Ancestral inference from DNA could serve as an important adjunct for both standard and future human identity testing procedures. However, current STR methods for the inference of ancestral affiliation have inherent statistical and technical limitations. In an effort to identify biallelic markers that can be used to infer ancestral affiliation from DNA, we screened 211 SNPs in the human pigmentation and xenobiotic metabolism genes. Allele frequencies of 56 SNPs (most from pigmentation genes) were dramatically different between groups of unrelated individuals of Asian, African, and European descent, and both observed and simulated log likelihood ratios revealed that the markers were of exceptional value for ancestral inference. Log likelihood ratios of the multilocus estimates of biological ancestry (EAE/EBA) ranged from 7 to 10, which are on par with the best of the STR batteries yet described. A linear classification method was developed for incorporating these SNPs into a classifier model that was 99, 98, and 100% accurate for identifying individuals of European, African, and Asian descent, respectively. The methods and markers we describe are therefore an important first step for the development of a practical multiplex test for the inference of ancestry in a forensics setting.

To determine whether and how common polymorphisms are associated with natural distributions of iris colors, we surveyed 851 individuals of mainly European descent at 335 SNP loci in 13 pigmentation genes and 419 other SNPs distributed throughout the genome and known or thought to be informative for certain elements of population structure. We identified numerous SNPs, haplotypes, and diplotypes (diploid pairs of haplotypes) within the OCA2, MYO5A, TYRP1, AIM, DCT, and TYR genes and the CYP1A2-15q22-ter, CYP1B1-2p21, CYP2C8-10q23, CYP2C9-10q24, and MAOA-Xp11.4 regions as significantly associated with iris colors. Half of the associated SNPs were located on chromosome 15, which corresponds with results that others have previously obtained from linkage analysis. We identified 5 additional genes (ASIP, MC1R, POMC, and SILV) and one additional region (GSTT2-22q11.23) with haplotype and/or diplotypes, but not individual SNP alleles associated with iris colors. For most of the genes, multilocus gene-wise genotype sequences were more strongly associated with iris colors than were haplotypes or SNP alleles. Diplotypes for these genes explain 15% of iris color variation. Apart from representing the first comprehensive candidate gene study for variable iris pigmentation and constituting a first step toward developing a classification model for the inference of iris color from DNA, our results suggest that cryptic population structure might serve as a leverage tool for complex trait gene mapping if genomes are screened with the appropriate ancestry informative markers.

Background We deal with genetic complexity through population structure to reduce the number of markers, condition case/control study results and combat the confounding influence of genetic heterogeneity and minor locus effects. This abstract describes the screening of patient genomes to construct a DNA based test for measuring Taxol/Carboplatin response proclivities. Methods Pan genome maps of Ancestry Informative Markers to estimate individual biogeographical ancestry admixture for patient samples, integrated with a case/control design were used to screen the genome. Results Taxol/Carboplatin response was associated with Haplotypes in 3 xenobiotic metabolism genes and a few other SNPs. Integrating ancestry and gene‐specific features enabled construction of a classification method for predicting Taxol/Carboplatin response proclivities that is capable of relatively sensitive, specific and powerful performance in “blind” sample classification trials. Conclusions When gene haplotype and SNP associations are viewed through the lens of biogeographical ancestry admixture, pattern emerges that enables relatively accurate classification of patient response proclivities to Taxol/Carboplatin in ovarian cancer patients. Clinical Pharmacology & Therapeutics (2005) 77, P61–P61; doi:

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