Recently, substantial progress has been made in development and application of admixture mapping including derivation of appropriate statistical tools and testing in African American populations. The implementation of this approach in Mexican Americans (MA) has been hampered by the lack of a sufficient density of markers that can provide the ancestry information distinguishing the two major parental populations, Amerindians (Al) and European that contribute to this admixed population. The current proposal will remedy this problem by identification, validation and analysis of a panel of >5000 AI/European American (EA) Ancestry Informative Markers (AIMs) distributed over the genome. The panel of AIMs will be tested using a large collected set of MA DNA samples from type 2 diabetics with nephropathy (750 samples) or without nephropathy (600 samples) and a smaller matched set of nondiabetic MA subjects (300 samples). The utilization of this set will enable both case only and case control designs to be examined using a variety of recently developed algorithms. The first phase of the study (identification of AI/EA AIMs) will utilize an enrichment strategy based on pre-selecting AIMs dependent on putative Chinese/EA AIMs that have been identified in a completed 1.6 x 106 SNP screen. Over 23,000 putative Chinese/EA AIMs with Fsts > 0.45 have been identified and these will be used to examine 48 Pima Amerindian samples. Our previous studies conservatively suggest that >25% of these putative Chinese/EA AIMs will have an AI/EA Fst > 0.4. In the second phase > 5000 AI/EA AIMs will be chosen based on both Fst and chromosomal location to achieve a high density AIMs panel. This will be validated utilizing an additional set of 48 Pima Amerindians in addition to testing two other Al groups (96 samples each) and EA (96 samples). The MA populations will be examined using this panel that is expected to include >4000 validated EA/AI AIMs and analyzed to 1) define chromosomal segments derived from EA and Al, 2) perform modeling studies and 3) identify putative diabetes and diabetic nephropathy associated regions distinguishing the major ancestry. Finally, the three strongest signals will be further investigated utilizing a dense set of SNPs in the putative susceptibility regions. ? ?
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