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| || We developed MEGHA (Massively Expedited Genome-wide Heritability Analysis), a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time. We computed vertex-wise surface maps in FreeSurfer fsaverage space for SNP-based heritability estimates and significance of cortical thickness, sulcal depth, curvature, and surface area measures, using data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry as part of the Harvard/MGH Brain Genomics Superstruct Project (GSP). These surface maps are available for download. ||{{attachment:MEGHA_Ge2015.png | Heritability Map | width=250 height=150}} || | || We developed MEGHA (Massively Expedited Genome-wide Heritability Analysis), a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time. We computed vertex-wise surface maps in FreeSurfer fsaverage space for SNP-based heritability estimates and significance of cortical thickness, sulcal depth, curvature, and surface area measures, using data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry as part of the Harvard/MGH Brain Genomics Superstruct Project (GSP). These surface maps are available for download. || {{attachment:MEGHA_Ge2015.png | Heritability | width=250 height=150}} || |
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| [[http://scholar.harvard.edu/files/tge/files/massively_expedited_genome-wide_heritability_analysis_megha.pdf | Ge T, Nichols TE, Lee PH, Holmes AJ, Roffman JL, Buckner RL, Sabuncu MR, Smoller JW. Massively Expedited Genome-wide Heritability Analysis (MEGHA), Proceedings of the National Academy of Sciences, 112(8), 2479-2484.]] | [[http://scholar.harvard.edu/files/tge/files/massively_expedited_genome-wide_heritability_analysis_megha.pdf | Ge T, Nichols TE, Lee PH, Holmes AJ, Roffman JL, Buckner RL, Sabuncu MR*, Smoller JW*. Massively Expedited Genome-wide Heritability Analysis (MEGHA), Proceedings of the National Academy of Sciences, 112(8), 2479-2484. (*contributed equally)]] |
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{{attachment:SurfaceMap_Ge2015.png | Surface Maps | height=500}} == Downloads == |
Massively Expedited Genome-wide Heritability Analysis (MEGHA)
We developed MEGHA (Massively Expedited Genome-wide Heritability Analysis), a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time. We computed vertex-wise surface maps in FreeSurfer fsaverage space for SNP-based heritability estimates and significance of cortical thickness, sulcal depth, curvature, and surface area measures, using data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry as part of the Harvard/MGH Brain Genomics Superstruct Project (GSP). These surface maps are available for download. |
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References
Software
A MATLAB implementation of MEGHA can be found here.
Surface Maps for Cortical Thickness Measures
