Given the DNA fingerprints of two individuals with some bands being shared by both individuals, we define a new measure of the degree of similarity between the DNA profiles of two individuals. We use this measure to calculate the expected DNA similarity of two unrelated individuals of a randomly mating population; this similarity is due to chance only. Then, the expected similarity between two related individuals is obtained; this similarity is due to chance andrelatedness. From these results, the degree of similarity due to relatedness alone may be calculated.

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The affected-pedigree-member (APM) method of linkage analysis is a nonparametric statistic for testing for nonindependent segregation of a marker to affected members of a pedigree. We present here results of a simulation study evaluating the power of the APM method to detect linkage. We have systematically explored, by computer simulation, the effect of a variety of factors on the power to detect linkage using the single-locus APM statistic. These factors include mode of inheritance, marker polymorphism, the distance between marker and disease, phenocopy rate, heterogeneity, and misspecified marker allele frequencies. We also evaluated the relative power obtained under fixed-structure sampling and sequential sampling. For a dominant disease, sequential sampling led to increased power as compared to fixed-structure sampling, while for a recessive disease, there was no clear advantage in sampling beyond nuclear families.

Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large ( n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in Admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study (GWAS) data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide binding groove, explaining 12.9% of trait variance.