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Massively expedited genome-wide heritability analysis (MEGHA)

The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability requ... Full description

Journal Title: Proceedings of the National Academy of Sciences USA, Feb 2015, Vol.112(8), p.2479
Main Author: Ge, Tian
Other Authors: Nichols, Thomas , Lee, Phil , Holmes, Avram , Roffman, Joshua , Buckner, Randy , Sabuncu, Mert , Smoller, Jordan
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0027-8424
Link: http://search.proquest.com/docview/1676362828/?pq-origsite=primo
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title: Massively expedited genome-wide heritability analysis (MEGHA)
format: Article
creator:
  • Ge, Tian
  • Nichols, Thomas
  • Lee, Phil
  • Holmes, Avram
  • Roffman, Joshua
  • Buckner, Randy
  • Sabuncu, Mert
  • Smoller, Jordan
subjects:
  • Pedigree
  • Brain Mapping
  • Neuroimaging
  • Data Processing
  • Statistics
  • Magnetic Resonance Imaging
  • Statistical Analysis
  • Image Processing
  • Twins
  • Single-Nucleotide Polymorphism
  • Sampling
  • Heritability
  • Computational Neuroscience
  • Gene Mapping
  • Human Genetics
ispartof: Proceedings of the National Academy of Sciences, USA, Feb 2015, Vol.112(8), p.2479
description: The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary...
language: eng
source:
identifier: ISSN: 0027-8424
fulltext: fulltext
issn:
  • 00278424
  • 0027-8424
url: Link


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titleMassively expedited genome-wide heritability analysis (MEGHA)
creatorGe, Tian ; Nichols, Thomas ; Lee, Phil ; Holmes, Avram ; Roffman, Joshua ; Buckner, Randy ; Sabuncu, Mert ; Smoller, Jordan
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ispartofProceedings of the National Academy of Sciences, USA, Feb 2015, Vol.112(8), p.2479
identifierISSN: 0027-8424
subjectPedigree ; Brain Mapping ; Neuroimaging ; Data Processing ; Statistics ; Magnetic Resonance Imaging ; Statistical Analysis ; Image Processing ; Twins ; Single-Nucleotide Polymorphism ; Sampling ; Heritability ; Computational Neuroscience ; Gene Mapping ; Human Genetics
descriptionThe discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary...
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titleMassively expedited genome-wide heritability analysis (MEGHA)
descriptionThe discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary...
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abstractThe discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary...
urlhttp://search.proquest.com/docview/1676362828/
doi10.1073/pnas.1415603112
eissn10916490
date2015-02-24