen
- Australian Centre for Precision Health, Uni Clinical & Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Amit Srivastava
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Octavious Talbot
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Catherine Allard
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CHUS), Sherbrooke, Québec, Canada
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Ramkumar Menon
- Department of Obstetrics and Gynaecology, University of Texas Medical Branch, Galveston, TX, USA
| | - Beverley M Shields
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Huan Xu
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mads Melbye
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - William Lowe
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Clinical Department of Laboratory Medicine, Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay-Lac-St-Jean - Hôpital Universitaire de Chicoutimi, Saguenay, Québec, Canada
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health, University of Aarhus, Aarhus, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Rolv T Lie
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Kari Teramo
- Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Mikko Hallman
- PEDEGO Research Unit and Medical Research Center Oulu, University of Oulu, Oulu, Finland
- Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | | | - Hakon Hakonarson
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Andrew T Hattersley
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Line Sletner
- Department of Pediatric and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Mario Merialdi
- Maternal Newborn Health Innovations, PBC, Geneva, Switzerland
| | - Sheryl L Rifas-Shiman
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Thora Steingrimsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Department of Obstetrics and Gynecology, Landspitali - The National University Hospital of Iceland, Reykjavik, Iceland
| | - Denise Scholtens
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Christine Power
- Population, Policy, Practice. Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Jane West
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Mette Nyegaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - John A Capra
- Bakar Computational Health Sciences Institute and Department of Epidemiology and Statistics, University of California San Francisco, San Francisco, CA, USA
| | - Anne H Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- NORMENT Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Struan F A Grant
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Spatial and Functional Genomics Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elisabeth Qvigstad
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Geoffrey M Hayes
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter of Oulu, University of Oulu, Linnanmaa, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, UK
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, Bristol, UK
| | - Henriette S Nielsen
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- The Recurrent Pregnancy Loss Unit, The Capital Region, Copenhagen University Hospitals Rigshospitalet & Hvidovre Hospital, Hvidovre, Denmark
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Kari Stefansson
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Pål Njolstad
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Louis J Muglia
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Rachel M Freathy
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Stefan Johansson
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Ge Zhang
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Human Genetics, Center for the Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Bo Jacobsson
- Department of Obstetrics and Gynaecology, Sahlgrenska Academy, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden.
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway.
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Nag A, Dhindsa RS, Middleton L, Jiang X, Vitsios D, Wigmore E, Allman EL, Reznichenko A, Carss K, Smith KR, Wang Q, Challis B, Paul DS, Harper AR, Petrovski S. Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank. Am J Hum Genet 2023; 110:487-498. [PMID: 36809768 PMCID: PMC10027475 DOI: 10.1016/j.ajhg.2023.02.002] [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: 11/21/2022] [Accepted: 01/30/2023] [Indexed: 02/22/2023] Open
Abstract
Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.
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Affiliation(s)
- Abhishek Nag
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ryan S Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - Lawrence Middleton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Xiao Jiang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Eleanor Wigmore
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Erik L Allman
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Anna Reznichenko
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA
| | - Benjamin Challis
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andrew R Harper
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Department of Medicine, University of Melbourne, Austin Health, Melbourne, VIC, Australia.
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Genetic Risk, Adherence to a Healthy Lifestyle, and Hyperuricemia: The TCLSIH Cohort Study. Am J Med 2023; 136:476-483.e5. [PMID: 36708795 DOI: 10.1016/j.amjmed.2023.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/03/2023] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
BACKGROUND Genetic factors have been associated with hyperuricemia in large studies, but the extent to which this can be offset by a healthy lifestyle is unknown. This study aimed to examine whether healthy lifestyle could reduce hyperuricemia risk among individuals with different genetic profiles. METHODS We defined a lifestyle score using body mass index, smoking, alcohol consumption, physical activities, and diets in 2796 unrelated individuals from the Tianjin Chronic Low-grade Systemic Inflammation and Health (TCLSIH) cohort study. Polygenic risk scores (PRS) were constructed based on uric acid loci. Associations of combined lifestyle factors and genetic risk and incident hyperuricemia were estimated using Cox proportional hazard regression. RESULTS Of 2796 individuals, 747 participants (26.7%) developed hyperuricemia. Genetic risk and lifestyle were predictors of incident events, and they showed an interaction for the outcome. Compared with high PRS, low PRS reduced risk of incident hyperuricemia by 40%, and compared with unhealthy lifestyle, healthy lifestyle reduced risk of incident hyperuricemia by 41%. Compared with unhealthy lifestyle and high genetic risk, adherence to healthy lifestyle was associated with a 68% (95% confidence interval, 44%-81%) lower risk of hyperuricemia among those at a low genetic risk. CONCLUSIONS In this prospective cohort study, we observed an interaction between genetics and lifestyle and the risk of hyperuricemia. The public health implication is that a healthy lifestyle is important for hyperuricemia prevention, especially for individuals with high genetic risk scores.
