Abstract
For mapping complex disease traits, linkage studies are often followed by a case-control association strategy in order to identify disease-associated genes/single-nucleotide polymorphisms (SNPs). Substantial efforts are required in selecting the most informative cases from a large collection of affected individuals in order to maximize the power of the study, while taking into consideration study cost. In this article, we applied and extended three case-selection strategies that use allele-sharing information method for families with multiple affected offspring to select most informative cases using additional information on disease severity. Our results revealed that most significant associations, as measured by the lowest p-values, were obtained from a strategy that selected a case with the most allele sharing with other affected sibs from linked families ("linked-best"), despite reduction in sample size resulting from discarding unlinked families. Moreover, information on disease severity appears to be useful to improve the ability to detect associations between markers and disease loci.
Highlights
Linkage analyses are often the first step in mapping genes for complex traits
Once regions linked to the trait were identified, 34 additional single-nucleotide polymorphisms (SNPs) were included under each of the linkage peaks: packets 417 and 418 under the chromosome 9 linkage peak and packets 28 and 29 under the linkage peak on chromosome 1
Fingerlin et al [4] compared three case-selection strategies that use allele-sharing information with the standard strategy that selects a single individual from each family at random. We extended these strategies by using additional information on disease severity
Summary
Linkage analyses are often the first step in mapping genes for complex traits. Such methods typically implicate broad regions of the genome, and identifying causal genes remains a challenge. One may want to follow a linkage peak with a case-control study to identify the exact causal variants in a region implicated by linkage analysis. To reduce the cost and increase the power of detecting a disease-marker association, different strategies have been exploited previously to identify genetically "loaded" individuals by choosing subjects with history of disease [1], a more severe form of disease [2], or early onset of disease [3] to increase the chance of detecting genetic risk factors in a population. Our goal is to choose individuals who maximize the expected difference in allele frequency between cases and controls
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