Population-specific Causal Disease Effect Sizes in Functionally Important Regions Impacted by Selection

Overview

Journal Nat Commun

Specialty Biology

Date 2021 Feb 18

PMID 33597505

Citations 68

Authors

Huwenbo Shi

Steven Gazal

Masahiro Kanai

Evan M Koch

Armin P Schoech

Katherine M Siewert

Samuel S Kim

Yang Luo

Tiffany Amariuta

Hailiang Huang

Yukinori Okada

Soumya Raychaudhuri

Shamil R Sunyaev

Alkes L Price

Affiliations

Soon will be listed here.

Abstract

Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

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References

Bose M, Olivan B, Laferrere B . Stress and obesity: the role of the hypothalamic-pituitary-adrenal axis in metabolic disease. Curr Opin Endocrinol Diabetes Obes. 2009; 16(5):340-6. PMC: 2858344. DOI: 10.1097/MED.0b013e32832fa137. View

Carlson C, Matise T, North K, Haiman C, Fesinmeyer M, Buyske S . Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study. PLoS Biol. 2013; 11(9):e1001661. PMC: 3775722. DOI: 10.1371/journal.pbio.1001661. View

Chang C, Chow C, Tellier L, Vattikuti S, Purcell S, Lee J . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4:7. PMC: 4342193. DOI: 10.1186/s13742-015-0047-8. View

. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012; 489(7414):57-74. PMC: 3439153. DOI: 10.1038/nature11247. View

Loh P, Tucker G, Bulik-Sullivan B, Vilhjalmsson B, Finucane H, Salem R . Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet. 2015; 47(3):284-90. PMC: 4342297. DOI: 10.1038/ng.3190. View

Itoh N, Ornitz D . Fibroblast growth factors: from molecular evolution to roles in development, metabolism and disease. J Biochem. 2010; 149(2):121-30. PMC: 3106964. DOI: 10.1093/jb/mvq121. View

Kichaev G, Pasaniuc B . Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies. Am J Hum Genet. 2015; 97(2):260-71. PMC: 4573268. DOI: 10.1016/j.ajhg.2015.06.007. View

Scott R, Scott L, Magi R, Marullo L, Gaulton K, Kaakinen M . An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. Diabetes. 2017; 66(11):2888-2902. PMC: 5652602. DOI: 10.2337/db16-1253. View

McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood A, Teumer A . A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016; 48(10):1279-83. PMC: 5388176. DOI: 10.1038/ng.3643. View

10.

Zhu Z, Bakshi A, Vinkhuyzen A, Hemani G, Lee S, Nolte I . Dominance genetic variation contributes little to the missing heritability for human complex traits. Am J Hum Genet. 2015; 96(3):377-85. PMC: 4375616. DOI: 10.1016/j.ajhg.2015.01.001. View

11.

Robinson M, English G, Moser G, Lloyd-Jones L, Triplett M, Zhu Z . Genotype-covariate interaction effects and the heritability of adult body mass index. Nat Genet. 2017; 49(8):1174-1181. DOI: 10.1038/ng.3912. View

12.

Vicennati V, Pasquali R . Abnormalities of the hypothalamic-pituitary-adrenal axis in nondepressed women with abdominal obesity and relations with insulin resistance: evidence for a central and a peripheral alteration. J Clin Endocrinol Metab. 2000; 85(11):4093-8. DOI: 10.1210/jcem.85.11.6946. View

13.

Durvasula A, Lohmueller K . Negative selection on complex traits limits phenotype prediction accuracy between populations. Am J Hum Genet. 2021; 108(4):620-631. PMC: 8059340. DOI: 10.1016/j.ajhg.2021.02.013. View

14.

Stahl E, Wegmann D, Trynka G, Gutierrez-Achury J, Do R, Voight B . Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet. 2012; 44(5):483-9. PMC: 6560362. DOI: 10.1038/ng.2232. View

15.

Davydov E, Goode D, Sirota M, Cooper G, Sidow A, Batzoglou S . Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol. 2010; 6(12):e1001025. PMC: 2996323. DOI: 10.1371/journal.pcbi.1001025. View

16.

Myers S, Bottolo L, Freeman C, McVean G, Donnelly P . A fine-scale map of recombination rates and hotspots across the human genome. Science. 2005; 310(5746):321-4. DOI: 10.1126/science.1117196. View

17.

Li J, Hong X, Mesiano S, Muglia L, Wang X, Snyder M . Natural Selection Has Differentiated the Progesterone Receptor among Human Populations. Am J Hum Genet. 2018; 103(1):45-57. PMC: 6035283. DOI: 10.1016/j.ajhg.2018.05.009. View

18.

Walter K, Min J, Huang J, Crooks L, Memari Y, McCarthy S . The UK10K project identifies rare variants in health and disease. Nature. 2015; 526(7571):82-90. PMC: 4773891. DOI: 10.1038/nature14962. View

19.

Gazal S, Loh P, Finucane H, Ganna A, Schoech A, Sunyaev S . Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations. Nat Genet. 2018; 50(11):1600-1607. PMC: 6236676. DOI: 10.1038/s41588-018-0231-8. View

20.

Schaid D, Chen W, Larson N . From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018; 19(8):491-504. PMC: 6050137. DOI: 10.1038/s41576-018-0016-z. View