Abstract
ABSTRACTAutozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution.
Highlights
Autozygosity mapping using individuals from complex consanguineous families [Lander and Botstein, 1987; Mueller and Bishop, 1993] has been used to localize the genes causing many rare, autosomal recessive diseases
Exome sequencing is a powerful method for the detection of deleterious variants
Due to the volume of data generated by each experiment, identifying a disease variant can be very difficult without secondary information
Summary
Autozygosity mapping using individuals from complex consanguineous families [Lander and Botstein, 1987; Mueller and Bishop, 1993] has been used to localize the genes causing many rare, autosomal recessive diseases. The pace at which autozygous regions could be delineated was a significant hurdle for new mapping projects. With the advent of exome sequencing, the practical aspects of variant detection became facile, while bioinformatic analysis became more complex. The latter required computer programs that could both detect sequence variants and filter them against a number of specified parameters, including position, predicted effect, quality score, and genotype [Li et al, 2009; McKenna et al, 2010; Watson et al, 2014]
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