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Though most genetic studies of substance use focus on specific substances in isolation or generalized vulnerability across multiple substances, few studies to date focus on the concurrent use of two or more substances within a specified time frame (i.e., polysubstance use; PSU). We evaluated whether distinct genetic factors underlying internalizing and externalizing traits were associated with past 30-day PSU above variance shared across general psychopathology and substance use (SU). Using Genomic Structural Equation Modeling, we constructed theory-driven, multivariate genetic factors of 16 internalizing, externalizing, and SU traits using genome-wide association studies (GWAS) summary statistics. Next, we fit a model with a higher order SU-related psychopathology factor as well as genetic variance specific to externalizing and internalizing (i.e., residual genetic variance not explained by SU or general psychopathology). GWAS-by-subtraction was used to obtain single nucleotide polymorphism effects on each of these factors. Polygenic scores (PGS) were then created in an independent target sample with data on PSU, the National Longitudinal Study of Adolescent to Adult Health. To evaluate the effect of genetic variance due to internalizing and externalizing traits independent of variance related to SU, we regressed PSU on the PGSs, controlling for sex, age, and genetic principal components. PGSs for SU-related psychopathology and non-SU externalizing traits were associated with higher PSU factor scores, while the non-SU internalizing PGS was not significantly associated with PSU. In total, the three PGSs accounted for an additional 4% of the variance in PSU above and beyond a null model with only age, sex, and genetic principal components as predictors. These findings suggest that there may be unique genetic variance in externalizing traits contributing to liability for PSU that is independent of the genetic variance shared with SU.
Pubmed ID: 37066212 RIS Download
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International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes
View all literature mentionsDatabase for the UK Brain Expression Consortium (UKBEC) dataset that comprises of brains from individuals free of neurodegenerative disorders. The aim of Braineac is to release to the scientific community a valid instrument to investigate the genes and SNPs associated with neurological disorders.
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