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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies.

ABSTRACT: Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene-environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits.

SUBMITTER: Hall MA

PROVIDER: S-EPMC5660079 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies.

Hall Molly A MA Wallace John J Lucas Anastasia A Kim Dokyoon D Basile Anna O AO Verma Shefali S SS McCarty Cathy A CA Brilliant Murray H MH Peissig Peggy L PL Kitchner Terrie E TE Verma Anurag A Pendergrass Sarah A SA Dudek Scott M SM Moore Jason H JH Ritchie Marylyn D MD

Nature communications 20171027 1

Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool ...[more]

PMID: 29079728

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Project description:The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.

Project description:BackgroundMitochondria play a critical role in the cell and have DNA independent of the nuclear genome. There is much evidence that mitochondrial DNA (mtDNA) variation plays a role in human health and disease, however, this area of investigation has lagged behind research into the role of nuclear genetic variation on complex traits and phenotypic outcomes. Phenome-wide association studies (PheWAS) investigate the association between a wide range of traits and genetic variation. To date, this approach has not been used to investigate the relationship between mtDNA variants and phenotypic variation. Herein, we describe the development of a PheWAS framework for mtDNA variants (mt-PheWAS). Using the Metabochip custom genotyping array, nuclear and mitochondrial DNA variants were genotyped in 11,519 African Americans from the Vanderbilt University biorepository, BioVU. We employed both polygenic modeling and association testing with mitochondrial single nucleotide polymorphisms (mtSNPs) to explore the relationship between mtDNA variants and a group of eight cardiovascular-related traits obtained from de-identified electronic medical records within BioVU.ResultsUsing polygenic modeling we found evidence for an effect of mtDNA variation on total cholesterol and type 2 diabetes (T2D). After performing comprehensive mitochondrial single SNP associations, we identified an increased number of single mtSNP associations with total cholesterol and T2D compared to the other phenotypes examined, which did not have more significantly associated SNPs than would be expected by chance. Among the mtSNPs significantly associated with T2D we identified variant mt16189, an association previously reported only in Asian and European-descent populations.ConclusionsOur replication of previous findings and identification of novel associations from this initial study suggest that our mt-PheWAS approach is robust for investigating the relationship between mitochondrial genetic variation and a range of phenotypes, providing a framework for future mt-PheWAS.

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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies.

Publications

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies.

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