On combining family-and populationbased sequencing data

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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.

Original languageEnglish
Article number21
JournalBMC Proceedings
StatePublished - 2016

Bibliographical note

Publisher Copyright:
© 2016 The Author(s).

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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