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S S Cherny

Publications and source records attributed to S S Cherny.

At least 19 recordsLinked to original sources
PubMed2006

Replication of twelve association studies for Huntington's disease residual age of onset in large Venezuelan kindreds.

BACKGROUND: The major determinant of age of onset in Huntington's disease is the length of the causative triplet CAG repeat. Significant variance remains, however, in residual age of onset even after repeat length is factored out. Many genetic polymorphisms have previously shown evidence of association with age of onset of Huntington's disease in several different populations. OBJECTIVE: To replicate these genetic association tests in 443 affected people from a large set of kindreds from Venezuela. METHODS: Previously tested polymorphisms were analysed in the HD gene itself (HD), the GluR6 kainate glutamate receptor (GRIK2), apolipoprotein E (APOE), the transcriptional coactivator CA150 (TCERG1), the ubiquitin carboxy-terminal hydrolase L1 (UCHL1), p53 (TP53), caspase-activated DNase (DFFB), and the NR2A and NR2B glutamate receptor subunits (GRIN2A, GRIN2B). RESULTS: The GRIN2A single-nucleotide polymorphism explains a small but considerable amount of additional variance in residual age of onset in our sample. The TCERG1 microsatellite shows a trend towards association but does not reach statistical significance, perhaps because of the uninformative nature of the polymorphism caused by extreme allele frequencies. We did not replicate the genetic association of any of the other genes. CONCLUSIONS: GRIN2A and TCERG1 may show true association with residual age of onset for Huntington's disease. The most surprising negative result is for the GRIK2 (TAA)(n) polymorphism, which has previously shown association with age of onset in four independent populations with Huntington's disease. The lack of association in the Venezuelan kindreds may be due to the extremely low frequency of the key (TAA)(16) allele in this population.

Age of Onset↗
PubMed2006

Introduction to the special issue: human linkage studies for behavioral traits.

In the post Genome era, the aim of behavior genetics has shifted from estimating the relative contributions of genes and environmental factors to (co-)variation in human complex traits, to localization of genes and identification of functional genetic variants. This special issue reflects this transition and presents fifteen papers that report on genome-wide linkage scans for complex traits in humans and on methodological tools and innovations. Six papers focus on cognition and report overlapping linkage peaks on chromosomes 6p and 14p. Papers on addictive behavior, i.e. smoking and alcohol dependence and its endophenotypes, find moderate LOD scores on chromosomes 6p, 5q, 4p and 7q, respectively. Three papers concentrate on emotionality, depression and loneliness and examine chromosomes 2q and 12q. The papers in this issue represent a summary of the first large scale linkage enterprises of human behavioral traits.

Genetic Linkage↗
PubMed2001

The effect of genotype and pedigree error on linkage analysis: analysis of three asthma genome scans.

The effects of genotype and relationship errors on linkage results are evaluated in three of the Genetic Analysis Workshop 12 asthma genome scans. A number of errors are detected in the samples. While the evidence for linkage is not striking in any data set with or without error, in some cases the difference in test statistic could support different conclusions. The results provide empirical evidence for the predicted effects of genotype and relationship error and highlight the need for rigorous detection and elimination of data error in complex trait studies.

Adult↗
PubMed2001

Association analysis in a variance components framework.

Association analyses conducted in a variance components framework can include information from all available individuals but remain unbiased in the presence of familiality or linkage. Models that include both linkage and association parameters provide different estimates of the effect of a single locus and can be used to distinguish causal polymorphisms from other types of variation. We examine some of these models and their properties in a blind analysis of the simulated Genetic Analysis Workshop 12 data sets.

Chromosome Mapping↗
PubMed2001

A powerful and rapid approach to human genome scanning using small quantities of genomic DNA.

Dense maps of short-tandem-repeat polymorphisms (STRPs) have allowed genome-wide searches for genes involved in a great variety of diseases with genetic influences, including common complex diseases. Generally for this purpose, marker sets with a 10 cM spacing are genotyped in hundreds of individuals. We have performed power simulations to estimate the maximum possible intermarker distance that still allows for sufficient power. In this paper we further report on modifications of previously published protocols, resulting in a powerful screening set containing 229 STRPs with an average spacing of 18.3 cM. A complete genome scan using our protocol requires only 80 multiplex PCR reactions which are all carried out using one set of conditions and which do not contain overlapping marker allele sizes. The multiplex PCR reactions are grouped by sets of chromosomes, which enables on-line statistical analysis of a set of chromosomes, as sets of chromosomes are being genotyped. A genome scan following this modified protocol can be performed using a maximum amount of 2.5 micrograms of genomic DNA per individual, isolated from either blood or from mouth swabs.

Alleles↗
PubMed2001

Life events and depression in a community sample of siblings.