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Genetic analyses implicate complex links between adult testosterone levels and health and disease. COMMUNICATIONS MEDICINE 2023; 3:4. [PMID: 36653534 PMCID: PMC9849476 DOI: 10.1038/s43856-022-00226-0] [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: 09/17/2021] [Accepted: 12/07/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes. METHODS Leveraging genetic and health registry data from the UK Biobank and FinnGen (total N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality. RESULTS We show testosterone and SHBG levels are intricately tied to metabolic health, but report lack of causality behind most associations, including type 2 diabetes (T2D). Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes. CONCLUSIONS Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences.
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Okamoto J, Wang L, Yin X, Luca F, Pique-Regi R, Helms A, Im HK, Morrison J, Wen X. Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits. Am J Hum Genet 2023; 110:44-57. [PMID: 36608684 PMCID: PMC9892769 DOI: 10.1016/j.ajhg.2022.12.002] [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: 09/01/2022] [Accepted: 12/06/2022] [Indexed: 01/07/2023] Open
Abstract
Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.
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Affiliation(s)
- Jeffrey Okamoto
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Lijia Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Adam Helms
- University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
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30
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Song J, Zou Y, Wu Y, Miao J, Yu Z, Fletcher JM, Lu Q. Decomposing heritability and genetic covariance by direct and indirect effect paths. PLoS Genet 2023; 19:e1010620. [PMID: 36689559 PMCID: PMC9894552 DOI: 10.1371/journal.pgen.1010620] [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: 09/16/2022] [Revised: 02/02/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.
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Affiliation(s)
- Jie Song
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Yiqing Zou
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Ze Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Jason M. Fletcher
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Sociology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Qiongshi Lu
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail:
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31
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Abstract
We organized this special issue to highlight new work and review recent advances at the cutting edge of 'wild quantitative genomics'. In this editorial, we will present some history of wild quantitative genetic and genomic studies, before discussing the main themes in the papers published in this special issue and highlighting the future outlook of this dynamic field.
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Affiliation(s)
- Susan E Johnston
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, Edinburgh EH9 3FL, UK
| | - Nancy Chen
- Department of Biology, University of Rochester, Rochester, 14627, NY, USA
| | - Emily B Josephs
- Department of Plant Biology and Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, 48824, MI, USA
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32
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Zhang Z, Kryvokhyzha D, Orsucci M, Glémin S, Milesi P, Lascoux M. How broad is the selfing syndrome? Insights from convergent evolution of gene expression across species and tissues in the Capsella genus. THE NEW PHYTOLOGIST 2022; 236:2344-2357. [PMID: 36089898 PMCID: PMC9828073 DOI: 10.1111/nph.18477] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/01/2022] [Indexed: 06/15/2023]
Abstract
The shift from outcrossing to selfing is one of the main evolutionary transitions in plants. It is accompanied by profound effects on reproductive traits, the so-called selfing syndrome. Because the transition to selfing also implies deep genomic and ecological changes, one also expects to observe a genomic selfing syndrome. We took advantage of the three independent transitions from outcrossing to selfing in the Capsella genus to characterize the overall impact of mating system change on RNA expression, in flowers but also in leaves and roots. We quantified the extent of both selfing and genomic syndromes, and tested whether changes in expression corresponded to adaptation to selfing or to relaxed selection on traits that were constrained in outcrossers. Mating system change affected gene expression in all three tissues but more so in flowers than in roots and leaves. Gene expression in selfing species tended to converge in flowers but diverged in the two other tissues. Hence, convergent adaptation to selfing dominates in flowers, whereas genetic drift plays a more important role in leaves and roots. The effect of mating system transition is not limited to reproductive tissues and corresponds to both adaptation to selfing and relaxed selection on previously constrained traits.
