hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes

Abstract

Complex medical disorders, such as heart disease and diabetes, are thought to involve a number of genes which act in conjunction with lifestyle and environmental factors to increase disease susceptibility. Associations between complex traits and single nucleotide polymorphisms (SNPs) in candidate genomic regions can provide a useful tool for identifying genetic risk factors. However, analysis of trait associations with single SNPs ignores the potential for extra information from haplotypes, combinations of variants at multiple SNPs along a chromosome inherited from a parent. When haplotype-trait associations are of interest and haplotypes of individuals can be determined, generalized linear models (GLMs) may be used to investigate haplotype associations while adjusting for the effects of non-genetic cofactors or attributes. Unfortunately, haplotypes cannot always be determined cost-effectively when data is collected on unrelated subjects. Uncertain haplotypes may be inferred on the basis of data from single SNPs. However, subsequent analyses of risk factors must account for the resulting uncertainty in haplotype assignment in order to avoid potential errors in interpretation. To account for such uncertainty, we have developed hapassoc, software for R implementing a likelihood approach to inference of haplotype and non-genetic effects in GLMs of trait associations. We provide a description of the underlying statistical method and illustrate the use of hapassoc with examples that highlight the flexibility to specify dominant and recessive effects of genetic risk factors, a feature not shared by other software that restricts users to additive effects only. Additionally, hapassoc can accommodate missing SNP genotypes for limited numbers of subjects.

An evaluation of the polymorphisms Ins16bp and Arg72Pro in p53 as breast cancer risk modifiers in BRCA1 and BRCA2 mutation carriers

“…Haplotypes were imputed using the R-package 'hapassoc' (Burkett and McNeney, 2006). Associations of individual haplotypes with time to breast cancer or ovarian cancer diagnosis were evaluated using weighted Cox proportional hazards models, using age as the time variable (Antoniou et al, 2005).…”

Kullback–Leibler divergence for detection of rare haplotype common disease association

“…We carried out a simulation to thoroughly evaluate the performance of hapKL, and to compare it with LBL 6 and hapassoc, 14 both logistic regression modeling based. We perform three sets of simulation: the first is to gauge the type I error rate of hapKL; the second is to compare the performance with LBL and hapassoc; the last simulation is to show that hapKL performs well when the underlying disease model is not additive, a departure from the typical assumption in LBL and hapassoc.…”

“…11 For example, generalized linear model (GLM)-based methods [12][13][14] may encounter non-convergence in its expectation-maximization (EM) estimates when challenged with rare haplotypes. Among such new approaches, the majority use likelihood-based regularization methods (eg, Lasso 15 ) to weed out unassociated haplotypes [3][4][5]7,8 so that those that are associated with the disease, especially the rare ones, can be more precisely estimated for their effects on the trait.…”

Multivariate meta-analysis of the association of G-protein beta 3 gene (GNB3) haplotypes with cardiovascular phenotypes

“…Parameter estimates were obtained by the use of the expectation maximization (EM) algorithm, in which the extra uncertainty due to the unknown phase is taken into account, resulting in wider standard errors [16]. The variance-covariance matrix of regression parameters for each individual study was obtained as discussed previously [17]. For the present analyses, the haplotype containing only non-risk alleles was defined as the reference haplotype [6].…”

“…This occurs because even considerably high effects from uncommon haplotypes may yield meta-analyses with markedly reduced statistical power due to larger uncertainty around point estimates. Furthermore, the construction of useful models is likely to require the specification of a threshold frequency below which uncommon haplotypes are pooled into a single category [17]. In other words, combination of several haplotypes into a single group jeopardizes the interpretation and generalizability of results.…”