Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals

Kangcheng Hou; Yi Ding; Ziqi Xu; Yue Wu; Arjun Bhattacharya; Rachel Mester; Gillian M Belbin; Steve Buyske; David V Conti; Burcu F Darst; Myriam Fornage; Chris Gignoux; Xiuqing Guo; Christopher Haiman; Eimear E Kenny; Michelle Kim; Charles Kooperberg; Leslie Lange; Ani Manichaikul; Kari E North; Ulrike Peters; Laura J Rasmussen-Torvik; Stephen S Rich; Jerome I Rotter; Heather E Wheeler; Genevieve L Wojcik; Ying Zhou; Sriram Sankararaman; Bogdan Pasaniuc

doi:10.1038/s41588-023-01338-6

Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals

Nat Genet. 2023 Apr;55(4):549-558. doi: 10.1038/s41588-023-01338-6. Epub 2023 Mar 20.

Authors

Kangcheng Hou¹, Yi Ding², Ziqi Xu³, Yue Wu³, Arjun Bhattacharya⁴, Rachel Mester⁵, Gillian M Belbin^#^{6

7}, Steve Buyske^#⁸, David V Conti^#⁹, Burcu F Darst^#¹⁰, Myriam Fornage^#¹¹, Chris Gignoux^#¹², Xiuqing Guo^#¹³, Christopher Haiman^#⁹, Eimear E Kenny^#^{6

14

15}, Michelle Kim^#¹⁰, Charles Kooperberg^#¹⁰, Leslie Lange^#¹⁶, Ani Manichaikul^#¹⁷, Kari E North^#^{8

18}, Ulrike Peters^#¹⁰, Laura J Rasmussen-Torvik^#¹⁹, Stephen S Rich^#¹⁷, Jerome I Rotter^#¹³, Heather E Wheeler^#^{20

21}, Genevieve L Wojcik^#²², Ying Zhou^#¹⁰, Sriram Sankararaman^{2

3

23

24}, Bogdan Pasaniuc^{25

26

27

28}

Affiliations

¹ Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA. houkc@ucla.edu.
² Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
³ Department of Computer Science, UCLA, Los Angeles, CA, USA.
⁴ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
⁵ Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
⁶ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁷ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁸ Department of Statistics, Rutgers University, Piscataway, NJ, USA.
⁹ Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹⁰ Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹¹ Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA.
¹² Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA.
¹³ Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁴ Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁵ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
¹⁶ Department of Medicine, University of Colorado, Aurora, CO, USA.
¹⁷ Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
¹⁸ Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁹ Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
²⁰ Department of Biology, Loyola University Chicago, Chicago, IL, USA.
²¹ Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA.
²² Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
²³ Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
²⁴ Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
²⁵ Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.
²⁶ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.
²⁷ Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.
²⁸ Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. pasaniuc@ucla.edu.

^# Contributed equally.

PMID: 36941441
DOI: 10.1038/s41588-023-01338-6

Abstract

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (r_admix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis r_admix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.

Publication types

Meta-Analysis
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural

MeSH terms

Black or African American / genetics
Genetics, Population*
Genome-Wide Association Study / methods
Humans
Multifactorial Inheritance* / genetics
Polymorphism, Single Nucleotide / genetics
Racial Groups / genetics

Abstract

Publication types

MeSH terms

Grants and funding