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Affiliation(s)
- Zebin Zhang
- Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology CentreUppsala UniversityNorbyvägen 18D752 36UppsalaSweden
| | - Dmytro Kryvokhyzha
- Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology CentreUppsala UniversityNorbyvägen 18D752 36UppsalaSweden
- Department of Clinical SciencesLund University Diabetes Centre214 28MalmöSweden
| | - Marion Orsucci
- Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology CentreUppsala UniversityNorbyvägen 18D752 36UppsalaSweden
- Department of Plant BiologySwedish University of Agricultural Sciences, Uppsala BioCenter750 07UppsalaSweden
| | - Sylvain Glémin
- Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology CentreUppsala UniversityNorbyvägen 18D752 36UppsalaSweden
- Université de Rennes, Centre National de la Recherche Scientifique (CNRS), ECOBIO (Ecosystèmes, Biodiversité, Evolution) – Unité Mixte de Recherche (UMR) 6553F‐35042RennesFrance
| | - Pascal Milesi
- Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology CentreUppsala UniversityNorbyvägen 18D752 36UppsalaSweden
- Science For Life Laboratory (SciLifeLab)752 37UppsalaSweden
| | - Martin Lascoux
- Program in Plant Ecology and Evolution, Department of Ecology and Genetics, Evolutionary Biology CentreUppsala UniversityNorbyvägen 18D752 36UppsalaSweden
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Multi-omics peripheral and core regions of cancer. NPJ Syst Biol Appl 2022; 8:47. [PMID: 36446819 PMCID: PMC9707100 DOI: 10.1038/s41540-022-00258-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 11/07/2022] [Indexed: 11/30/2022] Open
Abstract
Thousands of genes are perturbed by cancer, and these disturbances can be seen in transcriptome, methylation, somatic mutation, and copy number variation omics studies. Understanding their connectivity patterns as an omnigenic neighbourhood in a molecular interaction network (interactome) is a key step towards advancing knowledge of the molecular mechanisms underlying cancers. Here, we introduce a unified connectivity line (CLine) to pinpoint omics-specific omnigenic patterns across 15 curated cancers. Taking advantage of the universality of CLine, we distinguish the peripheral and core genes for each omics aspect. We propose a network-based framework, multi-omics periphery and core (MOPC), to combine peripheral and core genes from different omics into a button-like structure. On the basis of network proximity, we provide evidence that core genes tend to be specifically perturbed in one omics, but the peripheral genes are diversely perturbed in multiple omics. And the core of one omics is regulated by multiple omics peripheries. Finally, we take the MOPC as an omnigenic neighbourhood, describe its characteristics, and explore its relative contribution to network-based mechanisms of cancer. We were able to present how multi-omics perturbations percolate through the human interactome and contribute to an integrated periphery and core.
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Golovchenko I, Aizikovich B, Golovchenko O, Reshetnikov E, Churnosova M, Aristova I, Ponomarenko I, Churnosov M. Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis. Int J Mol Sci 2022; 23:13691. [PMID: 36430184 PMCID: PMC9697627 DOI: 10.3390/ijms232213691] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/07/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022] Open
Abstract
The present study was designed to examine whether sex hormone polymorphisms proven by GWAS are associated with endometriosis risk. Unrelated female participants totaling 1376 in number (395 endometriosis patients and 981 controls) were recruited into the study. Nine single-nucleotide polymorphisms (SNPs) which GWAS correlated with circulating levels of sex hormones were genotyped using a TaqMan allelic discrimination assay. FSH-lowering, and LH- and testosterone-heightening polymorphisms of the FSHB promoter (allelic variants A rs11031002 and C rs11031005) exhibit a protective effect for endometriosis (OR = 0.60-0.68). By contrast, the TT haplotype loci that were GWAS correlated with higher FSH levels and lower LH and testosterone concentrations determined an increased risk for endometriosis (OR = 2.03). Endometriosis-involved epistatic interactions were found between eight loci of sex hormone genes (without rs148982377 ZNF789) within twelve genetic simulation models. In silico examination established that 8 disorder-related loci and 80 proxy SNPs are genome variants affecting the expression, splicing, epigenetic and amino acid conformation of the 34 genes which enrich the organic anion transport and secondary carrier transporter pathways. In conclusion, the present study showed that sex hormone polymorphisms proven by GWAS are associated with endometriosis risk and involved in the molecular pathophysiology of the disease due to their functionality.
