Genomewide Linkage Scan for Opioid Dependence and Related Traits

Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA.
The American Journal of Human Genetics (Impact Factor: 10.93). 06/2006; 78(5):759-69. DOI: 10.1086/503631
Source: PubMed


Risk of opioid dependence is genetically influenced. We recruited a sample of 393 small nuclear families (including 250 full-sib and 46 half-sib pairs), each with at least one individual with opioid dependence. Subjects underwent a detailed evaluation of substance dependence-related traits. As planned a priori to reduce heterogeneity, we used cluster analytic methods to identify opioid dependence-related symptom clusters, which were shown to be heritable. We then completed a genomewide linkage scan (with 409 markers) for the opioid-dependence diagnosis and for the two cluster-defined phenotypes represented by >250 families: the heavy-opioid-use cluster and the non-opioid-use cluster. Further exploratory analyses were completed for the other cluster-defined phenotypes. The statistically strongest results were seen with the cluster-defined traits. For the heavy-opioid-use cluster, we observed a LOD score of 3.06 on chromosome 17 (empirical pointwise P = .0002) for European American (EA) and African American (AA) subjects combined, and, for the non-opioid-use cluster, we observed a LOD score of 3.46 elsewhere on chromosome 17 (empirical pointwise P = .00002, uncorrected for multiple traits studied) for EA subjects only. We also identified a possible linkage (LOD score 2.43) of opioid dependence with chromosome 2 markers for the AA subjects. These results are an initial step in identifying genes for opioid dependence on the basis of a genomewide investigation (i.e., a study not conditioned on prior physiological candidate-gene hypotheses).

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Available from: Marsha Wilcox, Dec 18, 2013
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    • "It is challenging to identify the genetic causes of complex disorders such as substance dependence, due to their heterogeneous clinical manifestations and complex genetic etiologies, which include gene x environment interactions. Phenotype refinement that leads to homogeneous subtypes is a promising approach to solve this problem [1,5,23-25]. However, most of the methods used to refine phenotypes take into consideration only the phenotypic information, despite the availability of genotypic information in genetic studies of a complex disorder. "
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    ABSTRACT: Background Accurate classification of patients with a complex disease into subtypes has important implications for medicine and healthcare. Using more homogeneous disease subtypes in genetic association analysis will facilitate the detection of new genetic variants that are not detectible using the non-differentiated disease phenotype. Subtype differentiation can also improve diagnostic classification, which can in turn inform clinical decision making and treatment matching. Currently, the most sophisticated methods for disease subtyping perform cluster analysis using patients’ clinical features. Without guidance from genetic information, the resultant subtypes are likely to be suboptimal and efforts at genetic association may fail. Results We propose a multi-view matrix decomposition approach that integrates clinical features with genetic markers to detect confirmatory evidence for a disease subtype. This approach groups patients into clusters that are consistent between the clinical and genetic dimensions of data; it simultaneously identifies the clinical features that define the subtype and the genotypes associated with the subtype. A simulation study validated the proposed approach, showing that it identified hypothesized subtypes and associated features. In comparison to the latest biclustering and multi-view data analytics using real-life disease data, the proposed approach identified clinical subtypes of a disease that differed from each other more significantly in the genetic markers, thus demonstrating the superior performance of the proposed approach. Conclusions The proposed algorithm is an effective and superior alternative to the disease subtyping methods employed to date. Integration of phenotypic features with genetic markers in the subtyping analysis is a promising approach to identify concurrently disease subtypes and their genetic associations.
    Full-text · Article · Jun 2014 · BMC Genetics
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    • "This is the correlation parameter of a standard bivariate normal latent process representing forces that create association between alleles, for example, selection favoring some combination of alleles. Apart from the threshold-liability models of animal breeding, tetra-choric correlations have been used in human genetics, for example, heritability of migraines and of dependency on opioids (Nyholt et al., 2005;Gelernter et al., 2006). Typically, estimates of tetra-choric correlations are larger than standard correlation statistics (for example, Ueberseax, 2010). "
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    ABSTRACT: The analysis of systems involving many loci is important in population and quantitative genetics. An important problem is the study of linkage disequilibrium (LD), a concept relevant in genome-enabled prediction of quantitative traits and in exploration of marker-phenotype associations. This article introduces a new estimator of a LD parameter (ρ(2)) that is much easier to compute than a maximum likelihood (or Bayesian) estimate of a tetra-choric correlation. We examined the conjecture that the sampling distribution of the estimator of ρ(2) could be less frequency dependent than that of the estimator of r(2), a widely used metric for assessing LD. This was done via an empirical evaluation of LD in 806 Holstein-Friesian cattle using 771 single-nucleotide polymorphism (SNP) markers and of HapMap III data on 21 991 SNPs (chromosome 3) observed in 88 unrelated individuals from Tuscany. Also, 1600 haplotypes over a region of 1 Mb simulated under the coalescent were used to estimate LD using the two measures. Subsequently, a simulation study compared the new estimator with that of r(2) using several scenarios of LD and allelic frequencies. From these studies, it is concluded that ρ(2) provides a useful metric for the study of LD as the distribution of its estimator is less frequency dependent than that of the standard estimator of r(2).Heredity advance online publication, 7 August 2013; doi:10.1038/hdy.2013.46.
    Full-text · Article · Aug 2013 · Heredity
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    • "The stringency of the statistical criteria applied to the analysis, discussed in more detail below, may have been a critical factor in the limited number of positive effects identified in these studies. Indeed, a genome-wide linkage scan for opioid dependence identified only weak linkage signals (Gelernter et al., 2006), and a genome-wide linkage scan for nicotine dependence identified only one " genome-wide " result that was significant (Gelernter et al., 2007). As will be discussed below, there are statistical issues with regard to " genome-wide " significance, but because linkage involves fewer recombination events, it may lack the resolution necessary to identify these genetic contributions to addiction liability. "
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    ABSTRACT: Substantial genetic contributions to addiction vulnerability are supported by data from twin studies, linkage studies, candidate gene association studies and, more recently, Genome Wide Association Studies (GWAS). Parallel to this work, animal studies have attempted to identify the genes that may contribute to responses to addictive drugs and addiction liability, initially focusing upon genes for the targets of the major drugs of abuse. These studies identified genes/proteins that affect responses to drugs of abuse; however, this does not necessarily mean that variation in these genes contributes to the genetic component of addiction liability. One of the major problems with initial linkage and candidate gene studies was an a priori focus on the genes thought to be involved in addiction based upon the known contributions of those proteins to drug actions, making the identification of novel genes unlikely. The GWAS approach is systematic and agnostic to such a priori assumptions. From the numerous GWAS now completed several conclusions may be drawn: (1) addiction is highly polygenic; each allelic variant contributing in a small, additive fashion to addiction vulnerability; (2) unexpected, compared to our a priori assumptions, classes of genes are most important in explaining addiction vulnerability; (3) although substantial genetic heterogeneity exists, there is substantial convergence of GWAS signals on particular genes. This review traces the history of this research; from initial transgenic mouse models based upon candidate gene and linkage studies, through the progression of GWAS for addiction and nicotine cessation, to the current human and transgenic mouse studies post-GWAS.
    Full-text · Article · Jul 2013 · Pharmacology [?] Therapeutics
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