BACKGROUND: The overall aim of the GENESiS project is to identify quantitative trait loci (QTLs) for anxiety/depression, and to examine the interaction between these loci and psychosocial adversity. Here we present life-events data with the aim of clarifying: (i) the aetiology of life events as inferred from sibling correlations; (ii) the relationship between life events and measures of anxiety and depression, as well as neuroticism; and (iii) the interaction between life events and neuroticism on anxiety/depression indices. METHODS: We assessed the occurrence of one network and three personal life-event categories and multiple indices of anxiety/depression including General Health Questionnaire, Anhedonic Depression, Anxious Arousal and Neuroticism in a large community-based sample of2150 sib pairs, 410 trios and 81 quads. Liability threshold models and raw ordinal maximum likelihood were used to estimate within-individual and between-sibling correlations of life events. The relationship between life events and indices of emotional states and personality were assessed by multiple linear regression and canonical correlations. RESULTS: Life events showed sibling correlations of 0-37 for network events and between 0-10 and 0.19 for personal events. Adverse life events were related to anxiety and depression and, to a less extent, neuroticism. Trait-vulnerability (as indexed by co-sib's neuroticism, anxiety and depression) accounted for 11% and life events for 3% of the variance in emotional states. There were no interaction effects. CONCLUSIONS: Life events show moderate familiality and are significantly related to symptoms of anxiety and depression in the community. Appropriate modelling of life events in linkage and association analyses should help to identify QTLs for depression and anxiety.

Adult↗
PubMed2001

The impact of genotyping error on family-based analysis of quantitative traits.

Errors in genotyping can substantially influence the power to detect linkage using affected sib-pairs, but it is not clear what effect such errors have on quantitative trait analyses. Here we use Monte Carlo simulation to examine the influence of genotyping error on multipoint vs two-point analysis, variable map density, locus effect size and allele frequency in quantitative trait linkage and association studies of sib-pairs. The analyses are conducted using variance components methods. We contrast the effects of error on quantitative trait analyses with those on the affected sib-pair design. The results indicate that genotyping error influences linkage studies of affected sib pairs more severely than studies of quantitative traits in unselected sibs. In situations of modest effect size, 5% genotyping error eliminates all supporting evidence for linkage to a true susceptibility locus in affected pairs, but may only result in a loss of 15% of linkage information in random pairs. Multipoint analysis does not suffer substantially more than two-point analysis; for moderate error rates (< 5%), multipoint analysis with error is more powerful than two-point with no error. Map density does not appear to be an important factor for linkage analysis. QTL association analyses of common alleles are reasonably robust to genotyping error but power can be affected dramatically with rare alleles.

Alleles↗
PubMed2001

GRR: graphical representation of relationship errors.

SUMMARY: A graphical tool for verifying assumed relationships between individuals in genetic studies is described. GRR can detect many common errors using genotypes from many markers. AVAILABILITY: GRR is available at http://bioinformatics.well.ox.ac.uk/GRR.

Alleles↗
PubMed2001

Optimal sibship selection for genotyping in quantitative trait locus linkage analysis.

In this paper we present a novel method for selecting optimally informative sibships of any size for quantitative trait locus (QTL) linkage analysis. The method allocates a quantitative index of potential informativeness to each sibship on the basis of observed trait scores and an assumed true QTL model. Any sample of phenotypically screened sibships can therefore be easily rank-ordered for selective genotyping. The quantitative index is the sibship's expected contribution to the non-centrality parameter. This expectation represents the weighted sum of chi(2) test statistics that would be obtained given the observed trait values over all possible sibship genotypic configurations; each configuration is weighted by the likelihood of it occurring given the assumed true genetic model. The properties of this procedure are explored in relation to the accuracy of the assumed true genetic model and sibship size. In comparison to previous methods of selecting phenotypically extreme sibships for genotyping, the proposed method is considerably more efficient and is robust with regard to the specification of the genetic model.

Chromosome Mapping↗
PubMed2000

Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data.

Optimal design of quantitative-trait loci (QTL) mapping studies requires a precise understanding of the power of QTL linkage versus QTL association analysis, under a range of different conditions. In this article, we investigate the power of QTL linkage and association analyses for simple random sibship samples, under the variance-components model proposed by Fulker et al. After a brief description of an extension of this variance-components model, we show that the powers of both linkage and association analyses are crucially dependent on the proportion of phenotypic variance attributable to the QTL. The main difference between the two tests is that, whereas the power of association is directly related to the QTL heritability, the power of linkage is related more closely to the square of the QTL heritability. We also describe both how the power of linkage is attenuated by incomplete linkage and incomplete marker information and how the power of association is attenuated by incomplete linkage disequilibrium.

