Cited by 336Genetic Associations and EpidemiologyForensic and Genetic ResearchRace, Genetics, and Society

Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos

Open access
Conomos, Matthew P., Laurie, Cecelia A., Stilp, Adrienne M., Gogarten, Stephanie M., McHugh, Caitlin P., Nelson, Sarah C., Sofer, Tamar, Fernández-Rhodes, Lindsay +29 more·2016·The American Journal of Human Genetics

Abstract

US Hispanic/Latino individuals are diverse in genetic ancestry, culture, and environmental exposures. Here, we characterized and controlled for this diversity in genome-wide association studies (GWASs) for the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We simultaneously estimated population-structure principal components (PCs) robust to familial relatedness and pairwise kinship coefficients (KCs) robust to population structure, admixture, and Hardy-Weinberg departures. The PCs revealed substantial genetic differentiation within and among six self-identified background groups (Cuban, Dominican, Puerto Rican, Mexican, and Central and South American). To control for variation among groups, we developed a multi-dimensional clustering method to define a "genetic-analysis group" variable that retains many properties of self-identified background while achieving substantially greater genetic homogeneity within groups and including participants with non-specific self-identification. In GWASs of 22 biomedical traits, we used a linear mixed model (LMM) including pairwise empirical KCs to account for familial relatedness, PCs for ancestry, and genetic-analysis groups for additional group-associated effects. Including the genetic-analysis group as a covariate accounted for significant trait variation in 8 of 22 traits, even after we fit 20 PCs. Additionally, genetic-analysis groups had significant heterogeneity of residual variance for 20 of 22 traits, and modeling this heteroscedasticity within the LMM reduced genomic inflation for 19 traits. Furthermore, fitting an LMM that utilized a genetic-analysis group rather than a self-identified background group achieved higher power to detect previously reported associations. We expect that the methods applied here will be useful in other studies with multiple ethnic groups, admixture, and relatedness.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ajhg.2015.12.001

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • All statistical analyses were adjusted for sex and 3 genetic principal components estimated with the PC-air method, and linear mixed-model analyses were additionally covaried by a sparse genetic relationship matrix estimated with the PC-Relate method.

    “All statistical analyses were adjusted for sex and 3 genetic principal components estimated with the PC-air method 29 implemented in GENESIS.Linear mixed-model analyses were additionally covaried by a sparse genetic relationship matrix estimated with the PC-Relate method 30 implemented in GENESIS.”

    From Association of African Ancestry–Specific <i>APOE</i> Missense Variant R145C With Risk of Alzheimer Disease (2023) · cites this paper as [30]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 7

  1. Bryc, K., Velez, C., Karafet, T., Moreno-Estrada, A., Reynolds, A., Auton, A., Hammer, M., Bustamante, C.D., and Ostrer, H. (2010). Colloquium paper: genome-wide patterns of popula- tion structure and admixture among Hispanic/Latino popula- tions. Proc. Natl. Acad. Sci. USA 107 (Suppl 2 ), 8954-8961.
  2. Moreno-Estrada, A., Gignoux, C.R., Ferna ´ndez-Lo ´pez, J.C., Za- kharia, F., Sikora, M., Contreras, A.V., Acun ˜a-Alonzo, V., San- doval, K., Eng, C., Romero-Hidalgo, S., et al. (2014). Human genetics. The genetics of Mexico recapitulates Native The American Journal of Human Genetics 98, 165-184, January 7, 2016 181 American substructure and affects biomedical traits. Science 344, 1280-1285.
  3. Joliffe, I.T. (2004). Principal Component Analysis (Springer).
  4. Rich, S.S., Goodarzi, M.O., Palmer, N.D., Langefeld, C.D., Zie- gler, J., Haffner, S.M., Bryer-Ash, M., Norris, J.M., Taylor, K.D., Haritunians, T., et al. (2009). A genome-wide association scan 182 The American Journal of Human Genetics 98, 165-184, January 7, 2016 for acute insulin response to glucose in Hispanic-Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS FS). Diabetologia 52, 1326-1333.
  5. Conomos, M.P. (2014). Inferring, estimating and accounting for population and pedigree structure in genetic analyses. PhD thesis (University of Washington).
  6. Adler, D., and Murdoch, D. (2014). rgl: 3D visualization using OpenGL. R package version 0.95.1201. http://CRAN. R-project/package¼rgl. The American Journal of Human Genetics 98, 165-184, January 7, 2016 183
  7. Burnham, K.P., and Anderson, D.R. (2002). Model Selection and Multimodel Inference (New York: Springer-Verlag).

