scholarly journals Architecting a distributed bioinformatics platform with iRODS and iPlant Agave API

2015 ◽  
Author(s):  
Liya Wang ◽  
Peter Van Buren ◽  
Doreen Ware
Keyword(s):  
Large Scale ◽  
Genome Wide Association Study ◽  
Data Transfer ◽  
Genomic Analysis ◽  
The Past ◽  
Large Scale Data ◽  
Genome Wide ◽  
A Genome ◽  
Bioinformatics Workflow ◽  
Scale Data

Over the past few years, cloud-based platforms have been proposed to address storage, management, and computation of large-scale data, especially in the field of genomics. However, for collaboration efforts involving multiple institutes, data transfer and management, interoperability and standardization among different platforms have imposed new challenges. This paper proposes a distributed bioinformatics platform that can leverage local clusters with remote computational clusters for genomic analysis using the unified bioinformatics workflow. The platform is built with a data server configured with iRODS, a computation cluster authenticated with iPlant Agave system, and web server to interact with the platform. A Genome-Wide Association Study workflow is integrated to validate the feasibility of the proposed approach.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior

Science ◽  
2019 ◽  
Vol 365 (6456) ◽  
pp. eaat7693 ◽  
Author(s):  
Andrea Ganna ◽  
Karin J. H. Verweij ◽  
Michel G. Nivard ◽  
Robert Maier ◽  
Robbee Wedow ◽  
...  
Keyword(s):  
Sexual Behavior ◽  
Genetic Architecture ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Same Sex ◽  
Genome Wide ◽  
A Genome ◽  
Number Of Sexual Partners ◽  
Opposite Sex ◽  
Males And Females

Twin and family studies have shown that same-sex sexual behavior is partly genetically influenced, but previous searches for specific genes involved have been underpowered. We performed a genome-wide association study (GWAS) on 477,522 individuals, revealing five loci significantly associated with same-sex sexual behavior. In aggregate, all tested genetic variants accounted for 8 to 25% of variation in same-sex sexual behavior, only partially overlapped between males and females, and do not allow meaningful prediction of an individual’s sexual behavior. Comparing these GWAS results with those for the proportion of same-sex to total number of sexual partners among nonheterosexuals suggests that there is no single continuum from opposite-sex to same-sex sexual behavior. Overall, our findings provide insights into the genetics underlying same-sex sexual behavior and underscore the complexity of sexuality.

scholarly journals Genomic variation across a clinical Cryptococcus population linked to disease outcome

2021 ◽  
Author(s):  
Poppy Channa Sakti Sephton-Clark ◽  
Jennifer Tenor ◽  
Dena Toffaletti ◽  
Nancy Meyers ◽  
Charles Giamberardino ◽  
...  
Keyword(s):  
Patient Outcomes ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Sugar Transport ◽  
Genomic Variation ◽  
Clinical Isolates ◽  
Genome Wide ◽  
A Genome ◽  
Gwas Approach

Cryptococcus neoformans is the causative agent of cryptococcosis, a disease with poor patient outcomes, accounting for approximately 180,000 deaths each year. Patient outcomes may be impacted by the underlying genetics of the infecting isolate, however, our current understanding of how genetic diversity contributes to clinical outcomes is limited. Here, we leverage clinical, in vitro growth and genomic data for 284 C. neoformans isolates to identify clinically relevant pathogen variants within a population of clinical isolates from patients with HIV-associated cryptococcosis in Malawi. Through a genome-wide association study (GWAS) approach, we identify variants associated with fungal burden and growth rate. We also find both small and large-scale variation, including aneuploidy, associated with alternate growth phenotypes, which may impact the course of infection. Genes impacted by these variants are involved in transcriptional regulation, signal transduction, glycolysis, sugar transport, and glycosylation. When combined with clinical data, we show that growth within the CNS is reliant upon glycolysis in an animal model, and likely impacts patient mortality, as CNS burden modulates patient outcome. Additionally, we find genes with roles in sugar transport are under selection in the majority of these clinical isolates. Further, we demonstrate that two hypothetical proteins identified by GWAS impact virulence in animal models. Our approach illustrates links between genetic variation and clinically relevant phenotypes, shedding light on survival mechanisms within the CNS and pathways involved in this persistence.

scholarly journals Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models

2016 ◽  
Author(s):  
Lana S. Martin ◽  
Eleazar Eskin
Keyword(s):  
Population Structure ◽  
Mixed Models ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Laboratory Mouse ◽  
Mouse Strains ◽  
Genetic Studies ◽  
The Past ◽  
Genome Wide ◽  
A Genome

AbstractA genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to effectively test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.

scholarly journals Genome-Wide Natural Selection Signatures Are Linked to Genetic Risk of Modern Phenotypes in the Japanese Population

2020 ◽  
Vol 37 (5) ◽  
pp. 1306-1316 ◽  
Author(s):  
Yoshiaki Yasumizu ◽  
Saori Sakaue ◽  
Takahiro Konuma ◽  
Ken Suzuki ◽  
Koichi Matsuda ◽  
...  
Keyword(s):  
Natural Selection ◽  
Japanese Population ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Enrichment Analysis ◽  
Recent Common Ancestor ◽  
Selection Signatures ◽  
Most Recent Common Ancestor ◽  
Genome Wide ◽  
A Genome

Abstract Elucidation of natural selection signatures and relationships with phenotype spectra is important to understand adaptive evolution of modern humans. Here, we conducted a genome-wide scan of selection signatures of the Japanese population by estimating locus-specific time to the most recent common ancestor using the ascertained sequentially Markovian coalescent (ASMC), from the biobank-based large-scale genome-wide association study data of 170,882 subjects. We identified 29 genetic loci with selection signatures satisfying the genome-wide significance. The signatures were most evident at the alcohol dehydrogenase (ADH) gene cluster locus at 4q23 (PASMC = 2.2 × 10−36), followed by relatively strong selection at the FAM96A (15q22), MYOF (10q23), 13q21, GRIA2 (4q32), and ASAP2 (2p25) loci (PASMC < 1.0 × 10−10). The additional analysis interrogating extended haplotypes (integrated haplotype score) showed robust concordance of the detected signatures, contributing to fine-mapping of the genes, and provided allelic directional insights into selection pressure (e.g., positive selection for ADH1B-Arg48His and HLA-DPB1*04:01). The phenome-wide selection enrichment analysis with the trait-associated variants identified a variety of the modern human phenotypes involved in the adaptation of Japanese. We observed population-specific evidence of enrichment with the alcohol-related phenotypes, anthropometric and biochemical clinical measurements, and immune-related diseases, differently from the findings in Europeans using the UK Biobank resource. Our study demonstrated population-specific features of the selection signatures in Japanese, highlighting a value of the natural selection study using the nation-wide biobank-scale genome and phenotype data.

scholarly journals Approximate conditional phenotype analysis based on genome wide association summary statistics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peitao Wu ◽  
Biqi Wang ◽  
Steven A. Lubitz ◽  
Emelia J. Benjamin ◽  
James B. Meigs ◽  
...  
Keyword(s):  
Large Scale ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Genome Wide ◽  
Level Data ◽  
A Genome ◽  
Phenotype Analysis

AbstractBecause single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.

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