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Copy number variants (CNVs) are associated with psychiatric conditions in clinical populations. The relationship between rare CNV burden and neuropsychiatric traits in young, general populations is underexplored. A total of 6,807 children from the Avon Longitudinal Study of Parents and Children (ALSPAC) were studied. CNVs were inferred from single nucleotide polymorphism-array data using PennCNV. After excluding children with known candidate CNVs for schizophrenia (SCZ), rare (<1%) CNV burden (total number of genes affected by CNVs, total length of CNVs, and largest CNV carried) was analyzed in relation to: psychotic experiences (PEs) and anxiety/depression in adolescence; autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD), ASD and ADHD traits, and cognitive measures during childhood. Outcomes were also assessed in relation to known SCZ CNVs. The number of genes affected by rare CNVs was associated with a continuous measure of ASD: the standardized mean difference [SMD] per gene affected was increased by 0.018 [95%CI 0.011,0.025], p = 3e-07 for duplications and by 0.021 [95%CI 0.010, 0.032], p = 1e-04 for deletions. In line with our published results on educational attainment in ALSPAC, intelligence quotient (IQ) was associated with CNV burden: the SMD per gene affected was -0.017 [95%CI -0.025, -0.008] p = 1e-04 for duplications and -0.023 [95%CI -0.037, -0.009], p = .002 for deletions. Associations were also observed for measures of coherence, attention, memory, and social cognition. SCZ-associated deletions were associated with IQ (SMD: -0.617 [95%CI -0.936, -0.298], p = 2e-04), but not with PEs or other traits. We found that rare CNV burden and known SCZ candidate CNVs are associated with neuropsychiatric phenotypes in a nonclinically ascertained sample of young people.
Pubmed ID: 29687944
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THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A multi-country collaboration among scientists and funding agencies to develop a public resource where genetic similarities and differences in human beings are identified and catalogued. Using this information, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. All of the information generated by the Project will be released into the public domain. Their goal is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. HapMap project related data, software, and documentation include: bulk data on genotypes, frequencies, LD data, phasing data, allocated SNPs, recombination rates and hotspots, SNP assays, Perlegen amplicons, raw data, inferred genotypes, and mitochondrial and chrY haplogroups; Generic Genome Browser software; protocols and information on assay design, genotyping and other protocols used in the project; and documentation of samples/individuals and the XML format used in the project.
View all literature mentionsOpen source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.
View all literature mentionsA free software tool for Copy Number Variation (CNV) detection from SNP genotyping arrays. Currently it can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs form segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.
View all literature mentionsA long-term health research project which follows pregnant women and their offspring in a continuous health and developmental study. More than 14,000 mothers enrolled during pregnancy in 1991 and 1992, and the health and development of their children has been followed in great detail. The ALSPAC families have provided a vast amount of genetic and environmental information over the years which can be made available to researchers globally.
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