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Affiliation(s)
- Ilya Golovchenko
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Boris Aizikovich
- Department of Fundamental Medicine, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Oleg Golovchenko
- Department of Obstetrics and Gynecology, Belgorod State University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
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35
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Partanen JJ, Häppölä P, Zhou W, Lehisto AA, Ainola M, Sutinen E, Allen RJ, Stockwell AD, Leavy OC, Oldham JM, Guillen-Guio B, Cox NJ, Hirbo JB, Schwartz DA, Fingerlin TE, Flores C, Noth I, Yaspan BL, Jenkins RG, Wain LV, Ripatti S, Pirinen M, Laitinen T, Kaarteenaho R, Myllärniemi M, Daly MJ, Koskela JT. Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics. CELL GENOMICS 2022; 2:100181. [PMID: 36777997 PMCID: PMC9903787 DOI: 10.1016/j.xgen.2022.100181] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/24/2022] [Accepted: 09/07/2022] [Indexed: 04/12/2023]
Abstract
The research of rare and devastating orphan diseases, such as idiopathic pulmonary fibrosis (IPF) has been limited by the rarity of the disease itself. The prognosis is poor-the prevalence of IPF is only approximately four times the incidence, limiting the recruitment of patients to trials and studies of the underlying biology. Global biobanking efforts can dramatically alter the future of IPF research. We describe a large-scale meta-analysis of IPF, with 8,492 patients and 1,355,819 population controls from 13 biobanks around the globe. Finally, we combine this meta-analysis with the largest available meta-analysis of IPF, reaching 11,160 patients and 1,364,410 population controls. We identify seven novel genome-wide significant loci, only one of which would have been identified if the analysis had been limited to European ancestry individuals. We observe notable pleiotropy across IPF susceptibility and severe COVID-19 infection and note an unexplained sex-heterogeneity effect at the strongest IPF locus MUC5B.
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Affiliation(s)
- Juulia J. Partanen
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Paavo Häppölä
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arto A. Lehisto
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Mari Ainola
- Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland
| | - Eva Sutinen
- Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland
| | - Richard J. Allen
- Department of Health Sciences, University of Leicester, Leicester, UK
| | | | - Olivia C. Leavy
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Justin M. Oldham
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of California, Davis, Sacramento, CA, USA
| | | | - Nancy J. Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jibril B. Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Tasha E. Fingerlin
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - Carlos Flores
- Research Unit, Hospital Universitario Ntra. Sra. de Candelaria, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain
- Faculty of Health Sciences, University of Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
| | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - R. Gisli Jenkins
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Samuli Ripatti
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - International IPF Genetics Consortium
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland
- Department of Health Sciences, University of Leicester, Leicester, UK
- Human Genetics, Genentech, South San Francisco, CA, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of California, Davis, Sacramento, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, University of Colorado, Aurora, CO, USA
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
- Research Unit, Hospital Universitario Ntra. Sra. de Candelaria, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain
- Faculty of Health Sciences, University of Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, VA, USA
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Global Biobank Meta-Analysis Initiative (GBMI)
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland
- Department of Health Sciences, University of Leicester, Leicester, UK
- Human Genetics, Genentech, South San Francisco, CA, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of California, Davis, Sacramento, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetic Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, University of Colorado, Aurora, CO, USA
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
- Research Unit, Hospital Universitario Ntra. Sra. de Candelaria, Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain
- Faculty of Health Sciences, University of Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, VA, USA
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Tarja Laitinen
- Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Riitta Kaarteenaho
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Marjukka Myllärniemi
- Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland
| | - Mark J. Daly
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jukka T. Koskela
- Institute for Molecular Medicine, Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
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Liao Z, Vosberg DE, Pausova Z, Paus T. A Shifting Relationship Between Sex Hormone-Binding Globulin and Total Testosterone Across Puberty in Boys. J Clin Endocrinol Metab 2022; 107:e4187-e4196. [PMID: 35965384 PMCID: PMC9516180 DOI: 10.1210/clinem/dgac484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Indexed: 11/19/2022]
Abstract
CONTEXT Sex hormone-binding globulin (SHBG) is associated with levels of total testosterone (total-T), and both total-T and SHBG are associated with obesity. OBJECTIVE We aimed to clarify the nature of the relationship between testosterone and SHBG and improve our understanding of their relationships with obesity. We hypothesize that the hypothalamic-pituitary-gonadal axis contributes to the homeostasis of testosterone by increasing the production of gonadal testosterone through a feedback mechanism that might operate differently at different pubertal stages. METHODS We investigated the dynamics of the relationship between SHBG, total-T, and body mass index (BMI) throughout puberty (from age 9 to 17) using longitudinal data obtained in 507 males. The directionality of this relationship was explored using polygenic scores of SHBG and total-T, and a two-sample Mendelian Randomization (MR) in male adults. RESULTS Consistent with our hypothesis, we found positive relationships between SHBG and total-T at age 15 and 17 but either no relationship or a negative relationship during the earlier time points. Such shifting relationships explained age-related changes in the association between total-T and BMI. Polygenic scores of SHBG and total-T in mediation analyses and the two-sample MR in male adults suggested an effect of SHBG on total-T but also a somewhat weaker effect of total-T on SHBG. Two-sample MR also showed an effect of BMI on SHBG but no effect of SHBG on BMI. CONCLUSION These results clarify the nature of the relationship between testosterone and SHBG during puberty and adulthood and shed new light on their possible relationship with obesity.
