Genome-wide autozygosity mapping in human populations.

Individuals are frequently observed to have long segments of uninterrupted sequences of homozygous markers. One of the major mechanisms that gives rise to such long homozygous segments is consanguineous marriages, where parents pass shared chromosomal segments to their child. Such chromosomal segments are also known as autozygous segments. The clinical evidence that progeny from inbred individuals may have reduced health and fitness because of homozygosity of recessive alleles is well known. As the length of such homozygous segments depends on the degree of parental consanguinity, it would be logical to observe shorter homozygous segments in more outbred populations. However, a recent study identified long homozygous regions, thus likely to be autozygous segments in the HapMap populations. While an abundance of homozygous segments may significantly reduce the ability to fine map disease genes using association studies, detecting tracts of extended homozygosity related to disease status seems the natural next step in genome-wide association studies beyond allele, genotype and haplotype association analyses. In this study, we propose a new algorithm to map disease-related segments based on autozygosity using case-control data. The underlying rationale for the proposed method is that shared autozygosity regions that differ between diseased and healthy individuals may harbor mutations underlying diseases. Specifically, our algorithm uses a sliding-window framework and employs a logarithm of the odds score measure of autozygosity coupled with permutation-based methods to identify disease-related regions. We illustrate the advantage of the algorithm with its application to a genome-wide association study on Parkinson's disease.