Chi-Square Distribution↗
PubMed2000

Variance-Components QTL linkage analysis of selected and non-normal samples: conditioning on trait values.

Standard variance-components quantitative trait loci (QTL) linkage analysis can produce an elevated rate of type 1 errors when applied to selected samples and non-normal data. Here we describe an adjustment of the log-likelihood function based on conditioning on trait values. This leads to a likelihood ratio test that is valid in selected samples and non-normal data, and equal in power to alternative methods for analyzing selected samples that require knowledge of the ascertainment procedure or the trait values of non-selected individuals.

Analysis of Variance↗
PubMed2000

Parent ratings of temperament in twins: explaining the 'too low' DZ correlations.

Twin studies of child temperament using objective measures consistently suggest moderate heritability for most dimensions. However, parent rating measures produce unusual patterns of results. Intraclass correlations for identical (MZ) twins are typically high, whereas fraternal (DZ) twin intraclass correlations are much lower than would be predicted from an additive genetic model. The 'too low' DZ correlations can be explained by parent-rating biases that either exaggerate the differences between DZ twins (contrast effects) or that inflate the similarity of MZ twins (assimilation effects), or by the presence of non-additive genetic variance. To evaluate the three possible explanations, we used model-fitting procedures applied to parent-rating data averaged across 14, 20, 24, and 36 months of age in a sample of 196 twin pairs participating in the MacArthur Longitudinal Twin Study. The data were best described by a model that included contrast effects. Implications for non-twin research are discussed.

Analysis of Variance↗
PubMed1999

High-resolution mapping of quantitative trait loci in outbred mice.

Screening the whole genome of a cross between two inbred animal strains has proved to be a powerful method for detecting genetic loci underlying quantitative behavioural traits, but the level of resolution offered by quantitative trait loci (QTL) mapping is still too coarse to permit molecular cloning of the genetic determinants. To achieve high-resolution mapping, we used an outbred stock of mice for which the entire genealogy is known. The heterogeneous stock (HS) was established 30 years ago from an eight-way cross of C57BL/6, BALB/c, RIII, AKR, DBA/2, I, A/J and C3H inbred mouse strains. At the time of the experiment reported here, the HS mice were at generation 58, theoretically offering at least a 30-fold increase in resolution for QTL mapping compared with a backcross or an F2 intercross. Using the HS mice we have mapped a QTL influencing a psychological trait in mice to a 0.8-cM interval on chromosome 1. This method allows simultaneous fine mapping of multiple QTLs, as shown by our report of a second QTL on chromosome 12. The high resolution possible with this approach makes QTLs accessible to positional cloning.

Animals↗
PubMed1999

Quantitative-trait locus for specific language and reading deficits on chromosome 6p.

Reading disability (RD), or dyslexia, is a complex cognitive disorder manifested by difficulties in learning to read, in otherwise normal individuals. Individuals with RD manifest deficits in several reading and language skills. Previous research has suggested the existence of a quantitative-trait locus (QTL) for RD on the short arm of chromosome 6. In the present study, RD subjects' performance in several measures of word recognition and component skills of orthographic coding, phonological decoding, and phoneme awareness were individually subjected to QTL analysis, with a new sample of 126 sib pairs, by means of a multipoint mapping method and eight informative DNA markers on chromosome 6 (D6S461, D6S276, D6S105, D6S306, D6S258, D6S439, D6S291, and D6S1019). The results indicate significant linkage across a distance of at least 5 cM for deficits in orthographic (LOD = 3.10) and phonological (LOD = 2.42) skills, confirming previous findings.

Adult↗
PubMed1999

Combined linkage and association sib-pair analysis for quantitative traits.

An extension to current maximum-likelihood variance-components procedures for mapping quantitative-trait loci in sib pairs that allows a simultaneous test of allelic association is proposed. The method involves modeling of the allelic means for a test of association, with simultaneous modeling of the sib-pair covariance structure for a test of linkage. By partitioning of the mean effect of a locus into between- and within-sibship components, the method controls for spurious associations due to population stratification and admixture. The power and efficacy of the method are illustrated through simulation of various models of both real and spurious association.

Genetic Linkage↗
PubMed1998

Increase in power through multivariate analyses.

Power to detect genetic and environmental influences increases not only with sample size but also with the number of measurements through longitudinal and/or multivariate designs, if those measurements correlate with each other. Power simulations are presented for uni- through quadrivariate cases, with differing genetic and environmental parameters. Even though subject attrition is a problem for most longitudinal studies, the gain in power available may more than make up for this shortcoming in many situations. In terms of planning studies to examine genetic and environmental influences, power calculations should not only consider sample size but number of measurements on particular phenotypes and their intercorrelations.

Genotype↗