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Affiliation(s)
- Zhijie Liao
- Department of Psychology, University of Toronto, Toronto, Ontario, M5S 3G3, Canada
| | - Daniel E Vosberg
- Centre Hospitalier Universitaire (CHU) Sainte-Justine, Montreal, Quebec, H3T 1C5, Canada
- Departments of Psychiatry and Neuroscience, University of Montreal, Montreal, Quebec, H3T 1J4, Canada
| | - Zdenka Pausova
- Research Institute of the Hospital for Sick Children, Toronto, Ontario, M5G 0A4, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
| | - Tomas Paus
- Department of Psychology, University of Toronto, Toronto, Ontario, M5S 3G3, Canada
- Centre Hospitalier Universitaire (CHU) Sainte-Justine, Montreal, Quebec, H3T 1C5, Canada
- Departments of Psychiatry and Neuroscience, University of Montreal, Montreal, Quebec, H3T 1J4, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, M5T 1R8, Canada
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Smith CJ, Sinnott-Armstrong N, Cichońska A, Julkunen H, Fauman EB, Würtz P, Pritchard JK. Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation. eLife 2022; 11:e79348. [PMID: 36073519 PMCID: PMC9536840 DOI: 10.7554/elife.79348] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/06/2022] [Indexed: 11/15/2022] Open
Abstract
Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.
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Affiliation(s)
- Courtney J Smith
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
| | - Nasa Sinnott-Armstrong
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
- Herbold Computational Biology Program, Fred Hutchinson Cancer Research CenterSeattleUnited States
| | | | | | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and MedicalCambridgeUnited States
| | | | - Jonathan K Pritchard
- Department of Genetics, Stanford University School of MedicineStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
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Blum JA, Gitler AD. Singling out motor neurons in the age of single-cell transcriptomics. Trends Genet 2022; 38:904-919. [PMID: 35487823 PMCID: PMC9378604 DOI: 10.1016/j.tig.2022.03.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 01/07/2023]
Abstract
Motor neurons are a remarkably powerful cell type in the central nervous system. They innervate and control the contraction of virtually every muscle in the body and their dysfunction underlies numerous neuromuscular diseases. Some motor neurons seem resistant to degeneration whereas others are vulnerable. The intrinsic heterogeneity of motor neurons in adult organisms has remained elusive. The development of high-throughput single-cell transcriptomics has changed the paradigm, empowering rapid isolation and profiling of motor neuron nuclei, revealing remarkable transcriptional diversity within the skeletal and autonomic nervous systems. Here, we discuss emerging technologies for defining motor neuron heterogeneity in the adult motor system as well as implications for disease and spinal cord injury. We establish a roadmap for future applications of emerging techniques - such as epigenetic profiling, spatial RNA sequencing, and single-cell somatic mutational profiling to adult motor neurons, which will revolutionize our understanding of the healthy and degenerating adult motor system.
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Affiliation(s)
- Jacob A Blum
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Neurosciences Interdepartmental Program, Stanford University School of Medicine, Stanford, CA, USA.
| | - Aaron D Gitler
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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Watanabe K, Jansen PR, Savage JE, Nandakumar P, Wang X, Hinds DA, Gelernter J, Levey DF, Polimanti R, Stein MB, Van Someren EJW, Smit AB, Posthuma D. Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. Nat Genet 2022; 54:1125-1132. [PMID: 35835914 DOI: 10.1038/s41588-022-01124-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 06/06/2022] [Indexed: 12/20/2022]
Abstract
Insomnia is a heritable, highly prevalent sleep disorder for which no sufficient treatment currently exists. Previous genome-wide association studies with up to 1.3 million subjects identified over 200 associated loci. This extreme polygenicity suggested that many more loci remain to be discovered. The current study almost doubled the sample size to 593,724 cases and 1,771,286 controls, thereby increasing statistical power, and identified 554 risk loci (including 364 novel loci). To capitalize on this large number of loci, we propose a novel strategy to prioritize genes using external biological resources and functional interactions between genes across risk loci. Of all 3,898 genes naively implicated from the risk loci, we prioritize 289 and find brain-tissue expression specificity and enrichment in specific gene sets of synaptic signaling functions and neuronal differentiation. We show that this novel gene prioritization strategy yields specific hypotheses on underlying mechanisms of insomnia that would have been missed by traditional approaches.
