Dataset Information

Genomewide Gene-by-Sex Interaction Scans Identify ADGRV1 for Sex Differences in Opioid Dependent African Americans.

ABSTRACT: Sex differences in opioid dependence (OD) are genetically influenced. We conducted genomewide gene-by-sex interaction scans for the DSM-IV diagnosis of OD in 8,387 African-American (AA) or European-American subjects (43.6% women; 4,715 OD subjects). Among AAs, 9 SNPs were genome-wide significant at ADGRV1 (adhesion G-protein-coupled receptor V1, lead-SNP rs2366929*(C/T), p?=?1.5?×?10-9) for sex-different risk of OD, with the rs2366929*C-allele increasing OD risk only for men. The top co-expressions in brain were between ADGRV1 and GRIK2 in substantia nigra and medullary inferior olivary nucleus, and between ADGRV1 and EFHC2 in frontal cortex and putamen. Significant sex-differential ADGRV1 expression from GTEx was detected in breast (Bonferroni-corrected-p?

SUBMITTER: Yang BZ

PROVIDER: S-EPMC6889277 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Genomewide Gene-by-Sex Interaction Scans Identify ADGRV1 for Sex Differences in Opioid Dependent African Americans.

Yang Bao-Zhu BZ Zhou Hang H Cheng Zhongshan Z Kranzler Henry R HR Gelernter Joel J

Scientific reports 20191202 1

Sex differences in opioid dependence (OD) are genetically influenced. We conducted genomewide gene-by-sex interaction scans for the DSM-IV diagnosis of OD in 8,387 African-American (AA) or European-American subjects (43.6% women; 4,715 OD subjects). Among AAs, 9 SNPs were genome-wide significant at ADGRV1 (adhesion G-protein-coupled receptor V1, lead-SNP rs2366929*(C/T), p = 1.5 × 10<sup>-9</sup>) for sex-different risk of OD, with the rs2366929*C-allele increasing OD risk only for men. The top ...[more]

PMID: 31792237

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Project description:With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ~860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ~6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free.

Dataset Information

Genomewide Gene-by-Sex Interaction Scans Identify ADGRV1 for Sex Differences in Opioid Dependent African Americans.

Publications

Genomewide Gene-by-Sex Interaction Scans Identify ADGRV1 for Sex Differences in Opioid Dependent African Americans.

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