TY - JOUR
T1 - Whole genome sequence analysis of the simulated systolic blood pressure in Genetic Analysis Workshop 18 family data
T2 - Long-term average and collapsing methods
AU - Sung, Yun Ju
AU - Basson, Jacob
AU - Rao, Dabeeru C.
N1 - Funding Information:
We appreciate the constructive and insightful comments of 2 anonymous reviewers; their suggestions substantially improved the manuscript. The work was sponsored by National Institutes of Health (NIH) grants HL086694, HL107552, and HL111249. The GAW18 WGS data were provided by the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in Ethnic Samples) Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The GAW is supported by NIH grant R01 GM031575. This article has been published as part of BMC Proceedings Volume 8 Supplement 1, 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/ supplements/8/S1. Publication charges for this supplement were funded by the Texas Biomedical Research Institute.
Publisher Copyright:
© 2014 Sung et al.; licensee BioMed Central Ltd.
PY - 2014/6/17
Y1 - 2014/6/17
N2 - Analysis of longitudinal family data is challenging because of 2 sources of correlations: correlations across longitudinal measurements and correlations among related individuals. We investigated whether analysis using long-term average (average of all 3 visits) can enhance gene discovery compared with a single-visit analysis. We analyzed all 200 replicates of simulated systolic blood pressure (SBP) in Genetic Analysis Workshop 18 (GAW18) family data using both single-marker and collapsing methods. We considered 2 collapsing approaches: collapsing all variants and collapsing low-frequency variants. Analysis using long-term average performed slightly better than SBP measured at a single visit. Collapsing all variants performed much better than collapsing low-frequency variants at MAP4 and FLNB, which included a common variant with a relatively large effect. For several variants in gene MAP4, single-marker analysis also provided high power. In contrast, collapsing only low-frequency variants performed much better for SCAP, DNASE1L3, and LOC152217, where rare variants in these genes had larger effect than common variants. However, for other causal variants, all approaches provided disappointingly poor performance. This poor performance appeared to occur because most of these causal variants explained a very small fraction of phenotypic variance. We also found that collapsing multiple variants did worse than single-marker analysis for several genes when they contained causal single-nucleotide polymorphisms (SNPs) with both positive and negative effects. Because half of causal SNPs were not found in the annotation file based on the 1000 Genomes Project, we found that power was also affected by our use of incomplete annotation information.
AB - Analysis of longitudinal family data is challenging because of 2 sources of correlations: correlations across longitudinal measurements and correlations among related individuals. We investigated whether analysis using long-term average (average of all 3 visits) can enhance gene discovery compared with a single-visit analysis. We analyzed all 200 replicates of simulated systolic blood pressure (SBP) in Genetic Analysis Workshop 18 (GAW18) family data using both single-marker and collapsing methods. We considered 2 collapsing approaches: collapsing all variants and collapsing low-frequency variants. Analysis using long-term average performed slightly better than SBP measured at a single visit. Collapsing all variants performed much better than collapsing low-frequency variants at MAP4 and FLNB, which included a common variant with a relatively large effect. For several variants in gene MAP4, single-marker analysis also provided high power. In contrast, collapsing only low-frequency variants performed much better for SCAP, DNASE1L3, and LOC152217, where rare variants in these genes had larger effect than common variants. However, for other causal variants, all approaches provided disappointingly poor performance. This poor performance appeared to occur because most of these causal variants explained a very small fraction of phenotypic variance. We also found that collapsing multiple variants did worse than single-marker analysis for several genes when they contained causal single-nucleotide polymorphisms (SNPs) with both positive and negative effects. Because half of causal SNPs were not found in the annotation file based on the 1000 Genomes Project, we found that power was also affected by our use of incomplete annotation information.
UR - http://www.scopus.com/inward/record.url?scp=85018193261&partnerID=8YFLogxK
U2 - 10.1186/1753-6561-8-S1-S12
DO - 10.1186/1753-6561-8-S1-S12
M3 - Article
C2 - 25519365
AN - SCOPUS:85018193261
SN - 1753-6561
VL - 8
JO - BMC Proceedings
JF - BMC Proceedings
M1 - S12
ER -