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- All Subjects: Genetic Diversity
- All Subjects: Sequencing
- Creators: Stone, Anne
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
Description
Unlike the autosomes, recombination on the sex chromosomes is limited to the pseudoautosomal regions (PARs) at each end of the chromosome. PAR1 spans approximately 2.7 Mb from the tip of the proximal arm of each sex chromosome, and a pseudoautosomal boundary between the PAR1 and non-PAR region is thought to have evolved from a Y-specific inversion that suppressed recombination across the boundary. In addition to the two PARs, there is also a human-specific X-transposed region (XTR) that was duplicated from the X to the Y chromosome. Genetic diversity is expected to be higher in recombining than nonrecombining regions, particularly because recombination reduces the effects of linked selection, allowing neutral variation to accumulate. We previously showed that diversity decreases linearly across the previously defined pseudoautosomal boundary (rather than drop suddenly at the boundary), suggesting that the pseudoautosomal boundary may not be as strict as previously thought. In this study, we analyzed data from 1271 genetic females to explore the extent to which the pseudoautosomal boundary varies among human populations (broadly, African, European, South Asian, East Asian, and the Americas). We found that, in all populations, genetic diversity was significantly higher in the PAR1 and XTR than in the non-PAR regions, and that diversity decreased linearly from the PAR1 to finally reach a non-PAR value well past the pseudoautosomal boundary in all populations. However, we also found that the location at which diversity changes from reflecting the higher PAR1 diversity to the lower nonPAR diversity varied by as much as 500 kb among populations. The lack of genetic evidence for a strict pseudoautosomal boundary and the variability in patterns of diversity across the pseudoautosomal boundary are consistent with two potential explanations: (1) the boundary itself may vary across populations, or (2) that population-specific demographic histories have shaped diversity across the pseudoautosomal boundary.
ContributorsCotter, Daniel Juetten (Author) / Wilson Sayres, Melissa (Thesis director) / Stone, Anne (Committee member) / Webster, Timothy (Committee member) / School of Life Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Analyzing human DNA sequence data allows researchers to identify variants associated with disease, reconstruct the demographic histories of human populations, and further understand the structure and function of the genome. Identifying variants in whole genome sequences is a crucial bioinformatics step in sequence data processing and can be performed using multiple approaches. To investigate the consistency between different bioinformatics methods, we compared the accuracy and sensitivity of two genotyping strategies, joint variant calling and single-sample variant calling. Autosomal and sex chromosome variant call sets were produced by joint and single-sample calling variants for 10 female individuals. The accuracy of variant calls was assessed using SNP array genotype data collected from each individual. To compare the ability of joint and single-sample calling to capture low-frequency variants, folded site frequency spectra were constructed from variant call sets. To investigate the potential for these different variant calling methods to impact downstream analyses, we estimated nucleotide diversity for call sets produced using each approach. We found that while both methods were equally accurate when validated by SNP array sites, single-sample calling identified a greater number of singletons. However, estimates of nucleotide diversity were robust to these differences in the site frequency spectrum between call sets. Our results suggest that despite single-sample calling’s greater sensitivity for low-frequency variants, the differences between approaches have a minimal effect on downstream analyses. While joint calling may be a more efficient approach for genotyping many samples, in situations that preclude large sample sizes, our study suggests that single-sample calling is a suitable alternative.
ContributorsHowell, Emma (Co-author) / Wilson, Melissa (Thesis director) / Stone, Anne (Committee member) / Phung, Tanya (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05