JASPER: Fast, Powerful, Multitrait Association Testing in Structured Samples Gives Insight on Pleiotropy in Gene Expression

Overview

Journal bioRxiv

Date 2024 Jan 8

PMID 38187553

Authors

Joelle Mbatchou

Mary Sara McPeek

Affiliations

Soon will be listed here.

Abstract

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

References

Kennedy A, Ozbek U, Dorak M . What has GWAS done for HLA and disease associations?. Int J Immunogenet. 2017; 44(5):195-211. DOI: 10.1111/iji.12332. View

Han X, Gharahkhani P, Hamel A, Ong J, Renteria M, Mehta P . Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci. Nat Genet. 2023; 55(7):1116-1125. PMC: 10335935. DOI: 10.1038/s41588-023-01428-5. View

Dutta D, Scott L, Boehnke M, Lee S . Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes. Genet Epidemiol. 2018; 43(1):4-23. PMC: 6330125. DOI: 10.1002/gepi.22156. View

Jiang X, Zhang M, Zhang Y, Durvasula A, Inouye M, Holmes C . Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk. Nat Genet. 2023; 55(11):1854-1865. PMC: 10632146. DOI: 10.1038/s41588-023-01522-8. View

Ferreira M, McRae A, Medland S, Nyholt D, Gordon S, Wright M . Association between ORMDL3, IL1RL1 and a deletion on chromosome 17q21 with asthma risk in Australia. Eur J Hum Genet. 2010; 19(4):458-64. PMC: 3060316. DOI: 10.1038/ejhg.2010.191. View

Xu C, Ganesh S, Zhou X . mtPGS: Leverage multiple correlated traits for accurate polygenic score construction. Am J Hum Genet. 2023; 110(10):1673-1689. PMC: 10577082. DOI: 10.1016/j.ajhg.2023.08.016. View

Hua W, Ghosh D . Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies. Biometrics. 2015; 71(3):812-20. DOI: 10.1111/biom.12314. View

Chen J, Chen W, Zhao N, Wu M, Schaid D . Small Sample Kernel Association Tests for Human Genetic and Microbiome Association Studies. Genet Epidemiol. 2015; 40(1):5-19. PMC: 4679685. DOI: 10.1002/gepi.21934. View

Joo J, Kang E, Org E, Furlotte N, Parks B, Hormozdiari F . Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Genetics. 2016; 204(4):1379-1390. PMC: 5161272. DOI: 10.1534/genetics.116.189712. View

10.

Broadaway K, Cutler D, Duncan R, Moore J, Ware E, Jhun M . A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants. Am J Hum Genet. 2016; 98(3):525-540. PMC: 4800053. DOI: 10.1016/j.ajhg.2016.01.017. View

11.

Balding D, Nichols R . A method for quantifying differentiation between populations at multi-allelic loci and its implications for investigating identity and paternity. Genetica. 1995; 96(1-2):3-12. DOI: 10.1007/BF01441146. View

12.

Han X, Steven K, Qassim A, Marshall H, Bean C, Tremeer M . Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA. Am J Hum Genet. 2021; 108(7):1204-1216. PMC: 8322932. DOI: 10.1016/j.ajhg.2021.05.005. View

13.

Kim J, Zhang Y, Pan W . Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data. Genetics. 2016; 203(2):715-31. PMC: 4896189. DOI: 10.1534/genetics.115.186502. View

14.

Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S . The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics. 2016; 54:1.30.1-1.30.33. DOI: 10.1002/cpbi.5. View

15.

Chen H, Wang C, Conomos M, Stilp A, Li Z, Sofer T . Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models. Am J Hum Genet. 2016; 98(4):653-66. PMC: 4833218. DOI: 10.1016/j.ajhg.2016.02.012. View

16.

Liu H, Ling W, Hua X, Moon J, Williams-Nguyen J, Zhan X . Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity. Microbiome. 2023; 11(1):80. PMC: 10116795. DOI: 10.1186/s40168-023-01530-0. View

17.

Zhong S, Jiang D, McPeek M . CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates. PLoS Genet. 2016; 12(10):e1006329. PMC: 5047592. DOI: 10.1371/journal.pgen.1006329. View

18.

Stephens M . A unified framework for association analysis with multiple related phenotypes. PLoS One. 2013; 8(7):e65245. PMC: 3702528. DOI: 10.1371/journal.pone.0065245. View

19.

Feinleib M, Kannel W, Garrison R, MCNAMARA P, CASTELLI W . The Framingham Offspring Study. Design and preliminary data. Prev Med. 1975; 4(4):518-25. DOI: 10.1016/0091-7435(75)90037-7. View

20.

Lee S, Fuchsberger C, Kim S, Scott L . An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies. Biostatistics. 2015; 17(1):1-15. PMC: 4692986. DOI: 10.1093/biostatistics/kxv033. View