Enhanced linkage maps from family-based genetics studies

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DESCRIPTION (provided by applicant): Meiotic linkage maps are the foundation of both linkage and linkage disequilibrium studies for mapping disease genes. Despite the importance of precise maps, existing genome-wide linkage maps were built using only a small collection of pedigrees, and so have wide confidence intervals surrounding estimates of map distance. Incorrect marker order and map distances can have a profound effect on linkage analyses. Using a sex-averaged map instead of a sex-specific map biases the lod scores upward, markedly increasing the false positive rate. Since it is very costly to follow-up many false-positive results, there is a clear need for more precise and accurate sex-specific genetic maps. Accurate estimates of meiotic map distance cannot be obtained by any means other than by linkage analysis using genotype data. We propose to build improved highly-precise sex-specific linkage maps utilizing thousands of individuals who have previously been genotyped. After filtering out obvious relationship and genotype errors, we will incorporate methods that properly model for genotyping errors. In addition to creating precise maps for the scientific community, we also propose to use these genotype data to study how recombination may vary between ethnic groups. The genotypes generated by the NHLBI Mammalian Genotyping Service are precisely the type of data required to produce more accurate maps. These data collections contain over 3,400 pedigrees with more than a 1 00-fold increase in information compared to that contained in the 8 CEPH families that have been used to construct current genome-wide linkage maps. Our new maps will be made publicly available and the genotype data from our study will be accessible by the MAP-0-MAT linkage mapping server. In the future, we anticipate broadening our study to incorporate genotype data from additional genotyping centers such as the Center for Inherited Disease Research (CIDR). The inaccuracies present in current maps can contribute to misleading results, and may be one of the reasons that disappointingly few genes have been definitively identified that contribute to such complex diseases as asthma, cardiovascular disease, hypertension, hypercholesterolemia, diabetes, obesity, and cancer. The more precise maps that we propose to construct will improve the power and value of many ongoing and new disease studies.