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Affiliation(s)
- Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands
| | - Philip R Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands
- Department of Human Genetics, Section Clinical Genetics, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands
| | | | - Xin Wang
- 23andMe, Inc., Sunnyvale, CA, USA
| | | | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Eus J W Van Someren
- Departments of Integrative Neurophysiology and Psychiatry InGeest, Amsterdam Neuroscience, VU University and Medical Center, Amsterdam, the Netherlands
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam, the Netherlands.
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam University Medical Centers, Amsterdam, the Netherlands.
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de la Fuente J, Grotzinger AD, Marioni RE, Nivard MG, Tucker-Drob EM. Integrated analysis of direct and proxy genome wide association studies highlights polygenicity of Alzheimer's disease outside of the APOE region. PLoS Genet 2022; 18:e1010208. [PMID: 35658006 PMCID: PMC9200312 DOI: 10.1371/journal.pgen.1010208] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 06/15/2022] [Accepted: 04/19/2022] [Indexed: 11/19/2022] Open
Abstract
Recent meta-analyses combining direct genome-wide association studies (GWAS) with those of family history (GWAX) have indicated very low SNP heritability of Alzheimer's disease (AD). These low estimates may call into question the prospects of continued progress in genetic discovery for AD within the spectrum of common variants. We highlight dramatic downward biases in previous methods, and we validate a novel method for the estimation of SNP heritability via integration of GWAS and GWAX summary data. We apply our method to investigate the genetic architecture of AD using GWAX from UK Biobank and direct case-control GWAS from the International Genomics of Alzheimer's Project (IGAP). We estimate the liability scale common variant SNP heritability of Clinical AD outside of APOE region at ~7-11%, and we project the corresponding estimate for AD pathology to be up to approximately 23%. We estimate that nearly 90% of common variant SNP heritability of Clinical AD exists outside the APOE region. Rare variants not tagged in standard GWAS may account for additional variance. Our results indicate that, while GWAX for AD in UK Biobank may result in greater attenuation of genetic effects beyond that conventionally assumed, it does not introduce appreciable contamination of signal by genetically distinct traits relative to direct case-control GWAS in IGAP. Genetic risk for AD represents a strong effect of APOE superimposed upon a highly polygenic background.
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Affiliation(s)
- Javier de la Fuente
- Department of Psychology, University of Texas at Austin, Texas, United States of America
- Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Texas, United States of America
- * E-mail: (JF); (EMT-D)
| | - Andrew D. Grotzinger
- Department of Psychology, University of Texas at Austin, Texas, United States of America
- Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, United Kingdom
| | - Michel G. Nivard
- Department of Biological Psychology, VU University Amsterdam, the Netherlands
| | - Elliot M. Tucker-Drob
- Department of Psychology, University of Texas at Austin, Texas, United States of America
- Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Texas, United States of America
- * E-mail: (JF); (EMT-D)
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Smith SP, Shahamatdar S, Cheng W, Zhang S, Paik J, Graff M, Haiman C, Matise TC, North KE, Peters U, Kenny E, Gignoux C, Wojcik G, Crawford L, Ramachandran S. Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries. Am J Hum Genet 2022; 109:871-884. [PMID: 35349783 PMCID: PMC9118115 DOI: 10.1016/j.ajhg.2022.03.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/02/2022] [Indexed: 12/12/2022] Open
Abstract
Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.
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Affiliation(s)
- Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Sahar Shahamatdar
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Wei Cheng
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Selena Zhang
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Joseph Paik
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
| | - Misa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christopher Haiman
- Department of Preventative Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - T C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eimear Kenny
- The Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO 80204, USA
| | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Biostatistics, Brown University, Providence, RI 02906, USA; Microsoft Research New England, Cambridge, MA 02142, USA
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA; Department of Ecology, Evolution, and Organismal Biology, Brown University, Providence, RI 02912, USA; Data Science Initiative, Brown University, Providence, RI 02912, USA.
