Michelle Kwong

Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Background: Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to detect ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. Results: We report a weighted likelihood approach for identifying ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to detect ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. Conclusions: This weighted likelihood ROA detection approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Microsatellites---contiguous arrays of 2–6 base-pair motifs---have formed the cornerstone of population-genetic studies for over two decades. Their genotype data typically takes the form of PCR fragment lengths obtained using locus-specific primer pairs to amplify the genomic region encompassing the microsatellite. Recently, we reported a dataset of 5,795 human and 84 chimpanzee individuals with genotypes at 246 human-derived autosomal microsatellites as a resource to facilitate interspecies comparisons. A major assumption underlying this dataset is that PCR amplicons at orthologous microsatellites are commensurable between species. We find this assumption to be frequently incorrect owing to discordance in microsatellite organization and variability, as well as nontrivial length imbalances caused by small species-specific indels in microsatellite flanking sequences. Converting PCR fragment lengths into the repeat numbers they represent at 138 microsatellites whose organization and variability was found to be highly similar in both species, we show that interspecies incommensurability among PCR amplicons can inflate FST and DPS estimates by up to 10.6%. Separate investigations of determinants of microsatellite variability in humans and chimpanzees uncover similar patterns with mean and maximum numbers of repeats, as well as numbers and ranges of distinct alleles, all important factors in predicting heterozygosity. In contrast, across microsatellites, numbers of repeats were significantly smaller in chimpanzees than in humans, while numbers and ranges of distinct alleles were instead larger. Our findings have fundamental implications for interspecies comparisons using microsatellites and offer new opportunities for more accurate comparisons of patterns of human and chimpanzee genetic variation in numerous areas of application.