Identifying, understanding, and correcting technical artifacts on the sex chromosomes in next-generation sequencing data

Abstract

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.

Identification of Novel Alzheimer’s Disease Loci Using Sex-Specific Family-Based Association Analysis of Whole-Genome Sequence Data

“…However, in the context of rare-variant data, family-based studies face one major hurdle: they are sensitive to genotyping/sequencing errors. In the context of sex-specific analyses, this issue is further aggravated as many genetic regions show sequence homology with the X-chromosome 29 . This can lead to differential genotyping error rates for females and males due to different X chromosome dosage.…”

“…This can lead to differential genotyping error rates for females and males due to different X chromosome dosage. Ignoring the impact of such sex-specific genotyping/sequencing errors can lead to substantially inflated type-1 errors 29 .…”

X chromosome inactivation in the human placenta is patchy and distinct from adult tissues

“…Specifically, we mapped the exome samples to a sex chromosome complement informed reference genome in which the Y chromosome is hard-masked (to avoid mismapping of X-linked reads to homologous regions on the Y chromosome in the XX samples). To generate the sex chromosome complement reference genome we employed XYalign (Webster et al 2019). XYalign created a Y-masked gencode GRCh38.p12 human reference genome for aligning XX individuals (Harrow et al 2012).…”

“…Post trimming quality was checked using fastqc version 0.11.8 (Andrews 2010) and multiqc version 0.9 (Ewels et al 2016) ( Figure S2, D, E, and F). Trimmed RNAseq reads were then aligned to a sex chromosome complement informed reference genome with the Y-masked (Webster et al 2019;Olney et al 2019) gencode GRCh38.p12 reference genome (Harrow et al 2012). Total reads mapped and duplicate reads were visually checked using BAMtools stats (Barnett et al 2011) (Table S3).…”

Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients

“…the sexual chromosomes. Alignment of short reads in such genomic regions is typically challenging and presence of these artefacts is likely 5 due to mapping issues ( Figure S1) [20]. The fact that most of the variations observed in healthy individuals are shared among samples is another indication that they could represent artefacts ( Figure S1).…”