Background Mammalian X and Y chromosomes share a common evolutionary origin and retain regions of high sequence similarity. Similar sequence content can confound the mapping of short next-generation sequencing reads to a reference genome. It is therefore possible that the presence of both sex chromosomes in a reference genome can cause technical artifacts in genomic data and affect downstream analyses and applications. Understanding this problem is critical for medical genomics and population genomic inference. Results Here, we characterize how sequence homology can affect analyses on the sex chromosomes and present XYalign, a new tool that (1) facilitates the inference of sex chromosome complement from next-generation sequencing data; (2) corrects erroneous read mapping on the sex chromosomes; and (3) tabulates and visualizes important metrics for quality control such as mapping quality, sequencing depth, and allele balance. We find that sequence homology affects read mapping on the sex chromosomes and this has downstream effects on variant calling. However, we show that XYalign can correct mismapping, resulting in more accurate variant calling. We also show how metrics output by XYalign can be used to identify XX and XY individuals across diverse sequencing experiments, including low- and high-coverage whole-genome sequencing, and exome sequencing. Finally, we discuss how the flexibility of the XYalign framework can be leveraged for other uses including the identification of aneuploidy on the autosomes. XYalign is available open source under the GNU General Public License (version 3). Conclusions Sex chromsome sequence homology causes the mismapping of short reads, which in turn affects downstream analyses. XYalign provides a reproducible framework to correct mismapping and improve variant calling on the sex chromsomes.

A major goal in evolutionary biology is to understand how natural selection has shaped patterns of genetic variation across genomes. Studies in a variety of species have shown that neutral genetic diversity (intra-species differences) has been reduced at sites linked to those under direct selection. However, the effect of linked selection on neutral sequence divergence (inter-species differences) remains ambiguous. While empirical studies have reported correlations between divergence and recombination, which is interpreted as evidence for natural selection reducing linked neutral divergence, theory argues otherwise, especially for species that have diverged long ago. Here we address these outstanding issues by examining whether natural selection can affect divergence between both closely and distantly related species. We show that neutral divergence between closely related species (e.g. human-primate) is negatively correlated with functional content and positively correlated with human recombination rate. We also find that neutral divergence between distantly related species (e.g. human-rodent) is negatively correlated with functional content and positively correlated with estimates of background selection from primates. These patterns persist after accounting for the confounding factors of hypermutable CpG sites, GC content, and biased gene conversion. Coalescent models indicate that even when the contribution of ancestral polymorphism to divergence is small, background selection in the ancestral population can still explain a large proportion of the variance in divergence across the genome, generating the observed correlations. Our findings reveal that, contrary to previous intuition, natural selection can indirectly affect linked neutral divergence between both closely and distantly related species. Though we cannot formally exclude the possibility that the direct effects of purifying selection drive some of these patterns, such a scenario would be possible only if more of the genome is under purifying selection than currently believed. Our work has implications for understanding the evolution of genomes and interpreting patterns of genetic variation.

Inference of demographic history from genetic data is a primary goal of population genetics of model and nonmodel organisms. Whole genome-based approaches such as the pairwise/multiple sequentially Markovian coalescent methods use genomic data from one to four individuals to infer the demographic history of an entire population, while site frequency spectrum (SFS)-based methods use the distribution of allele frequencies in a sample to reconstruct the same historical events. Although both methods are extensively used in empirical studies and perform well on data simulated under simple models, there have been only limited comparisons of them in more complex and realistic settings. Here we use published demographic models based on data from three human populations (Yoruba, descendants of northwest-Europeans, and Han Chinese) as an empirical test case to study the behavior of both inference procedures. We find that several of the demographic histories inferred by the whole genome-based methods do not predict the genome-wide distribution of heterozygosity, nor do they predict the empirical SFS. However, using simulated data, we also find that the whole genome methods can reconstruct the complex demographic models inferred by SFS-based methods, suggesting that the discordant patterns of genetic variation are not attributable to a lack of statistical power, but may reflect unmodeled complexities in the underlying demography. More generally, our findings indicate that demographic inference from a small number of genomes, routine in genomic studies of nonmodel organisms, should be interpreted cautiously, as these models cannot recapitulate other summaries of the data.

ABSTRACT. Inference of demographic history from genetic data is a primary goal of 48 population genetics of model and non-model organisms. Whole genome-based approaches such 49 as the Pairwise/Multiple Sequentially Markovian Coalescent (PSMC/MSMC) methods use 50 genomic data from one to four individuals to infer the demographic history of an entire 51 population, while site frequency spectrum (SFS)-based methods use the distribution of allele 52 frequencies in a sample to reconstruct the same historical events. Although both methods are 53 extensively used in empirical studies and perform well on data simulated under simple models, 54there have been only limited comparisons of them in more complex and realistic settings. Here 55 we use published demographic models based on data from three human populations (Yoruba 56 (YRI), descendants of northwest-Europeans (CEU), and Han Chinese (CHB)) as an empirical 57 test case to study the behavior of both inference procedures. We find that several of the 58 demographic histories inferred by the whole genome-based methods do not predict the genome-59 wide distribution of heterozygosity nor do they predict the empirical SFS. However, using 60 simulated data, we also find that the whole genome methods can reconstruct the complex 61 demographic models inferred by SFS-based methods, suggesting that the discordant patterns of 62 genetic variation are not attributable to a lack of statistical power, but may reflect unmodeled 63 complexities in the underlying demography. More generally, our findings indicate that 64 demographic inference from a small number of genomes, routine in genomic studies of non-65 model organisms, should be interpreted cautiously, as these models cannot recapitulate other 66

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