A genome‐wide linkage analysis for the personality trait neuroticism in the Irish affected sib‐pair study of alcohol dependence

Kuo, Po-Hsiu; Neale, Michael C.; Riley, Brien P.; Patterson, Diana G.; Walsh, Dermot; Prescott, Carol A.; Kendler, Kenneth S.

doi:10.1002/ajmg.b.30478

Cited by 40 publications

(30 citation statements)

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“…Suggestive linkage has been reported for several chromosomal regions with only few replications. 21, 22, 23, 24, 25, 26, 27 Candidate studies have reported associations with markers within several genes, 28, 29, 30, 31, 32 but again, replication generally failed. 33, 34, 35 To date, a number of genome-wide association studies (GWAS) for higher-order personality traits have been published.…”

Section: Introductionmentioning

confidence: 99%

Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion

et al. 2012

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The personality traits of neuroticism and extraversion are predictive of a number of social and behavioural outcomes and psychiatric disorders. Twin and family studies have reported moderate heritability estimates for both traits. Few associations have been reported between genetic variants and neuroticism/extraversion, but hardly any have been replicated. Moreover, the ones that have been replicated explain only a small proportion of the heritability (<∼2%). Using genome-wide single-nucleotide polymorphism (SNP) data from ∼12 000 unrelated individuals we estimated the proportion of phenotypic variance explained by variants in linkage disequilibrium with common SNPs as 0.06 (s.e.=0.03) for neuroticism and 0.12 (s.e.=0.03) for extraversion. In an additional series of analyses in a family-based sample, we show that while for both traits ∼45% of the phenotypic variance can be explained by pedigree data (that is, expected genetic similarity) one third of this can be explained by SNP data (that is, realized genetic similarity). A part of the so-called ‘missing heritability' has now been accounted for, but some of the reported heritability is still unexplained. Possible explanations for the remaining missing heritability are that: (i) rare variants that are not captured by common SNPs on current genotype platforms make a major contribution; and/ or (ii) the estimates of narrow sense heritability from twin and family studies are biased upwards, for example, by not properly accounting for nonadditive genetic factors and/or (common) environmental factors.

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Section: Introductionmentioning

confidence: 99%

Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion

et al. 2012

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“…In addition, we searched individual genome-wide linkage studies in the NCBI PubMed database that were published before 2010 and were not included in Harvey et al [1] for traits related to affection, including ‘depressive disorder’, ‘bipolar disorder’ and ‘neuroticism’ to obtain extra linkage regions. Three articles [6], [8], [12] were found. Because the resolution in linkage studies was usually low, and it is not easy to define a confidence interval for each linkage peak across many linkage studies, to identify candidate genes (using Ensembl Build 56) in every linkage peak, we arbitrarily defined the boundaries of each selected region by the position of the markers giving the highest logarithm of odds (LOD) scores and extending 10 megabases in both directions.…”

Section: Methodsmentioning

confidence: 99%

“…The lifetime prevalence of depression ranges from 9.2 to19.6% worldwide [2]–[4], and heritability is estimated at approximately 37–43% [5]. Over the last decade, many studies have been devoted to dissecting the genetic influences of depression using a variety of experimental designs and technological approaches, including genomic-wide linkage scans, genetic association studies, and microarray gene expression [6]–[12]. Several hypotheses have been proposed for the biological mechanisms of developing depression based on prior evidence [13]–[16], including monoamine-deficiency hypothesis , hypothalamic-pituitary-cortisol hypothesis and other possible pathophysiological mechanisms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources

et al. 2011

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BackgroundLarge scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression.MethodsDepression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues.ResultsA total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources. Through the prioritization procedures, we identified 169 DEPgenes, which exhibited high chance to be associated with depression in GWA dataset (Wilcoxon rank-sum test, p = 0.00005). Additionally, the DEPgenes had a higher percentage to express in human brain or nerve related tissues than non-DEPgenes, supporting the neurotransmitter and neuroplasticity theories in depression.ConclusionsWith comprehensive data collection and curation and an application of integrative approach, we successfully generated DEPgenes through an effective gene prioritization system. The prioritized DEPgenes are promising for future biological experiments or replication efforts to discoverthe underlying molecular mechanisms for depression.

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“…Inter-marker distances and marker coverage were based on the genotypic data of the Irish Affected Sib-Pair Study of Alcohol Dependence study (Kuo et al 2007, 2006; Prescott et al 2006) with six alleles present at each marker (allele frequencies 0.1, 0.5, 0.2, 0.1, 0.05, 0.05) yielding data for 1,020 autosomal markers at an average inter-marker distance of 4 cM. Data were simulated for 500 nuclear families each composed of a pair of parents with two offspring.…”

Section: Methodsmentioning

confidence: 99%

Efficient Calculation of Empirical P-values for Genome-Wide Linkage Analysis Through Weighted Permutation

et al. 2008

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Linkage analysis in multivariate or longitudinal context presents both statistical and computational challenges. The permutation test can be used to avoid some of the statistical challenges, but it substantially adds to the computational burden. Utilizing the distributional dependencies between π̂ (defined as the proportion of alleles at a locus that are identical by descent (IBD) for a pairs of relatives, at a given locus) and the permutation test we report a new method of efficient permutation. In summary, the distribution of π̂ for a sample of relatives at locus x is estimated as a weighted mixture of π̂ drawn from a pool of 'representative' π̂ distributions observed at other loci. This weighting scheme is then used to sample from the distribution of the permutation tests at the representative loci to obtain an empirical P-value at locus x (which is asymptotically distributed as the permutation test at loci x). This weighted mixture approach greatly reduces the number of permutation tests required for genome-wide scanning, making it suitable for use in multivariate and other computationally intensive linkage analyses. In addition, because the distribution of π̂ is a property of the genotypic data for a given sample and is independent of the phenotypic data, the weighting scheme can be applied to any phenotype (or combination of phenotypes) collected from that sample. We demonstrate the validity of this approach through simulation. The calculation of empirical P-values provides the most general solution to this problem. Currently the two most popular methods for obtaining empirical P-values for linkage analyses involve using 'gene-dropping' simulations or permutation to randomize the relationship between genotypic and phenotypic data. Both methods produce asymptotically unbiased estimates of significance (Churchill and Doerge 1994;Ott 1989). Gene-dropping requires the simulation of unlinked genotypic data that preserve the information content, allele frequency and patterns of missing data that are observed within the 'true' genotypic data. Alternatively, permutation can be used to randomize either the coefficient of genotypic sharing π, or the phenotypes, across relative pairs. Essentially, the connection between the genotypic and the phenotypic data is deliberately disrupted and the linkage analysis is repeated over a large number of permutations in order to obtain an empirical distribution of test statistics for which the null hypothesis is true. The test statistic obtained from the original, un-permuted dataset is then compared with this empirical distribution to assess significance. This approach has an attractive simplicity, but it does require that the permutations are performed across pedigrees with the same family structure by first permuting across families of the same size and then permuting within families. However, while empirical P-values offer robustness against violation of the assumptions of statistical methods (Churchill and Doerge 1994;Ott 1989), they remain computationally intensive for mult...

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A genome‐wide linkage analysis for the personality trait neuroticism in the Irish affected sib‐pair study of alcohol dependence

Cited by 40 publications

References 41 publications

Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion

Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion

Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources

Efficient Calculation of Empirical P-values for Genome-Wide Linkage Analysis Through Weighted Permutation

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