By analyzing the allelic frequencies at the D1S80 locus in 43 human populations, we show that the locus is polymorphic globally and that it can be used to discriminate between major racial groups and subpopulations through phylogenetic analysis. Although the use of informative multiple loci generally provides more accurate phylogenetic relationships, in instances where time and/or target DNA availability is limited, D1S80 could provide useful data to discriminate between human groups. Also, knowledge of which loci independently provide accurate phylogenetic relationships, such as the D1S80, can be used to design more accurate multi-locus combinations. In addition, allele frequencies at the locus are reported, for the first time, for Bahamian individuals of African origin and for Chimila, Bari, and Navajo (Cañoncito Valley) native Americans. Allelic data was obtained using standard polymerase chain reaction (PCR) techniques. In the four new populations, 65 genotypes and 20 segregating alleles were observed. All populations conformed to Hardy-Weinberg expectations except the Chimila.

Alu sequences are present in humans in excess of 500,000 copies per haploid genome and represent the largest family of short interspersed repetitive elements (SINEs). These mobile genetic elements are ancestrally derived from the 7SL RNA gene and move throughout the genomes of primates by retroposition. Polymorphic Alu insertions have proven to be useful for population studies, paternity determinations and forensic applications. Additionally, a simple polymerase chain reaction (PCR)-based assay has been established to examine these polymorphisms. In the present study, we have applied the technique of multiplex polymerase chain reaction to the Alu polymorphic system. Duplex and triplex PCR reactions were performed for the analysis of five different Alu polymorphic loci in different combinations. This study represents a starting point for further experimentation to improve and eventually optimize Alu multiplex PCR.

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DNA typing for forensic identification is a two-step process. The first step involves determining the profiles of samples collected at the crime scene and comparing them with the profiles obtained from suspects and the victims. In the case of a match that includes the suspect as the potential source of the material collected at the crime scene, the last step in the process is to answer the question, what is the likelihood that someone in addition to the suspect could match the profile of the sample studied? This likelihood is calculated by determining the frequency of the suspect's profile in the relevant population databases. The design of forensic databases and the criteria for comparison has been addressed by the NRC report of 1996 (National Research Council, 1996). However, the fact that geographical proximity, migrational patterns, and even cultural and social practices have effects on subpopulation structure establishes the grounds for further study into its effects on the calculation of probability of occurrence values. The issue becomes more relevant in the case of discrete polymorphic markers that show higher probability of occurrence in the reference populations, where several orders of magnitude difference between the databases may have an impact on the jury. In this study, we calculated G values for all possible pairwise comparisons of allelic frequencies in the different databases from the races or subpopulations examined. In addition, we analyzed a set of 24 unrelated Caucasian, 37 unrelated African-American, and 96 unrelated Sioux/Chippewa individuals for seven polymorphic loci (DQA1, LDLR, GYPA, HBGG, D7S8, GC, and D1S80). All three sets of individuals where sampled from Minnesota. The probability of occurrence for all seven loci were calculated with respect to nine different databases: Caucasian, Arabic, Korean, Sioux/Chippewa, Navajo, Pueblo, African American, Southeastern Hispanic, and Southwestern Hispanic. Analysis of the results demonstrated marked differences in the probabilities of occurrence when individuals were compared to the different populations and subpopulation databases. The possible genetic and forensic consequences of subpopulation structure on probability calculations are discussed.

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