Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

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Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.

Mid-pass whole genome sequencing enables biomedical genetic studies of diverse populations

BACKGROUND

Historically, geneticists have relied on genotyping arrays and imputation to study human genetic variation. However, an underrepresentation of diverse populations has resulted in arrays that poorly capture global genetic variation, and a lack of reference panels. This has contributed to deepening global health disparities. Whole genome sequencing (WGS) better captures genetic variation but remains prohibitively expensive. Thus, we explored WGS at "mid-pass" 1-7x coverage.

RESULTS

Here, we developed and benchmarked methods for mid-pass sequencing. When applied to a population without an existing genomic reference panel, 4x mid-pass performed consistently well across ethnicities, with high recall (98%) and precision (97.5%).

CONCLUSION

Compared to array data imputed into 1000 Genomes, mid-pass performed better across all metrics and identified novel population-specific variants with potential disease relevance. We hope our work will reduce financial barriers for geneticists from underrepresented populations to characterize their genomes prior to biomedical genetic applications.

Estimating sequencing error rates using families

BACKGROUND

As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample.

RESULTS

We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method's versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites.

CONCLUSION

Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

Evaluation of Whole-Exome Enrichment Solutions: Lessons from the High-End of the Short-Read Sequencing Scale

Whole-exome sequencing has become a popular technique in research and clinical settings, assisting in disease diagnosis and increasing the understanding of disease pathogenesis. In this study, we aimed to compare common enrichment capture solutions available in the market. Peripheral blood-purified DNA samples were enriched with SureSelect^QXT V6 (Agilent) and various Illumina solutions: TruSeq DNA Nano, TruSeq DNA Exome, Nextera DNA Exome, and Illumina DNA Prep with Enrichment, and sequenced on a HiSeq 4000. We found that their percentage of duplicate reads was as much as 2 times higher than previously reported values for the previous HiSeq series. SureSelect^QXT and Illumina DNA Prep with Enrichment showed the best average on-target coverage, which improved when off-target regions were included. At high coverage levels and in shared bases, these two solutions and TruSeq DNA Exome provided three of the best performances. With respect to the number of small variants detected, SureSelect^QXT presented the lowest number of detected variants in target regions. When off-target regions were considered, its ability equalized to other solutions. Our results show SureSelect^QXT and Illumina DNA Prep with Enrichment to be the best enrichment capture solutions.