Multipoint association mapping for longitudinal family data: an application to hypertension phenotypes

Abstract

It is essential to develop adequate statistical methods to fully utilize information from longitudinal family studies. We extend our previous multipoint linkage disequilibrium approach—simultaneously accounting for correlations between markers and repeat measurements within subjects, and the correlations between subjects in families—to detect loci relevant to disease through gene-based analysis. Estimates of disease loci and their genetic effects along with their 95 % confidence intervals (or significance levels) are reported. Four different phenotypes—ever having hypertension at 4 visits, incidence of hypertension, hypertension status at baseline only, and hypertension status at 4 visits—are studied using the proposed approach. The efficiency of estimates of disease locus positions (inverse of standard error) improves when using the phenotypes from 4 visits rather than using baseline only.

Longitudinal analytical approaches to genetic data

“…In the first GWA approach, Chiu et al [ 10 ] used multipoint LD with GEE to conduct gene-based association tests between SNPs within 1095 genes on chromosome 3 and 4 derived phenotypes of hypertension status: (a) Ever ; (b) Progression ; (c) Baseline ; and (d) Longitudinal . Of 1095 genes tested, 119 (Ever) , 79 (Progression) , and 42 (Longitudinal) were significantly associated ( P <4.57 × 10 −5 ), using a Bonferroni multiple correction for number of genes, with hypertension status.…”

“…In the first GWA approach, Chiu et al [ 10 ] developed a multipoint linkage disequilibrium (LD) mapping method to localize disease susceptibility loci using chromosome 3 GWA SNPs (see Table 1 ). Their model accounted for correlations between subjects in families, between markers, and repeat measurements within subjects, simultaneously using the GEE approach [ 15 , 16 ].…”

“…In this paper, we summarize 3 GAW19 contributions (Table 1 ) that are focused on the development of statistical methods using repeat measurement and either GWA [ 10 , 11 ] or WGS [ 12 ]. Similar to previous GAWs, investigators in this group applied a variety of statistical approaches and strategies to deal with the advantages and challenges incorporating longitudinal measurements mentioned above.…”

“…All of these discussed contributions used pedigree-based information, as only the family-based data contained repeat measurements. The 2 GWA contributions analyzed the real GAW19 data [ 10 , 11 ], while the WGS contribution used the simulated GAW19 data set [ 12 ] with prior knowledge of the answers.…”

Genetic Analysis Workshop 19: methods and strategies for analyzing human sequence and gene expression data in extended families and unrelated individuals

“…The discussion highlights the need for future extensions and generalizations of the concept of collapsing tests. Melton led a group that addressed tests of association with longitudinal phenotypes [7]. This paper describes recent publications on longitudinal data, the computational challenges, and benefits in power and understanding resulting from appropriate longitudinal analyses.…”