Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics

Tsz Fung Chan; Xinyue Rui; David V Conti; Myriam Fornage; Mariaelisa Graff; Jeffrey Haessler; Christopher Haiman; Heather M Highland; Su Yon Jung; Eimear E Kenny; Charles Kooperberg; Loic Le Marchand; Kari E North; Ran Tao; Genevieve Wojcik; Christopher R Gignoux; PAGE Consortium; Charleston W K Chiang; Nicholas Mancuso

doi:10.1016/j.ajhg.2023.09.012

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics

Am J Hum Genet. 2023 Nov 2;110(11):1853-1862. doi: 10.1016/j.ajhg.2023.09.012. Epub 2023 Oct 23.

Authors

Tsz Fung Chan¹, Xinyue Rui¹, David V Conti¹, Myriam Fornage², Mariaelisa Graff³, Jeffrey Haessler⁴, Christopher Haiman¹, Heather M Highland³, Su Yon Jung⁵, Eimear E Kenny⁶, Charles Kooperberg⁴, Loic Le Marchand⁷, Kari E North³, Ran Tao⁸, Genevieve Wojcik⁹, Christopher R Gignoux¹⁰; PAGE Consortium; Charleston W K Chiang¹¹, Nicholas Mancuso¹²

Affiliations

¹ Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
² Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA.
³ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
⁴ Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
⁵ Translational Sciences Section, School of Nursing, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
⁸ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
⁹ Department of Epidemiology, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA.
¹⁰ Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹¹ Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
¹² Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Electronic address: nicholas.mancuso@med.usc.edu.

Abstract

The heritability explained by local ancestry markers in an admixed population (h_γ²) provides crucial insight into the genetic architecture of a complex disease or trait. Estimation of h_γ² can be susceptible to biases due to population structure in ancestral populations. Here, we present heritability estimation from admixture mapping summary statistics (HAMSTA), an approach that uses summary statistics from admixture mapping to infer heritability explained by local ancestry while adjusting for biases due to ancestral stratification. Through extensive simulations, we demonstrate that HAMSTA h_γ² estimates are approximately unbiased and are robust to ancestral stratification compared to existing approaches. In the presence of ancestral stratification, we show a HAMSTA-derived sampling scheme provides a calibrated family-wise error rate (FWER) of ∼5% for admixture mapping, unlike existing FWER estimation approaches. We apply HAMSTA to 20 quantitative phenotypes of up to 15,988 self-reported African American individuals in the Population Architecture using Genomics and Epidemiology (PAGE) study. We observe hˆ_γ² in the 20 phenotypes range from 0.0025 to 0.033 (mean hˆ_γ² = 0.012 ± 9.2 × 10^-4), which translates to hˆ² ranging from 0.062 to 0.85 (mean hˆ² = 0.30 ± 0.023). Across these phenotypes we find little evidence of inflation due to ancestral population stratification in current admixture mapping studies (mean inflation factor of 0.99 ± 0.001). Overall, HAMSTA provides a fast and powerful approach to estimate genome-wide heritability and evaluate biases in test statistics of admixture mapping studies.

Keywords: admixture mapping; family-wise error rate; genetic admixture; genome-wide association; heritability; local ancestry; population structure; summary statistics.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Black or African American*
Chromosome Mapping
Genetics, Population*
Humans
Phenotype
Polymorphism, Single Nucleotide / genetics

Abstract

Publication types

MeSH terms

Grants and funding