Jason A. O'Rawe | bioRxiv Science

bioRxiv2015

Whole genome analysis of an extended pedigree with Prader–Willi Syndrome, hereditary hemochromatosis, and dysautonomia-like symptoms

This report includes the discovery and analysis of a pedigree with Prader-Willi Syndrome (PWS), hereditary hemochromatosis (HH), and dysautonomia-like symptoms. Nine members of the family participated in whole genome sequencing (WGS), which enabled a wide scope of variant calling from single-nucleotide polymorphisms to copy number variations. First, a 5.5 Mb de novo deletion is identified in the chromosome region 15q11.2 to 15q13.1 in the boy with PWS. Second, a female invididual with HH is homozygous for the p.C282Y variant in HFE, a mutation known to be associated with HH. Her brother is homozygous for the same variant, although he has yet to be clinically diagnosed with HH. Third, none of the people with dysautonomia-like symptoms carry any reported or novel rare variants in IKBKAP that are implicated in familial dysautonomia (FD - HSAN III). Although two people with dysautonomia-like symptoms carry two heterozygous variants in NTRK1, a gene that has been shown to contribute to HSAN IV (congenital insensitivity to pain with anhidrosis, a disease that closely resembles FD), this variant is not present in the third proband. Fourth, WGS revealed pharmacogenetic variants influencing the metabolism of warfarin and simvastatin, which are being routinely prescribed to the proband. Finally, reports of the phenotypes were standardized with the Human Phenotype Ontology annotation, which may facilitate the search for other families with similar phenotypes. Due to the extreme heterogeneity and insufficient knowledge of human diseases, it is of crucial importance that both phenotypic data and genomic data are standardized and shared.

Genetics↗

bioRxiv2014

Reducing INDEL calling errors in whole-genome and exome sequencing data

BackgroundINDELs, especially those disrupting protein-coding regions of the genome, have been strongly associated with human diseases. However, there are still many errors with INDEL variant calling, driven by library preparation, sequencing biases, and algorithm artifacts.\n\nMethodsWe characterized whole genome sequencing (WGS), whole exome sequencing (WES), and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors. We also developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls. We performed a large-scale validation experiment on 600 loci, and find high-quality INDELs to have a substantially lower error rate than low quality INDELs (7% vs. 51%).\n\nResultsSimulation and experimental data show that assembly based callers are significantly more sensitive and robust for detecting large INDELs (>5 bp) than alignment based callers, consistent with published data. The concordance of INDEL detection between WGS and WES is low (52%), and WGS data uniquely identifies 10.8-fold more high-quality INDELs. The validation rate for WGS-specific INDELs is also much higher than that for WES-specific INDELs (85% vs. 54%), and WES misses many large INDELs. In addition, the concordance for INDEL detection between standard WGS and PCR-free sequencing is 71%, and standard WGS data uniquely identifies 6.3-fold more low-quality INDELs. Furthermore, accurate detection with Scalpel of heterozygous INDELs requires 1.2-fold higher coverage than that for homozygous INDELs. Lastly, homopolymer A/T INDELs are a major source of low-quality INDEL calls, and they are highly enriched in the WES data.\n\nConclusionsOverall, we show that accuracy of INDEL detection with WGS is much greater than WES even in the targeted region. We calculated that 60X WGS depth of coverage from the HiSeq platform is needed to recover 95% of INDELs detected by Scalpel. While this is higher than current sequencing practice, the deeper coverage may save total project costs because of the greater accuracy and sensitivity. Finally, we investigate sources of INDEL errors (e.g. capture deficiency, PCR amplification, homopolymers) with various data that will serve as a guideline to effectively reduce INDEL errors in genome sequencing.

Bioinformatics↗