Abstract

BackgroundAssociations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors.ResultsTo address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available.ConclusionsResults from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods.

Highlights

  • Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases

  • Association analysis based on haplotypes tends to be more powerful than the analysis of individual SNPs, especially when the causal SNPs are not directly typed or when multiple mutations occur in the cis position [2,3,4,5,6]

  • The ambiguity of the gametic phase information poses a major challenge in the haplotype analysis

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Summary

Results

Simulation studies We conducted extensive simulation studies to assess the performance of the new methods in realistic settings. In the first set of simulation studies, we evaluated the performance of the new method for haplotype association analysis. The loss of power for the new method caused by missing genotypes is rather moderate, even when there is substantial missingness These results suggest that the new method can effectively infer the haplotype configuration and is efficient in dealing with missing genotype data. We studied the problem of untyped SNPs. We considered the same model as in the above simulation studies with missing genotype data. CAMP study We used the new method to study associations between asthma phenotypes and eight SNPs in the Beta2Adrenergic Receptor (β2AR) with data from the CAMP Ancillary Genetic Study and compared the results to those of the haplotype FBAT. The chisquare test statistic from the haplotype FBAT was 6.98, with a p-value of 0.32

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Background
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