Step by step one goes very far
Project context and summary :
Our recent analyses suggest that the genetic determinants of human neuroanatomical diversity are massively polygenic. Like other quantitative traits such as height – but also IQ or ASD risk – neuroanatomical diversity seems to result from the aggregated effect of thousands of frequent variants, each of small effect. GWAS should then require populations of hundreds of thousands of individuals to start to detect the individual variants. GCTA (genomic complex trait analysis) offers an alternative approach to obtain valuable neurogenetic information despite the current impossibility to detect enough individual variants to explaining any substantial part of the variability. We are currently pooling together neuroimaging genomics data from multiple international projects (in particular, IMAGEN, ENIGMA, UK Biobank) to replicate and extend our earlier analyses. We aim to: (1) Compute the amount of variance captured by genome-wide SNPs (SNP-heritability) for the several brain regions: ICV, BV, Hip, Th, Ca, Pa, Pu, Amy and Acc, (2) Compute the matrix of SNP-based genetic correlation among structures, (3) Partition the variance captured by SNPs among structural and functional sets: per chromosome, genic vs non-genic, low/medium/high minor-allele frequency, positive/negative selection, involved or not in neurodevelopment, etc. (4) Compare our results with those obtained using GWAS-based estimations (for example, those used in ENIGMA2). GCTA requires the computation of matrices of genetic relationship among all individuals, and thus, direct access to the genotyping data. Once the matrices are computed, the genotyping data is no longer required, and it is not possible to reconstruct an individual's genome from the matrices. Our analysis of the IMAGEN cohort was based on 1,765 Individuals, which gave us sufficient statistical power (80%) to detect only strong heritabilities (h2~45%), and the estimations had very large standard errors (~20%). A cohort of 4,000 subjects should allow us to decrease the standard error to ~8% (80% power to detect h2=22%), and a cohort of 8,000 subjects should decrease it to ~4% (80% power to detect h2=11%). In this way, we could obtain more accurate estimates, but also detect eventually more subtle effects related to functional genomic partitions. References Yang et al (2010) Common SNPs explain a large proportion of heritability for human height. Nature Genetics, doi: 10.1038/ng.608 Davies et al (2011) Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, doi: 10.1038/mp.2011.85 Gaugler et al (2014) Most genetic risk for autism resides with common variation. Nature Genetics, doi: 10.1038/ng.3039 Wood et al (2014) Defining the role of common variation in the genomic and biological architecture of adult human height, doi: 10.1038/ng.3097Related team publications :
Sorry. You must be logged in to view this form.