Publications

Abstract

First paragraph: The study of the transmission of sign languages can give novel insights into the transmission of spoken languages1 and, more generally, into gene–culture coevolution. Over the years, several papers related to the persistence of sign language have been
reported.2–6 All of these studies have emphasized the role of assortative (non-random) mating by deafness state (ie, a tendency for deaf individuals to partner together) for increasing the frequency of recessive deafness, and hence for the persistence of sign language in a population.

Abstract

Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype

Abstract

BACKGROUND: There is a growing interest in the study of the genetic origins of comorbidity, a direct consequence of the recent findings of genetic loci that are seemingly linked to more than one disorder. There are several potential causes for these shared regions of linkage, but one possibility is that these loci may harbor genes with manifold effects. The established genetic correlation between reading disability (RD) and attention-deficit/hyperactivity disorder (ADHD) suggests that their comorbidity is due at least in part to genes that have an impact on several phenotypes, a phenomenon known as pleiotropy. METHODS: We employ a bivariate linkage test for selected samples that could help identify these pleiotropic loci. This linkage method was employed to carry out the first bivariate genome-wide analysis for RD and ADHD, in a selected sample of 182 sibling pairs. RESULTS: We found evidence for a novel locus at chromosome 14q32 (multipoint LOD=2.5; singlepoint LOD=3.9) with a pleiotropic effect on RD and ADHD. Another locus at 13q32, which had been implicated in previous univariate scans of RD and ADHD, seems to have a pleiotropic effect on both disorders. 20q11 is also suggested as a pleiotropic locus. Other loci previously implicated in RD or ADHD did not exhibit bivariate linkage. CONCLUSIONS: Some loci are suggested as having pleiotropic effects on RD and ADHD, while others might have unique effects. These results highlight the utility of this bivariate linkage method to study pleiotropy.

Abstract

Family and twin studies consistently demonstrate a significant role for genetic factors in the aetiology of the reading disorder dyslexia. However, dyslexia is complex at both the genetic and phenotypic levels, and currently the nature of the core deficit or deficits remains uncertain. Traditional approaches for mapping disease genes, originally developed for single-gene disorders, have limited success when there is not a simple relationship between genotype and phenotype. Recent advances in high-throughput genotyping technology and quantitative statistical methods have made a new approach to identifying genes involved in complex disorders possible. The method involves assessing the genetic similarity of many sibling pairs along the lengths of all their chromosomes and attempting to correlate this similarity with that of their phenotypic scores. We are adopting this approach in an ongoing genome-wide search for genes involved in dyslexia susceptibility, and have already successfully applied the method by replicating results from previous studies suggesting that a quantitative trait locus at 6p21.3 influences reading disability.