Cited by

Sorted by citations

Authors
Conomos, M. P., Laurie, C. A., Stilp, A. M., Gogarten, S. M., McHugh, C. P., Nelson, S. C., Sofer, T., Fernández-Rhodes, L., Justice, A. E., Graff, M., Young, K. L., Seyerle, A. A., Avery, C. L., Taylor, K. D., Rotter, J. I., Talavera, G. A., Daviglus, M. L., Wassertheil-Smoller, S., Schneiderman, N., Heiss, G., Kaplan, R. C., Franceschini, N., Reiner, A. P., Shaffer, J. R., Barr, R. G., Kerr, K. F., Browning, S. R., Browning, B. L., Weir, B. S., Avilés-Santa, M. L., Papanicolaou, G. J., Lumley, T., Szpiro, A. A., North, K. E., Rice, K., Thornton, T. A., & Laurie, C. C.
Affiliations
  • University of Washington (US)
  • University of North Carolina at Chapel Hill (US)
  • The Lundquist Institute (US)
  • University of Illinois Chicago (US)
  • University of Miami (US)
  • Albert Einstein College of Medicine (US)
  • University of Pittsburgh (US)
  • Columbia University Irving Medical Center (US)
  • National Institutes of Health (US)
  • University of Auckland (NZ)
Corresponding author
  • Conomos, Matthew P.
  • Laurie, Cathy C.
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2016-01
Volume
98
Issue
1
Pages
165-184
ISSN
0002-9297 (ISSN-L)
OpenAlex
W2226732401
Cited by
336
References
13
Citations by year
2026: 31 · 2025: 30 · 2024: 24 · 2023: 40 · 2022: 37 · 2021: 39 · 2020: 20 · 2019: 36 · 2018: 30 · 2017: 30 · 2016: 18 · 2015: 1
FWCI
25.49
Type
journal article
Language
en
Open access
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • UNC Libraries
Funders
  • U.S. Department of Health and Human Services
  • Office of Dietary Supplements (N01-HC65237, N01-HC65235, N01-HC65233, N01-HC65234, N01-HC65236)
  • University of Miami (N01-HC65236, N01-HC65237, N01- HC65235, N01- HC65234, N01-HC65233)
  • Northwestern University (N01‐HC65236, N01HC65237, N01-HC65233, N01-HC65235, N01-HC65234)
  • San Diego State University (N01-HC65233, N01-HC65234, N01-HC65235, N01-HC65236, N01‐HC‐65237)
  • University of Washington (HHSN268201300005C)
  • National Institutes of Health (n01hc65235-13-0-1, n01hc65234-5-0-0, UL1TR000124, n01hc65237-14-0-1, n01hc65236-16-0-1, N01-HC65236, N01-HC65235, N01-HC65234, N01-HC65237, n01hc065233-1-0-0, DK-063491)
  • National Heart, Lung, and Blood Institute (DK-063491, N01HC65235, (N01-HC-65237, N01‐HC‐65234, N01 HC065235, HHSN268201300005C, N01-HC65236, N01 HC065237, N01‐HC65233, N01 HC065234, N01 HC065236, HHSN268201300005C AM03, N01 HC065233)
  • National Institute on Deafness and Other Communication Disorders (N01-HC65234, N01-HC65235, N01-HC65233, N01-HC65237, N01-HC65236)
  • National Institute of Diabetes and Digestive and Kidney Diseases (DK063491, N01-HC65234, N01-HC65236, N01-HC65233, N01-HC65235, N01-HC65237, R01DK101855, P30DK063491)
  • National Institute of Dental and Craniofacial Research (MOD03, N01-HC65236, N01-HC65234, N01-HC65237, HHSN268201300005C, HHSN268201300005C AM03, N01-HC65235, N01-HC65233, 13GRNT16490017)
  • National Center for Advancing Translational Sciences
  • National Institute on Minority Health and Health Disparities (N01-HC65233, N01-HC65234, N01-HC65235, N01-HC65237, N01-HC65236)
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 › Race, Genetics, and Society
Keywords
Hispanic/Latino populations, genetic ancestry, admixture, population structure, kinship coefficient, linear mixed models, genomic inflation, heteroscedasticity, genetic diversity, genome-wide association study, Hispanic Community Health Study/Study of Latinos, relatedness
MeSH terms
Hispanic or Latino, Humans, United States, Genetic Variation, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review