Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
27
07
2023
accepted:
21
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe .
Identifiants
pubmed: 39478020
doi: 10.1038/s41467-024-53818-w
pii: 10.1038/s41467-024-53818-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9374Informations de copyright
© 2024. The Author(s).
Références
Bycroft, C. et al. The UK biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pubmed: 30305743
pmcid: 6786975
doi: 10.1038/s41586-018-0579-z
Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK biobank. Nat. Genet. 50, 1593–1599 (2018).
pubmed: 30349118
pmcid: 6707814
doi: 10.1038/s41588-018-0248-z
Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).
pubmed: 29892013
pmcid: 6309610
doi: 10.1038/s41588-018-0144-6
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
pubmed: 28686856
pmcid: 5501872
doi: 10.1016/j.ajhg.2017.06.005
Ritz, B. R. et al. Lessons learned from past gene-environment interaction successes. Am. J. Epidemiol. 186, 778–786 (2017).
pubmed: 28978190
pmcid: 5860326
doi: 10.1093/aje/kwx230
Garcia-Closas, M. et al. Nat2 slow acetylation, gstm1 null genotype, and risk of bladder cancer: results from the spanish bladder cancer study and meta-analyses. Lancet 366, 649–659 (2005).
pubmed: 16112301
pmcid: 1459966
doi: 10.1016/S0140-6736(05)67137-1
Thomas, D. Gene–environment-wide association studies: emerging approaches. Nat. Rev. Genet. 11, 259–272 (2010).
pubmed: 20212493
pmcid: 2891422
doi: 10.1038/nrg2764
Magi, R., Lindgren, C. M. & Morris, A. P. Meta-analysis of sex-specific genome-wide association studies. Genet. Epidemiol. 34, 846–853 (2010).
pubmed: 21104887
pmcid: 3410525
doi: 10.1002/gepi.20540
Benner, C. et al. Finemap: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
pubmed: 26773131
pmcid: 4866522
doi: 10.1093/bioinformatics/btw018
Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B: Stat. Methodol. 82, 1273–1300 (2020).
doi: 10.1111/rssb.12388
Zhang, W., Najafabadi, H. & Li, Y. Sparsepro: An efficient fine-mapping method integrating summary statistics and functional annotations. PLoS Genet. 19, e1011104 (2023).
pubmed: 38153934
pmcid: 10781022
doi: 10.1371/journal.pgen.1011104
Zhang, W. et al. Sharepro: an accurate and efficient genetic colocalization method accounting for multiple causal signals. Bioinformatics 40, btae295 (2024).
Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017).
doi: 10.1080/01621459.2017.1285773
Titsias, M. & Lazaro-Gredilla, M. Spike and slab variational inference for multi-task and multiple kernel learning. Adv. Neural Inf. Process. Syst. 24, 2339–2347 (2011).
Wang, H. et al. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the uk biobank. Sci. Adv. 5, eaaw3538 (2019).
pubmed: 31453325
pmcid: 6693916
doi: 10.1126/sciadv.aaw3538
Ware, J. J., van den Bree, M. & Munafò, M. R. From men to mice: Chrna5/chrna3, smoking behavior and disease. Nicotine Tob. Res. 14, 1291–1299 (2012).
pubmed: 22544838
pmcid: 3482013
doi: 10.1093/ntr/nts106
Kaur-Knudsen, D., Nordestgaard, B. G. & Bojesen, S. E. Chrna3 genotype, nicotine dependence, lung function and disease in the general population. Eur. Respiratory J. 40, 1538–1544 (2012).
doi: 10.1183/09031936.00176811
Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).
pubmed: 30643251
pmcid: 6358542
doi: 10.1038/s41588-018-0307-5
Xu, K. et al. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nat. Commun. 11, 5302 (2020).
pubmed: 33082346
pmcid: 7598939
doi: 10.1038/s41467-020-18489-3
Tree, D. R. et al. Prickle mediates feedback amplification to generate asymmetric planar cell polarity signaling. Cell 109, 371–381 (2002).
pubmed: 12015986
doi: 10.1016/S0092-8674(02)00715-8
Zallen, J. A. Planar polarity and tissue morphogenesis. Cell 129, 1051–1063 (2007).
pubmed: 17574020
doi: 10.1016/j.cell.2007.05.050
Aros, C. J., Pantoja, C. J. & Gomperts, B. N. Wnt signaling in lung development, regeneration, and disease progression. Commun. Biol. 4, 601 (2021).
pubmed: 34017045
pmcid: 8138018
doi: 10.1038/s42003-021-02118-w
Paulissen, G. et al. Role of adam and adamts metalloproteinases in airway diseases. Respiratory Res. 10, 1–12 (2009).
doi: 10.1186/1465-9921-10-127
Dreymueller, D., Uhlig, S. & Ludwig, A. Adam-family metalloproteinases in lung inflammation: potential therapeutic targets. Am. J. Physiol. -Lung Cell. Mol. Physiol. 308, L325–L343 (2015).
pubmed: 25480335
doi: 10.1152/ajplung.00294.2014
Glunk, V. et al. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat. Metab. 5, 861–879 (2023).
pubmed: 37253881
doi: 10.1038/s42255-023-00807-w
Loh, N. Y. et al. Rspo3 impacts body fat distribution and regulates adipose cell biology in vitro. Nat. Commun. 11, 2797 (2020).
pubmed: 32493999
pmcid: 7271210
doi: 10.1038/s41467-020-16592-z
Cox-York, K. A., Erickson, C. B., Pereira, R. I., Bessesen, D. H. & Van Pelt, R. E. Region-specific effects of oestradiol on adipose-derived stem cell differentiation in post-menopausal women. J. Cell. Mol. Med. 21, 677–684 (2017).
