The primary goal of the Human Connectome Project is to understand the typical patterns of structural and functional connectivity in the healthy adult human brain. However, as we attempt to define "typical," we know that there are important individual differences in such patterns of connectivity even among persons with no diagnosable neurological or psychiatric disorders. One clue as to the importance of these individual differences may lie in their relationship to genetic variation.
Through the Human Connectome Project, we hope to better understand the role that genes play in influencing individual variation in patterns of structural and functional connectivity. We are using two approaches to examining genetic influences on connectivity. First, as described in the Recruitment section, we are including families with both identical and fraternal twins in our sample. We can learn about the strength of genetic influences by comparing how similar identical and fraternal twins are on connectivity patterns. For patterns of connectivity that are wholly genetic, identical twins will show 100% concordance (i.e., will be exactly the same) whereas fraternal twins and their siblings will show 50% concordance (i.e., will be the same about ½ the time). It is likely that a combination of genetic and environmental factors influence variation for most patterns of connectivity. Second, we will identify the specific genes that influence an individual pattern of connectivity by surveying DNA variants throughout the genome in our large sample of siblings.
Genome-wide association studies (GWAS) map genetic differences across human populations to identify DNA variants associated with a particular pattern (trait). This can be a normal trait such as structural and functional connectivity of the brain or it can be a disease or a disease associated trait. As many as one million DNA variants that exist in more than one form can be surveyed simultaneously in each individual using the high-throughput Illumina Infinium HD Beadchip.
Data will be deposited in the database of Genotypes and Phenotypes (dbGaP), which has been developed to archive and distribute the results of studies that have investigated the interaction of genotype and phenotype. Publicly released data will be coded so that no individuals or families can be identified. Principal Investigators funded by the National Institutes of Health can apply for access to both the genotype and phenotype data and are free to perform analyses of their choosing using the data.
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