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Crespi B, Procyshyn T, Mokkonen M. Natura Non Facit Saltus: The Adaptive Significance of Arginine Vasopressin in Human Affect, Cognition, and Behavior. Front Behav Neurosci 2022; 16:814230. [PMID: 35586834 PMCID: PMC9108674 DOI: 10.3389/fnbeh.2022.814230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/30/2022] [Indexed: 11/30/2022] Open
Abstract
Hormones coordinate internal bodily systems with cognition, affect, and behavior, and thereby influence aspects of social interactions including cooperation, competition, isolation, and loneliness. The adaptive significance and contextuality of oxytocin (OXT) and testosterone (T) have been well-studied, but a unified theory and evolutionary framework for understanding the adaptive functions of arginine vasopressin (AVP) remain undeveloped. We propose and evaluate the hypothesis that AVP mediates adaptive variation in the presence and strength of social and sociosexual salience, attention and behavior specifically in situations that involve combinations of cooperation with conflict or competition. This hypothesis can help to explain the ancestral, original functions of AVP-like peptides, and their continuity with the current roles of AVP, for humans, in male-male competition, male-male reciprocity, male-to-female pair bonding, female-female interactions, social integration, and social attention and anxiety. In this context, social isolation and loneliness may be mediated by reduced abilities or interests in navigation of social opportunities and situations, due in part to low AVP levels or reactivity, and in part to reductions in levels of OXT-mediated social reward.
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Wang YC, Wu Y, Choi J, Allington G, Zhao S, Khanfar M, Yang K, Fu PY, Wrubel M, Yu X, Mekbib KY, Ocken J, Smith H, Shohfi J, Kahle KT, Lu Q, Jin SC. Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy. J Pers Med 2022; 12:175. [PMID: 35207663 PMCID: PMC8878256 DOI: 10.3390/jpm12020175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/18/2022] [Accepted: 01/24/2022] [Indexed: 02/04/2023] Open
Abstract
Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.
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Affiliation(s)
- Yung-Chun Wang
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Yuchang Wu
- Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Julie Choi
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Garrett Allington
- Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA;
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; (H.S.); (K.T.K.)
| | - Shujuan Zhao
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Mariam Khanfar
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Kuangying Yang
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Po-Ying Fu
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Max Wrubel
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
| | - Xiaobing Yu
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
- Department of Computer Science & Engineering, Washington University, St. Louis, MO 63130, USA
| | - Kedous Y. Mekbib
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - Jack Ocken
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - Hannah Smith
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; (H.S.); (K.T.K.)
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - John Shohfi
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA; (K.Y.M.); (J.O.); (J.S.)
| | - Kristopher T. Kahle
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; (H.S.); (K.T.K.)
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA 02115, USA
- Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Qiongshi Lu
- Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA;
| | - Sheng Chih Jin
- Department of Genetics, School of Medicine, Washington University, St. Louis, MO 63110, USA; (Y.-C.W.); (J.C.); (S.Z.); (M.K.); (K.Y.); (P.-Y.F.); (M.W.); (X.Y.)
- Department of Pediatrics, School of Medicine, Washington University, St. Louis, MO 63110, USA
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45
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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46
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Ritchie SC, Lambert SA, Arnold M, Teo SM, Lim S, Scepanovic P, Marten J, Zahid S, Chaffin M, Liu Y, Abraham G, Ouwehand WH, Roberts DJ, Watkins NA, Drew BG, Calkin AC, Di Angelantonio E, Soranzo N, Burgess S, Chapman M, Kathiresan S, Khera AV, Danesh J, Butterworth AS, Inouye M. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat Metab 2021; 3:1476-1483. [PMID: 34750571 PMCID: PMC8574944 DOI: 10.1038/s42255-021-00478-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/14/2021] [Indexed: 01/13/2023]
Abstract
Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.
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Affiliation(s)
- Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Matthew Arnold
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Shu Mei Teo
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Sol Lim
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Petar Scepanovic
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Jonathan Marten
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sohail Zahid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mark Chaffin
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yingying Liu
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Gad Abraham
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Willem H Ouwehand
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - David J Roberts
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford and John Radcliffe Hospital, Oxford, UK
| | - Nicholas A Watkins
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
| | - Brian G Drew
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Anna C Calkin
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Centre for Health Data Science, Human Technopole, Milan, Italy
| | - Nicole Soranzo
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Michael Chapman
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | | | - Amit V Khera
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia.
- The Alan Turing Institute, London, UK.
