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Estimating relationships between phenotypes and subjects drawn from admixed families
BACKGROUND: Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133521/ https://www.ncbi.nlm.nih.gov/pubmed/27980662 http://dx.doi.org/10.1186/s12919-016-0056-3 |
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author | Blue, Elizabeth M. Brown, Lisa A. Conomos, Matthew P. Kirk, Jennifer L. Nato, Alejandro Q. Popejoy, Alice B. Raffa, Jesse Ranola, John Wijsman, Ellen M. Thornton, Timothy |
author_facet | Blue, Elizabeth M. Brown, Lisa A. Conomos, Matthew P. Kirk, Jennifer L. Nato, Alejandro Q. Popejoy, Alice B. Raffa, Jesse Ranola, John Wijsman, Ellen M. Thornton, Timothy |
author_sort | Blue, Elizabeth M. |
collection | PubMed |
description | BACKGROUND: Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. RESULTS: We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them. CONCLUSIONS: Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it. |
format | Online Article Text |
id | pubmed-5133521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51335212016-12-15 Estimating relationships between phenotypes and subjects drawn from admixed families Blue, Elizabeth M. Brown, Lisa A. Conomos, Matthew P. Kirk, Jennifer L. Nato, Alejandro Q. Popejoy, Alice B. Raffa, Jesse Ranola, John Wijsman, Ellen M. Thornton, Timothy BMC Proc Proceedings BACKGROUND: Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. RESULTS: We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them. CONCLUSIONS: Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it. BioMed Central 2016-10-18 /pmc/articles/PMC5133521/ /pubmed/27980662 http://dx.doi.org/10.1186/s12919-016-0056-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Proceedings Blue, Elizabeth M. Brown, Lisa A. Conomos, Matthew P. Kirk, Jennifer L. Nato, Alejandro Q. Popejoy, Alice B. Raffa, Jesse Ranola, John Wijsman, Ellen M. Thornton, Timothy Estimating relationships between phenotypes and subjects drawn from admixed families |
title | Estimating relationships between phenotypes and subjects drawn from admixed families |
title_full | Estimating relationships between phenotypes and subjects drawn from admixed families |
title_fullStr | Estimating relationships between phenotypes and subjects drawn from admixed families |
title_full_unstemmed | Estimating relationships between phenotypes and subjects drawn from admixed families |
title_short | Estimating relationships between phenotypes and subjects drawn from admixed families |
title_sort | estimating relationships between phenotypes and subjects drawn from admixed families |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133521/ https://www.ncbi.nlm.nih.gov/pubmed/27980662 http://dx.doi.org/10.1186/s12919-016-0056-3 |
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