Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations

Ar, Martin; Atkinson, Elizabeth; Chapman, Sinéad B.; Stevenson, Anne; Stroud, Rocky E.; Abebe, Tamrat; Akena, Dickens; Alemayehu, Melkam; Ashaba, Fred K.; Atwoli, Lukoye; Bowers, Tera; Chibnik, Lori B.; Daly, Mark J.; DeSmet, Timothy; Dodge, Sheila; Fekadu, Abebaw; Ferriera, Steven; Gelaye, Bizu; Gichuru, Stella; Injera, Wilfred E.; James, Roxanne; Kariuki, Symon M.; Kigen, Gabriel; Koenen, Karestan C.; Kwobah, Edith; Kyebuzibwa, Joseph; Majara, Lerato; Musinguzi, Henry; Mwema, Rehema M.; Neale, Benjamin M.; Newman, Carter P.; Newton, Charles R.; Pickrell, Joseph K.; Ramesar, Raj; Shiferaw, Welelta; Stein, Dan J.; Teferra, Solomon; Merwe, Celia van der; Zingela, Zukiswa

doi:10.1016/j.ajhg.2021.03.012

Cited by 50 publications

(42 citation statements)

References 38 publications

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“…In particular, we found that ancestry scores were imputed with near perfect accuracy, providing a framework to easily incorporate genetic ancestry into the study of tumors from existing large-scale datasets and expand our knowledge of population-specific mechanisms[44]. Multiple emerging studies have demonstrated the utility of low-coverage sequencing of normal samples[17,21,22] and our work extends these findings to tumor-only sequencing data.…”

Section: Discussionmentioning

confidence: 67%

“…Recent work has shown that ultra low-coverage sequencing, including off-target regions from targeted sequencing, can be used to accurately impute common germline polymorphisms by leveraging linkage disequilibrium (LD) information within the low-coverage data[16–22]. Here, we demonstrate that similar techniques can be used to infer common germline variation from targeted sequencing of tumors.…”

Section: Introductionmentioning

confidence: 84%

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Constructing germline research cohorts from the discarded reads of clinical tumor sequences

Gusev

Groha

Taraszka

et al. 2021

Preprint

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Background: Hundreds of thousands of cancer patients have had targeted (panel) tumor sequencing to identify clinically meaningful mutations. In addition to improving patient outcomes, this activity has led to significant discoveries in basic and translational domains. However, the targeted nature of clinical tumor sequencing has a limited scope, especially for germline genetics. In this work, we assess the utility of discarded, off-target reads from tumor-only panel sequencing for recovery of genome-wide germline genotypes through imputation. Methods: We develop a framework for inference of germline variants from tumor panel sequencing, including imputation, quality control, inference of genetic ancestry, germline polygenic risk scores, and HLA alleles. We benchmark our framework on 833 individuals with tumor sequencing and matched germline SNP array data. We then apply our approach to a prospectively collected panel sequencing cohort of 25,889 tumors. Results: We demonstrate high to moderate accuracy of each inferred feature relative to direct germline SNP array genotyping: individual common variants were imputed with a mean accuracy (correlation) of 0.86; genetic ancestry was inferred with a correlation of >0.98; polygenic risk scores were inferred with a correlation of >0.90; and individual HLA alleles were inferred with correlation of >0.89. We demonstrate a minimal influence on accuracy of somatic copy number alterations and other tumor features. We showcase the feasibility and utility of our framework by analyzing 25,889 tumors and identifying relationships between genetic ancestry, polygenic risk, and tumor characteristics that could not be studied with conventional data. Conclusions: We conclude that targeted tumor sequencing can be leveraged to build rich germline research cohorts from existing data, and make our analysis pipeline publicly available to facilitate this effort.

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Section: Discussionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 84%

Constructing germline research cohorts from the discarded reads of clinical tumor sequences

Gusev

Groha

Taraszka

et al. 2021

Preprint

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“…Previously, genotype concordances between low-coverage (~ 0.5 ×) and genotype array, and deep sequencing data (~ 30 ×) were highly correlated, and several studies have continuously demonstrated potentials of LPS for precision medicine [ 4 , 6 ]. Additionally, LPS under 1.0 × has shown strong advantages over genotype array in terms of cost and imputation accuracy [ 24 , 30 ]. Typically, LPS around 1.0 × is expected to be half of the cost of genotype array with less than 1 million variants.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson’s disease

Kim

Shin²,

Kwon³

et al. 2021

Hum Genomics

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Background Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD. Results Using eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered. Conclusions Overall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.

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“…As a result, disease risks can be mis-inferred and not yield accurate estimations depending on populations, highlighting the need of taking into account ancestry of study participants. Moreover, cost-effective alternatives to genotyping arrays, such as low-coverage sequencing (≥4X), have been shown to capture variants at all frequencies more precisely and to identify novel variation in underrepresented populations, as Africans ( Martin et al, 2021 ). The rest of the studies we have included (9) are based either on a single or on more omics technologies.…”

Section: Large Data Collections For Aging: a Survey Of Available Databases And Datasetsmentioning

confidence: 99%

Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

et al. 2021

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BackgroundAging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration.Recent AdvancesIn this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today.Critical IssuesAlthough the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types.Future DirectionsWe critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.

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Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations

Cited by 50 publications

References 38 publications

Constructing germline research cohorts from the discarded reads of clinical tumor sequences

Constructing germline research cohorts from the discarded reads of clinical tumor sequences

Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson’s disease

Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

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