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Multiphase analysis by linkage, quantitative transmission disequilibrium, and measured genotype: systolic blood pressure in complex Mexican American pedigrees.


ABSTRACT: We apply a multiphase strategy for pedigree-based genetic analysis of systolic blood pressure data collected in a longitudinal study of large Mexican American pedigrees. In the first phase, we conduct variance-components linkage analysis to identify regions that may harbor quantitative trait loci. In the second phase, we carry out pedigree-based association analysis in a selected region with common and low-frequency variants from genome-wide association studies and whole genome sequencing data. Using sequencing data, we compare approaches to pedigree analysis in a 10 megabase candidate region on chromosome 3 harboring a gene previously identified by a consortium for blood pressure genome-wide association studies. We observe that, as expected, the measured genotype analysis tends to provide larger signals than the quantitative transmission disequilibrium test. We also observe that while linkage signals are contributed by common variants, strong associations are found mainly at rare variants. Multiphase analysis can improve computational efficiency and reduce the multiple testing burden.

SUBMITTER: Chen Z 

PROVIDER: S-EPMC4143726 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Multiphase analysis by linkage, quantitative transmission disequilibrium, and measured genotype: systolic blood pressure in complex Mexican American pedigrees.

Chen Zhijian Z   Tan Kuan-Rui KR   Bull Shelley B SB  

BMC proceedings 20140617 Suppl 1


We apply a multiphase strategy for pedigree-based genetic analysis of systolic blood pressure data collected in a longitudinal study of large Mexican American pedigrees. In the first phase, we conduct variance-components linkage analysis to identify regions that may harbor quantitative trait loci. In the second phase, we carry out pedigree-based association analysis in a selected region with common and low-frequency variants from genome-wide association studies and whole genome sequencing data.  ...[more]

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