scholarly journals Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

2019 ◽  
Author(s):  
Zilin Li ◽  
Xihao Li ◽  
Yaowu Liu ◽  
Jincheng Shen ◽  
Han Chen ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Type I Error ◽  
Rare Variants ◽  
Error Rates ◽  
Type I ◽  
Whole Genome ◽  
Rare Variant Association ◽  
Dynamic Scan

AbstractWhole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

scholarly journals Whole Genome Rare-Variant Association Study of HIV-1 Progression in a Southern African Population

2020 ◽  
Author(s):  
Prisca K. Thami ◽  
Wonderful Choga ◽  
Delesa D. Mulisa ◽  
Collet Dandara ◽  
Andrey K. Shevchenko ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Rare Variants ◽  
Variant Calling ◽  
Analysis Tool ◽  
Whole Genome ◽  
Host Genetics ◽  
Hiv 1

ABSTRACTDespite the high burden of HIV-1 in Botswana, the population of Botswana is significantly underrepresentation in host genetics studies of HIV-1. Furthermore, the bulk of previous genomics studies evaluated common human genetic variations, however, there is increasing evidence of the influence of rare variants in the outcome of diseases which may be uncovered by comprehensive complete and deep genome sequencing. This research aimed to evaluate the role of rare-variants in susceptibility to HIV-1 and progression through whole genome sequencing. Whole genome sequences (WGS) of 265 HIV-1 positive and 125 were HIV-1 negative unrelated individuals from Botswana were mapped to the human reference genome GRCh38. Population joint variant calling was performed using Genome Analysis Tool Kit (GATK) and BCFTools. Cumulative effects of rare variant sets on susceptibility to HIV-1 and progression (CD4+ T-cell decline) were determined with optimized Sequence Kernel Association Test (SKAT-O). In silico functional analysis of the prioritized variants was performed through gene-set enrichment using databases in GeneMANIA and Enrichr. Novel rare-variants within the ANKRD39 (8.48 × 10−8), LOC105378523 (7.45 × 10−7) and GTF3C3 (1.36 × 10−6) genes were significantly associated with HIV-1 progression. Functional analysis revealed that these genes are involved in viral translation and transcription. These findings highlight the significance of whole genome sequencing in pinpointing rare-variants of clinical relevance. The research contributes towards a deeper understanding of the host genetics HIV-1 and offers promise of population specific interventions against HIV-1.

scholarly journals A permutation method for detecting trend correlations in rare variant association studies

2019 ◽  
Vol 101 ◽  
Author(s):  
Lifeng Liu ◽  
Pengfei Wang ◽  
Jingbo Meng ◽  
Lili Chen ◽  
Wensheng Zhu ◽  
...  
Keyword(s):  
Rare Variant ◽  
Type I Error ◽  
Rare Variants ◽  
Association Studies ◽  
Complex Diseases ◽  
Type I ◽  
Phenotypic Variance ◽  
Rare Variant Association ◽  
Significance Level ◽  
Association Analyses

Abstract In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

scholarly journals Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits

2018 ◽  
Author(s):  
Arthur Gilly ◽  
Daniel Suveges ◽  
Karoline Kuchenbaecker ◽  
Martin Pollard ◽  
Lorraine Southam ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Complex Traits ◽  
Rare Variants ◽  
Whole Genome ◽  
Isolated Population ◽  
Gamma Glutamyltransferase ◽  
Cardiometabolic Traits

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1,457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens overlapping with, and mostly independent of established common variant signals (ADIPOQ and adiponectin, P=4.2×10−8; APOC3 and triglyceride levels, P=1.58×10−26; GGT1 and gamma-glutamyltransferase, P=2.3×10−6; UGT1A9 and bilirubin, P=1.9×10−8), and identify replicating evidence for a burden associated with triglyceride levels in FAM189A (P=2.26×10−8), indicating a role for this gene in lipid metabolism.

scholarly journals STAAR Workflow: A cloud-based workflow for scalable and reproducible rare variant analysis

2021 ◽  
Author(s):  
Sheila M. Gaynor ◽  
Kenneth E. Westerman ◽  
Lea L. Ackovic ◽  
Xihao Li ◽  
Zilin Li ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Functional Annotations ◽  
Rare Variant Analysis ◽  
Variant Analysis

AbstractSummaryWe developed the STAAR WDL workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the workflow description language (WDL) allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate, and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline.Availability and implementationThe workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow.

scholarly journals Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

2017 ◽  
Author(s):  
Pradeep Natarajan ◽  
Gina M. Peloso ◽  
S. Maryam Zekavat ◽  
May Montasser ◽  
Andrea Ganna ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Whole Genome ◽  
Rare Variant Association ◽  
Coding Sequence ◽  
Polygenic Score ◽  
Coding Regions

Deep-coverage whole genome sequencing at the population level is now feasible and offers potential advantages for locus discovery, particularly in the analysis rare mutations in non-coding regions. Here, we performed whole genome sequencing in 16,324 participants from four ancestries at mean depth >29X and analyzed correlations of genotypes with four quantitative traits – plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. We conducted a discovery analysis including common or rare variants in coding as well as non-coding regions and developed a framework to interpret genome sequence for dyslipidemia risk. Common variant association yielded loci previously described with the exception of a few variants not captured earlier by arrays or imputation. In coding sequence, rare variant association yielded known Mendelian dyslipidemia genes and, in non-coding sequence, we detected no rare variant association signals after application of four approaches to aggregate variants in non-coding regions. We developed a new, genome-wide polygenic score for LDL-C and observed that a high polygenic score conferred similar effect size to a monogenic mutation (~30 mg/dl higher LDL-C for each); however, among those with extremely high LDL-C, a high polygenic score was considerably more prevalent than a monogenic mutation (23% versus 2% of participants, respectively).

scholarly journals Estimating sequencing error rates using families

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Variant Calling ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Whole Exome

Abstract 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.

scholarly journals Isolation and Identification of Kidney-derived Pathogenic Escherichia coli from Red Panda (Ailurus fulgens)

2020 ◽  
Author(s):  
Songrui Liu ◽  
Yunli Li ◽  
Chanjuan Yue ◽  
Dongsheng Zhang ◽  
Xiaoyan Su ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Bacterial Infection ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Ex Situ ◽  
Type I ◽  
Pathogenicity Test ◽  
Whole Genome ◽  
Red Panda ◽  
Ailurus Fulgens

Abstract Background Disease prevention and control is a significant part during the ex-situ conservation of the red panda (Ailurus fulgens) with bacterial infection being one of the important threats to the health of the captive population. So far, there was no systematic and detailed publications about the red panda-related E. coli disease. This study was conducted for the purpose of determining the cause of death, etiology and pathogenesis on a red panda through clinical symptoms, complete blood count, biochemical analysis, pathological diagnosis, antimicrobial susceptibility test, mouse pathogenicity test, and bacterial whole genome sequencing.Results A bacterial strain confirmed as Uropathogenic Escherichia coli (UPEC) was isolated from one captive dead red panda, which is resistant to most of the β-lactam drugs and a small number of aminoglycoside medications. The mouse pathogenicity test results showed the strains isolated postmortem from mice were the same as from the dead red panda, and the pathological findings were similar to the red panda while they were not completely the same. These pathological differences between red panda and mice may be related to the routes of infection and perhaps species differences and tolerance. The whole genome sequencing results showed that the isolated strain contained P pili, type I pili and iron uptake system related factors, which were closely related to its nephrotoxicity. Conclusion The red panda died of bacterial infection which was identified as Uropathogenic Escherichia coli. The pathogenic mechanisms of the strain are closely related to the expression of specific virulence genes.

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