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Cited by 63Genetic Associations and EpidemiologyForensic and Genetic ResearchGenetic and phenotypic traits in livestock

Leveraging population admixture to characterize the heritability of complex traits

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Zaitlen, Noah, Pasaniuc, Bogdan, Sankararaman, Sriram, Bhatia, Gaurav, Zhang, Jianqi, Gusev, Alexander, Young, Taylor, Tandon, Arti +33 more·2014·Nature Genetics
DOI 10.1038/ng.3139PMC PMC4244251
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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).

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  • The problem of bias in local-ancestry heritability estimates is most pronounced when the trait under consideration has experienced selection in the source populations.

    “This problem is most pronounced when the trait under consideration has experienced selection in the source populations, a scenario that Zaitlen et al. (15) recognized as potentially problematic for their estimator.”

    From Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics (2024) · cites this paper as [15]

    The Discussion recognizes that selection acting on causal variants can bias the estimator, particularly when positive selection in one ancestral group raises their genetic differentiation. The Results sensitivity analysis qualifies this: significant bias from positive selection occurred only at extreme values of both locus differentiation and heritability. Selection in a source population alone is therefore not enough to establish the claim's broader 'most pronounced' characterization. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The variation due to local ancestry that local-ancestry-based heritability estimation leverages has no clear relationship with between-group trait differences.

    “We show that the variation due to local ancestry that they leverage has no clear relationship with between-group trait differences.”

    From Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics (2024) · cites this paper as [15]

    The Results (Overview of method) and Online Methods describe how variation in local ancestry is used to estimate heritability. Our copy does not address whether that variation has a clear relationship with between-group trait differences; searches for population-level phenotype differences found no passage settling the claim. Page numbers were not available in the indexed copy. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Authors
Zaitlen, N., Pasaniuc, B., Sankararaman, S., Bhatia, G., Zhang, J., Gusev, A., Young, T., Tandon, A., Pollack, S., Vilhjálmsson, B. J., Assimes, T. L., Berndt, S. I., Blot, W. J., Chanock, S., Franceschini, N., Goodman, P. G., He, J., Hennis, A. J. M., Hsing, A., Ingles, S. A., Isaacs, W., Kittles, R. A., Klein, E. A., Lange, L. A., Nemesure, B., Patterson, N., Reich, D., Rybicki, B. A., Stanford, J. L., Stevens, V. L., Strom, S. S., Whitsel, E. A., Witte, J. S., Xu, J., Haiman, C., Wilson, J. G., Kooperberg, C., Stram, D., Reiner, A. P., Tang, H., & Price, A. L.
Affiliations
  • University of California, San Francisco (US)
  • Broad Institute (US)
  • University of Southern California (US)
  • Stanford Medicine (US)
  • National Institutes of Health (US)
  • Vanderbilt University (US)
  • University of North Carolina at Chapel Hill (US)
  • Ministry of Health (BB)
  • Cancer Prevention Institute of California (US)
  • Johns Hopkins University (US)
  • University of Illinois Chicago (US)
  • Cleveland Clinic (US)
  • Stony Brook University (US)
  • Henry Ford Health (US)
  • Fred Hutch Cancer Center (US)
  • American Cancer Society (US)
  • The University of Texas MD Anderson Cancer Center (US)
  • Wake Forest University Health Sciences (US)
  • University of Mississippi Medical Center (US)
  • University of Washington (US)
Corresponding author
  • Zaitlen, Noah
  • Tang, Hua
  • Price, Alkes L
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2014-11-10
Volume
46
Issue
12
Pages
1356-1362
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/ng.3139
PMC
PMC4244251
OpenAlex
W2112140283
Cited by
63
References
10
Citations by year
2026: 1 · 2025: 2 · 2024: 3 · 2023: 6 · 2022: 2 · 2021: 2 · 2020: 6 · 2019: 7 · 2018: 5 · 2017: 10 · 2016: 9 · 2015: 9 · 2013: 1
FWCI
6.13
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Genetics (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Funders
  • U.S. Department of Health and Human Services (HHSN268201100002C, HHSN2682011 00003C, HHSN 268201100001C, HHSN268201100046C, HHSN268201100004C, R01-HG006399, HHSN-271-2011-00004C, HG006399)
  • National Institutes of Health (HHSN268201100004C, HHSN268201100046C, HHSN271201100004C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, GM073059)
  • National Institute on Aging (HHSN-271-2011-00004C,)
  • National Heart, Lung, and Blood Institute (R21HL123677, K25HL121295, HHSN268201100004C, HHSN271201100004C, HHSN26820-1100002C, HHSN268201100046C, HHSN268201100001C, HHSN268201100003C)
  • National Human Genome Research Institute (R01HG006399)
  • National Cancer Institute (R01CA092447, UM1CA182883, UG1CA189974, U10CA037429, P30CA015704)
  • National Institute of General Medical Sciences (F32GM106584, R01GM073059, R01GM100233)
  • National Institute of Environmental Health Sciences (R21ES020754, R01ES011126, T32ES007142)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords
narrow-sense heritability, complex traits, population admixture, local ancestry, FST, African American population, unrelated individuals, height, body mass index, quantitative phenotypes, rare variants, epistasis, shared environment
MeSH terms
Aged, Black or African American, Cardiovascular Diseases, Chromosome Mapping, Computer Simulation, Epistasis, Genetic, Female, Genetics, Population, Genotype, Humans, Male, Middle Aged, Models, Genetic, Phenotype, Prostatic Neoplasms, United States, Reproducibility of Results, Models, Statistical, Cohort Studies, Body Mass Index, Case-Control Studies, Quantitative Trait, Heritable, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Black People, Genome-Wide Association Study

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