Leveraging population admixture to characterize the heritability of complex traits

Noah Zaitlen; Bogdan Pasaniuc; Sriram Sankararaman; Gaurav Bhatia; Jianqi Zhang; Alexander Gusev; Taylor Young; Arti Tandon; Samuela Pollack; Bjarni J Vilhjálmsson; Themistocles L Assimes; Sonja I Berndt; William J Blot; Stephen Chanock; Nora Franceschini; Phyllis G Goodman; Jing He; Anselm J M Hennis; Ann Hsing; Sue A Ingles; William Isaacs; Rick A Kittles; Eric A Klein; Leslie A Lange; Barbara Nemesure; Nick Patterson; David Reich; Benjamin A Rybicki; Janet L Stanford; Victoria L Stevens; Sara S Strom; Eric A Whitsel; John S Witte; Jianfeng Xu; Christopher Haiman; James G Wilson; Charles Kooperberg; Daniel Stram; Alex P Reiner; Hua Tang; Alkes L Price

doi:10.1038/ng.3139

Leveraging population admixture to characterize the heritability of complex traits

Nat Genet. 2014 Dec;46(12):1356-62. doi: 10.1038/ng.3139. Epub 2014 Nov 10.

Authors

Noah Zaitlen¹, Bogdan Pasaniuc², Sriram Sankararaman³, Gaurav Bhatia⁴, Jianqi Zhang⁵, Alexander Gusev⁴, Taylor Young⁶, Arti Tandon³, Samuela Pollack⁴, Bjarni J Vilhjálmsson⁴, Themistocles L Assimes⁷, Sonja I Berndt⁸, William J Blot⁹, Stephen Chanock⁸, Nora Franceschini¹⁰, Phyllis G Goodman¹¹, Jing He⁵, Anselm J M Hennis¹², Ann Hsing¹³, Sue A Ingles⁵, William Isaacs¹⁴, Rick A Kittles¹⁵, Eric A Klein¹⁶, Leslie A Lange¹⁷, Barbara Nemesure¹⁸, Nick Patterson⁶, David Reich¹⁹, Benjamin A Rybicki²⁰, Janet L Stanford²¹, Victoria L Stevens²², Sara S Strom²³, Eric A Whitsel¹⁰, John S Witte²⁴, Jianfeng Xu²⁵, Christopher Haiman²⁶, James G Wilson²⁷, Charles Kooperberg²¹, Daniel Stram⁵, Alex P Reiner²⁸, Hua Tang²⁹, Alkes L Price⁴

Affiliations

¹ Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
² Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, California, USA.
³ 1] Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. [2] Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.
⁴ 1] Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. [2] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. [3] Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
⁵ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
⁶ Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
⁷ Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
⁸ Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA.
⁹ 1] Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. [2] Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. [3] International Epidemiology Institute, Rockville, Maryland, USA.
¹⁰ Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA.
¹¹ SWOG Statistical Center, Seattle, Washington, USA.
¹² 1] Department of Preventive Medicine, Stony Brook University, Stony Brook, New York, USA. [2] Chronic Disease Research Centre, University of the West Indies, Bridgetown, Barbados. [3] Faculty of Medical Sciences, University of the West Indies, Bridgetown, Barbados. [4] Ministry of Health, Bridgetown, Barbados.
¹³ 1] Cancer Prevention Institute of California, Fremont, California, USA. [2] Division of Epidemiology, Stanford University School of Medicine, Stanford, California, USA.
¹⁴ James Buchanan Brady Urological Institute, Johns Hopkins Hospital and Medical Institutions, Baltimore, Maryland, USA.
¹⁵ Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA.
¹⁶ Glickman Urologic and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA.
¹⁷ Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.
¹⁸ Department of Preventive Medicine, Stony Brook University, Stony Brook, New York, USA.
¹⁹ 1] Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. [2] Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. [3] Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, USA.
²⁰ Department of Public Health Sciences, Henry Ford Hospital, Detroit, Michigan, USA.
²¹ Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
²² Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA.
²³ Department of Epidemiology, Division of Cancer Prevention and Population Sciences, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
²⁴ Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA.
²⁵ Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
²⁶ 1] Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. [2] Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, USA.
²⁷ Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, USA.
²⁸ Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, USA.
²⁹ Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.

PMID: 25383972
PMCID: PMC4244251
DOI: 10.1038/ng.3139

Abstract

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2).

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Aged
Black People
Black or African American / genetics
Body Mass Index
Cardiovascular Diseases / genetics
Case-Control Studies
Chromosome Mapping
Cohort Studies
Computer Simulation
Epistasis, Genetic
Female
Genetics, Population / methods*
Genome-Wide Association Study*
Genotype
Humans
Male
Middle Aged
Models, Genetic
Models, Statistical
Multifactorial Inheritance*
Phenotype
Polymorphism, Single Nucleotide
Prostatic Neoplasms / genetics
Quantitative Trait, Heritable*
Reproducibility of Results
United States

Abstract

Publication types

MeSH terms

Grants and funding