Faculty Opinions recommendation of Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

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.

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

10.1101/2021.09.07.456116 ◽

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.

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

10.1101/224378 ◽

2017 ◽

Cited By ~ 2

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).

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A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

10.1101/2021.11.05.467531 ◽

2021 ◽

Author(s):

Zilin Li ◽

Xihao Li ◽

Hufeng Zhou ◽

Sheila M Gaynor ◽

Margaret Sunitha Selvaraj ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Association Analysis ◽

Genome Sequencing ◽

Large Scale ◽

Rare Variants ◽

Whole Genome ◽

Computationally Efficient ◽

Annotation Information ◽

Sequencing Studies ◽

Complex Human Traits

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants' (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

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Genetic Characterization of Brucella spp.: Whole Genome Sequencing-Based Approach for the Determination of Multiple Locus Variable Number Tandem Repeat Profiles

Frontiers in Microbiology ◽

10.3389/fmicb.2021.740068 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ana Pelerito ◽

Alexandra Nunes ◽

Teresa Grilo ◽

Joana Isidro ◽

Catarina Silva ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

In Silico ◽

Variable Number Tandem Repeat ◽

Epidemiological Studies ◽

Variable Number ◽

Epidemiological Surveillance ◽

Future Application ◽

Whole Genome

Brucellosis is an important zoonosis that is emerging in some regions of the world, gaining increased relevance with the inclusion of the causing agent Brucella spp. in the class B bioterrorism group. Until now, multi-locus VNTR Analysis (MLVA) based on 16 loci has been considered as the gold standard for Brucella typing. However, this methodology is laborious, and, with the rampant release of Brucella genomes, the transition from the traditional MLVA to whole genome sequencing (WGS)-based typing is on course. Nevertheless, in order to avoid a disruptive transition with the loss of massive genetic data obtained throughout the last decade and considering that the transition timings will vary considerably among different countries, it is important to determine WGS-based MLVA alleles of the nowadays sequenced genomes. On this regard, we aimed to evaluate the performance of a Python script that had been previously developed for the rapid in silico extraction of the MLVA alleles, by comparing it to the PCR-based MLVA procedure over 83 strains from different Brucella species. The WGS-based MLVA approach detected 95.3% of all possible 1,328 hits (83 strains×16 loci) and showed an agreement rate with the PCR-based MLVA procedure of 96.4% for MLVA-16. According to our dataset, we suggest the use of a minimal depth of coverage of ~50x and a maximum number of ~200 contigs as guiding “boundaries” for the future application of the script. In conclusion, the evaluated script seems to be a very useful and robust tool for the in silico determination of MLVA profiles of Brucella strains, allowing retrospective and prospective molecular epidemiological studies, which are important for maintaining an active epidemiological surveillance of brucellosis.

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