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

Leveraging population admixture to characterize the heritability of complex traits.

Author information

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.

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).

PMID:
25383972
PMCID:
PMC4244251
DOI:
10.1038/ng.3139
[Indexed for MEDLINE]
Free PMC Article

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