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47
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Soergel H, Loosli F, Muhle-Goll C. Strain-Specific Liver Metabolite Profiles in Medaka. Metabolites 2021; 11:metabo11110744. [PMID: 34822402 PMCID: PMC8617739 DOI: 10.3390/metabo11110744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022] Open
Abstract
The relationship between genetic variation and phenotypic traits is often poorly understood since specific genotypes do not always easily translate into associated phenotypes, especially for complex disorders. The genetic background has been shown to affect metabolic pathways and thus contribute to variations in the metabolome. Here, we tested the suitability of NMR metabolomics for comparative analysis of fish lines as a first step towards phenotype-genotype association studies. The Japanese rice fish, medaka (Oryzias latipes), is a widely used genetic vertebrate model with several isogenic inbred laboratory strains. We used liver extracts of medaka iCab and HO5 strains as a paradigm to test the feasibility of distinguishing the metabolome of two different inbred strains. Fifteen metabolites could be detected in uni- and multivariate analyses that showed strain-specific levels. Differences could be assigned to specific metabolic pathways. Our results show that NMR spectroscopy is a suitable method to detect variance of the metabolome caused by subtle genetic differences. Thus, it has the potential to address genotype–phenotype associations in medaka, providing an additional level of phenotypic analysis.
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Affiliation(s)
- Hannah Soergel
- Institute of Organic Chemistry, Karlsruhe Institute of Technology, Fritz-Haber-Weg 6, 76131 Karlsruhe, Germany;
| | - Felix Loosli
- Institute of Biological and Chemical Systems, Biological Information Processing (IBCS-BIP), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Correspondence: (F.L.); (C.M.-G.); Tel.: +49-72160828743 (F.L.); +49-72160829357 (C.M.-G.)
| | - Claudia Muhle-Goll
- Institute for Biological Interfaces 4 (IBG 4), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Correspondence: (F.L.); (C.M.-G.); Tel.: +49-72160828743 (F.L.); +49-72160829357 (C.M.-G.)
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48
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Visscher PM, Yengo L, Cox NJ, Wray NR. Discovery and implications of polygenicity of common diseases. Science 2021; 373:1468-1473. [PMID: 34554790 PMCID: PMC9945947 DOI: 10.1126/science.abi8206] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines individual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in individuals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.
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Affiliation(s)
- Peter M. Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia,Corresponding author.
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia
| | - Nancy J. Cox
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia,Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
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49
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Abstract
[Figure: see text].
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Affiliation(s)
- Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,New York Genome Center, New York, NY, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, and UNSW Sydney, Sydney, New South Wales, Australia.,Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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50
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Vosberg DE, Parker N, Shin J, Pausova Z, Paus T. The genetics of testosterone contributes to "femaleness/maleness" of cardiometabolic traits and type 2 diabetes. Int J Obes (Lond) 2021; 46:235-237. [PMID: 34480103 DOI: 10.1038/s41366-021-00960-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/25/2021] [Indexed: 11/09/2022]
Abstract
The genetic architecture of testosterone is highly distinct between sexes. Moreover, obesity is associated with higher testosterone in females but lower testosterone in males. Here, we ask whether male-specific testosterone variants are associated with a male pattern of obesity and type 2 diabetes (T2D) in females, and vice versa. In the UK Biobank, we conducted sex-specific genome-wide association studies and computed polygenic scores for total (PGSTT) and bioavailable testosterone (PGSBT). We tested sex-congruent and sex-incongruent associations between sex-specific PGSTs and metabolic traits, as well as T2D diagnosis. Female-specific PGSBT was associated with an elevated cardiometabolic risk and probability of T2D, in both sexes. Male-specific PGSTT was associated with traits conferring a lower cardiometabolic risk and probability of T2D, in both sexes. We demonstrate the value in considering polygenic testosterone as sex-related continuous traits, in each sex.
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Affiliation(s)
- Daniel E Vosberg
- Research Institute of the Hospital for Sick Children, Toronto, Ontario, Canada.,Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
| | - Nadine Parker
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Jean Shin
- Research Institute of the Hospital for Sick Children, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Zdenka Pausova
- Research Institute of the Hospital for Sick Children, Toronto, Ontario, Canada.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.,Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada.,ECOGENE-21, Chicoutimi, Quebec, Canada
| | - Tomáš Paus
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada. .,ECOGENE-21, Chicoutimi, Quebec, Canada. .,Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada. .,Faculty of Medicine, Departments of Psychiatry and Neuroscience, University of Montreal, Montreal, Quebec, Canada.
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