pubmed: 27862950
doi: 10.1111/jcmm.13011
Frank, A. P., de Souza Santos, R., Palmer, B. F. & Clegg, D. J. Determinants of body fat distribution in humans may provide insight about obesity-related health risks. J. Lipid Res. 60, 1710–1719 (2019).
pubmed: 30097511
doi: 10.1194/jlr.R086975
Brown, L. & Clegg, D. Central effects of estradiol in the regulation of food intake, body weight, and adiposity. J. Steroid Biochem. Mol. Biol. 122, 65–73 (2010).
pubmed: 20035866
doi: 10.1016/j.jsbmb.2009.12.005
Small, K. S. et al. Regulatory variants at klf14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition. Nat. Genet. 50, 572–580 (2018).
pubmed: 29632379
pmcid: 5935235
doi: 10.1038/s41588-018-0088-x
Yang, Q. et al. Adipocyte-specific modulation of klf14 expression in mice leads to sex-dependent impacts on adiposity and lipid metabolism. Diabetes 71, 677–693 (2022).
pubmed: 35081256
pmcid: 8965685
doi: 10.2337/db21-0674
Hansen, G. T. et al. Genetics of sexually dimorphic adipose distribution in humans. Nat. Genet. 55, 461–470 (2023).
pubmed: 36797366
doi: 10.1038/s41588-023-01306-0
Selby, C. Sex hormone binding globulin: origin, function and clinical significance. Ann. Clin. Biochem. 27, 532–541 (1990).
pubmed: 2080856
doi: 10.1177/000456329002700603
Qu, D. et al. A role for melanin-concentrating hormone in the central regulation of feeding behaviour. Nature 380, 243–247 (1996).
pubmed: 8637571
doi: 10.1038/380243a0
Al-Massadi, O. et al. Multifaceted actions of melanin-concentrating hormone on mammalian energy homeostasis. Nat. Rev. Endocrinol. 17, 745–755 (2021).
pubmed: 34608277
doi: 10.1038/s41574-021-00559-1
Kim, K. et al. Dairy food intake is associated with reproductive hormones and sporadic anovulation among healthy premenopausal women. J. Nutr. 147, 218–226 (2017).
pubmed: 27881593
doi: 10.3945/jn.116.241521
Pirastu, N. et al. Genetic analyses identify widespread sex-differential participation bias. Nat. Genet. 53, 663–671 (2021).
pubmed: 33888908
pmcid: 7611642
doi: 10.1038/s41588-021-00846-7
Kraft, P., Yen, Y.-C., Stram, D. O., Morrison, J. & Gauderman, W. J. Exploiting gene-environment interaction to detect genetic associations. Hum. Heredity 63, 111–119 (2007).
pubmed: 17283440
doi: 10.1159/000099183
Aschard, H. et al. Evidence for large-scale gene-by-smoking interaction effects on pulmonary function. Int. J. Epidemiol. 46, 894–904 (2017).
pubmed: 28082375
Eilat-Adar, S. et al. Dietary patterns and their association with cardiovascular risk factors in a population undergoing lifestyle changes: the strong heart study. Nutr., Metab. Cardiovascular Dis. 23, 528–535 (2013).
doi: 10.1016/j.numecd.2011.12.005
Ambrosini, G. L. et al. Dietary patterns and markers for the metabolic syndrome in australian adolescents. Nutr., Metab. Cardiovascular Dis. 20, 274–283 (2010).
doi: 10.1016/j.numecd.2009.03.024
Panagiotakos, D. B., Pitsavos, C., Skoumas, Y. & Stefanadis, C. The association between food patterns and the metabolic syndrome using principal components analysis: The attica study. J. Am. Dietetic Assoc. 107, 979–987 (2007).
doi: 10.1016/j.jada.2007.03.006
A reference panel of 64,976 haplotypes for genotype imputation. Nat. Geneti. 48, 1279–1283 (2016).
Purcell, S. et al. Plink: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
Westerman, K. E. et al. Gem: scalable and flexible gene–environment interaction analysis in millions of samples. Bioinformatics 37, 3514–3520 (2021).
pubmed: 34695175
pmcid: 8545347
doi: 10.1093/bioinformatics/btab223
Yang, J. et al. Conditional and joint multiple-snp analysis of gwas summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
pubmed: 22426310
pmcid: 3593158
doi: 10.1038/ng.2213
Organization, W. H. et al. Tobacco Or Health: A Global Status Report (World Health Organization, 1997).
Weissbrod, O. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52, 1355–1363 (2020).
pubmed: 33199916
pmcid: 7710571
doi: 10.1038/s41588-020-00735-5
Karolchik, D. et al. The UCSC genome browser database. Nucleic Acids Res. 31, 51–54 (2003).
pubmed: 12519945
pmcid: 165576
doi: 10.1093/nar/gkg129
Després, J.-P. Body fat distribution and risk of cardiovascular disease: an update. Circulation 126, 1301–1313 (2012).
pubmed: 22949540
doi: 10.1161/CIRCULATIONAHA.111.067264
Valenzuela, P. L. et al. Obesity and the risk of cardiometabolic diseases. Nat. Rev. Cardiol. 20, 475–494 (2023).
Neeland, I. J. et al. Body fat distribution and incident cardiovascular disease in obese adults. J. Am. Coll. Cardiol. 65, 2150–2151 (2015).
pubmed: 25975481
pmcid: 4890465
doi: 10.1016/j.jacc.2015.01.061
Ruth, K. S. et al. Using human genetics to understand the disease impacts of testosterone in men and women. Nat. Med. 26, 252–258 (2020).
pubmed: 32042192
pmcid: 7025895
doi: 10.1038/s41591-020-0751-5
Zhang, W. et al. Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro https://doi.org/10.6084/m9.figshare.25959295.v2 (2024).