Local Genetic Correlation Via Knockoffs Reduces Confounding Due to Cross-trait Assortative Mating

Overview

Journal Am J Hum Genet

Publisher Cell Press

Specialty Genetics

Date 2024 Nov 15

PMID 39547235

Authors

Shiyang Ma

Fan Wang

Richard Border

Joseph Buxbaum

Noah Zaitlen

Iuliana Ionita-Laza

Affiliations

Soon will be listed here.

Abstract

Local genetic correlation analysis is an important tool for identifying genetic loci with shared biology across traits. Recently, Border et al. have shown that the results of these analyses are confounded by cross-trait assortative mating (xAM), leading to many false-positive findings. Here, we describe LAVA-Knock, a local genetic correlation method that builds off an existing genetic correlation method, LAVA, and augments it by generating synthetic data in a way that preserves local and long-range linkage disequilibrium (LD), allowing us to reduce the confounding induced by xAM. We show in simulations based on a realistic xAM model and in genome-wide association study (GWAS) applications for 630 trait pairs that LAVA-Knock can greatly reduce the bias due to xAM relative to LAVA. Furthermore, we show a significant positive correlation between the reduction in local genetic correlations and estimates in the literature of cross-mate phenotype correlations; in particular, pairs of traits that are known to have high cross-mate phenotype correlation values have a significantly higher reduction in the number of local genetic correlations compared with other trait pairs. A few representative examples include education and intelligence, education and alcohol consumption, and attention-deficit hyperactivity disorder and depression. These results suggest that LAVA-Knock can reduce confounding due to both short-range LD and long-range LD induced by xAM.

References

Bulik-Sullivan B, Finucane H, Anttila V, Gusev A, Day F, Loh P . An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015; 47(11):1236-41. PMC: 4797329. DOI: 10.1038/ng.3406. View

van Rheenen W, Peyrot W, Schork A, Lee S, Wray N . Genetic correlations of polygenic disease traits: from theory to practice. Nat Rev Genet. 2019; 20(10):567-581. DOI: 10.1038/s41576-019-0137-z. View

Nordsletten A, Larsson H, Crowley J, Almqvist C, Lichtenstein P, Mataix-Cols D . Patterns of Nonrandom Mating Within and Across 11 Major Psychiatric Disorders. JAMA Psychiatry. 2016; 73(4):354-61. PMC: 5082975. DOI: 10.1001/jamapsychiatry.2015.3192. View

Peyrot W, Robinson M, Penninx B, Wray N . Exploring Boundaries for the Genetic Consequences of Assortative Mating for Psychiatric Traits. JAMA Psychiatry. 2016; 73(11):1189-1195. DOI: 10.1001/jamapsychiatry.2016.2566. View

He Z, Liu L, Wang C, Guen Y, Lee J, Gogarten S . Identification of putative causal loci in whole-genome sequencing data via knockoff statistics. Nat Commun. 2021; 12(1):3152. PMC: 8149672. DOI: 10.1038/s41467-021-22889-4. View

Visscher P, Wray N, Zhang Q, Sklar P, McCarthy M, Brown M . 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017; 101(1):5-22. PMC: 5501872. DOI: 10.1016/j.ajhg.2017.06.005. View

Anttila V, Bulik-Sullivan B, Finucane H, Walters R, Bras J, Duncan L . Analysis of shared heritability in common disorders of the brain. Science. 2018; 360(6395). PMC: 6097237. DOI: 10.1126/science.aap8757. View

He Z, Liu L, Belloy M, Guen Y, Sossin A, Liu X . GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies. Nat Commun. 2022; 13(1):7209. PMC: 9684164. DOI: 10.1038/s41467-022-34932-z. View

Kulski J . Long Noncoding RNA , a Hybrid HLA Class I Endogenous Retroviral Gene: Structure, Expression, and Disease Associations. Cells. 2019; 8(5). PMC: 6562477. DOI: 10.3390/cells8050480. View

10.

Border R, Athanasiadis G, Buil A, Schork A, Cai N, Young A . Cross-trait assortative mating is widespread and inflates genetic correlation estimates. Science. 2022; 378(6621):754-761. PMC: 9901291. DOI: 10.1126/science.abo2059. View

11.

Li H, Mazumder R, Lin X . Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix. Nat Commun. 2023; 14(1):7954. PMC: 10692177. DOI: 10.1038/s41467-023-43565-9. View

12.

Watanabe K, Stringer S, Frei O, Mirkov M, de Leeuw C, Polderman T . A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019; 51(9):1339-1348. DOI: 10.1038/s41588-019-0481-0. View

13.

Greve A, Uher R, Als T, Jepsen J, Mortensen E, Gantriis D . A Nationwide Cohort Study of Nonrandom Mating in Schizophrenia and Bipolar Disorder. Schizophr Bull. 2021; 47(5):1342-1350. PMC: 8379547. DOI: 10.1093/schbul/sbab021. View

14.

Guo H, Li J, Lu Q, Hou L . Detecting local genetic correlations with scan statistics. Nat Commun. 2021; 12(1):2033. PMC: 8016883. DOI: 10.1038/s41467-021-22334-6. View

15.

Shi H, Mancuso N, Spendlove S, Pasaniuc B . Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. Am J Hum Genet. 2017; 101(5):737-751. PMC: 5673668. DOI: 10.1016/j.ajhg.2017.09.022. View

16.

Werme J, van der Sluis S, Posthuma D, de Leeuw C . An integrated framework for local genetic correlation analysis. Nat Genet. 2022; 54(3):274-282. DOI: 10.1038/s41588-022-01017-y. View

17.

Zhang Y, Lu Q, Ye Y, Huang K, Liu W, Wu Y . SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biol. 2021; 22(1):262. PMC: 8422619. DOI: 10.1186/s13059-021-02478-w. View

18.

Yengo L, Robinson M, Keller M, Kemper K, Yang Y, Trzaskowski M . Imprint of assortative mating on the human genome. Nat Hum Behav. 2019; 2(12):948-954. PMC: 6705135. DOI: 10.1038/s41562-018-0476-3. View

19.

Conley D, Laidley T, Belsky D, Fletcher J, Boardman J, Domingue B . Assortative mating and differential fertility by phenotype and genotype across the 20th century. Proc Natl Acad Sci U S A. 2016; 113(24):6647-52. PMC: 4914190. DOI: 10.1073/pnas.1523592113. View