Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Computational and statistical approaches to analyzing variants identified by exome sequencing. | Stitziel et al. Genome Biology 2011 12 227 http 2011 12 9 227 w Genome Biology REVIEW Computational and statistical approaches to analyzing variants identified by exome sequencing Nathan O Stitziel1 2 Adam Kiezun 2 3t and Shamil Sunyaev2 3 Abstract New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome and many computational and statistical approaches to identify disease-association signals have emerged. From quantitative trait locus mapping and linkage analysis to genome-wide association studies GWASs genetic markers have been used to locate causal genes underlying Mendelian and complex traits with impressive success the molecular basis for nearly 3 000 Mendelian disorders is known 1 and over 4 500 single nucleotide polymorphisms SNPs have been associated with a variety of human traits and complex diseases 2 . These studies rely on linkage with the disease-causing variant and by their very nature indirect genetic marker studies have limitations. The causal variant or gene remains unknown for the majority of the 4 500 SNPs associated with complex disease and for over 3 500 Mendelian disorders. New sequencing-based studies have emerged and are poised to change genetic mapping fundamentally by enabling the direct identification of causal sequence variants in a single experiment. We will no longer have to rely on linkage with the disease-causing variant instead by obtaining full sequence data for all genes we can now directly test for association with disease. As we have learned in the past few years however there is a great deal of human genetic variation 3 and finding the causal variant among thousands of candidates can be difficult. Here we review the computational and statistical approaches that have emerged for managing these data in this rapidly exploding field. First we briefly review the Correspondence ssunyaev@ 2Program in Medical and Population Genetics Broad
