Cargando…

On combining family- and population-based sequencing data

Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet well established. In this stu...

Descripción completa

Detalles Bibliográficos
Autores principales: Katsumata, Yuriko, Fardo, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133531/
https://www.ncbi.nlm.nih.gov/pubmed/27980632
http://dx.doi.org/10.1186/s12919-016-0026-9
_version_ 1782471282468585472
author Katsumata, Yuriko
Fardo, David W.
author_facet Katsumata, Yuriko
Fardo, David W.
author_sort Katsumata, Yuriko
collection PubMed
description Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet well established. In this study, we investigated the efficient combination of population-based and family-based sequencing data to evaluate best practices using the Genetic Analysis Workshop 19 (GAW19) data set. Because one design employed whole genome sequencing and the other whole exome sequencing, we examined variants overlapping both data sets. We used the family-based sequence kernel association test (famSKAT) to analyze the family- and population-based data sets separately as well as with a combined data set. These were compared against meta-analysis. Using the combined data, we showed that famSKAT has high power to detect associations between diastolic and/or systolic blood pressures and the genes that have causal variants with large effect sizes, such as MAP4, TNN, and CGN. However, when there was a considerable difference in the powers between family- and population-based data, famSKAT with the combined data had lower power than that from the population-based data alone. The famSKAT test statistic for the combined data can be influenced by sample imbalance from the 2 designs. This underscores the importance of foresight in study design as, in this situation, the greatly lower sample size in the family-based data essentially serves to dilute signal. We observed inflated type I errors in our simulation study, largely when using population-based data, which might be a result of principal components failing to completely account for population admixture in this cohort.
format Online
Article
Text
id pubmed-5133531
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-51335312016-12-15 On combining family- and population-based sequencing data Katsumata, Yuriko Fardo, David W. BMC Proc Proceedings Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet well established. In this study, we investigated the efficient combination of population-based and family-based sequencing data to evaluate best practices using the Genetic Analysis Workshop 19 (GAW19) data set. Because one design employed whole genome sequencing and the other whole exome sequencing, we examined variants overlapping both data sets. We used the family-based sequence kernel association test (famSKAT) to analyze the family- and population-based data sets separately as well as with a combined data set. These were compared against meta-analysis. Using the combined data, we showed that famSKAT has high power to detect associations between diastolic and/or systolic blood pressures and the genes that have causal variants with large effect sizes, such as MAP4, TNN, and CGN. However, when there was a considerable difference in the powers between family- and population-based data, famSKAT with the combined data had lower power than that from the population-based data alone. The famSKAT test statistic for the combined data can be influenced by sample imbalance from the 2 designs. This underscores the importance of foresight in study design as, in this situation, the greatly lower sample size in the family-based data essentially serves to dilute signal. We observed inflated type I errors in our simulation study, largely when using population-based data, which might be a result of principal components failing to completely account for population admixture in this cohort. BioMed Central 2016-10-18 /pmc/articles/PMC5133531/ /pubmed/27980632 http://dx.doi.org/10.1186/s12919-016-0026-9 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
Katsumata, Yuriko
Fardo, David W.
On combining family- and population-based sequencing data
title On combining family- and population-based sequencing data
title_full On combining family- and population-based sequencing data
title_fullStr On combining family- and population-based sequencing data
title_full_unstemmed On combining family- and population-based sequencing data
title_short On combining family- and population-based sequencing data
title_sort on combining family- and population-based sequencing data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133531/
https://www.ncbi.nlm.nih.gov/pubmed/27980632
http://dx.doi.org/10.1186/s12919-016-0026-9
work_keys_str_mv AT katsumatayuriko oncombiningfamilyandpopulationbasedsequencingdata
AT fardodavidw oncombiningfamilyandpopulationbasedsequencingdata