hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes
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 ge...
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doaj-80253256750b4711a5128a2c906d87cf2020-11-25T00:29:14ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602006-04-01162hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other AttributesKelly BurkettJinko GrahamBrad McNeneyComplex 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 sub jects. 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.http://www.jstatsoft.org/v16/i02/papergenetic associationsingle-nucleotide polymorphismshaplotypesgeneralized linear |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kelly Burkett Jinko Graham Brad McNeney |
spellingShingle |
Kelly Burkett Jinko Graham Brad McNeney hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes Journal of Statistical Software genetic association single-nucleotide polymorphisms haplotypes generalized linear |
author_facet |
Kelly Burkett Jinko Graham Brad McNeney |
author_sort |
Kelly Burkett |
title |
hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes |
title_short |
hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes |
title_full |
hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes |
title_fullStr |
hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes |
title_full_unstemmed |
hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes |
title_sort |
hapassoc: software for likelihood inference of trait associations with snp haplotypes and other attributes |
publisher |
Foundation for Open Access Statistics |
series |
Journal of Statistical Software |
issn |
1548-7660 |
publishDate |
2006-04-01 |
description |
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 sub jects. 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. |
topic |
genetic association single-nucleotide polymorphisms haplotypes generalized linear |
url |
http://www.jstatsoft.org/v16/i02/paper |
work_keys_str_mv |
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