34 research outputs found
The Genetic contribution to solving the cocktail-party problem
Communicating in everyday situations requires solving the cocktail-party problem, or segregating the acoustic mixture into its constituent sounds and attending to those of most interest. Humans show dramatic variation in this ability, leading some to experience real-world problems irrespective of whether they meet criteria for clinical hearing loss. Here, we estimated the genetic contribution to cocktail-party listening by measuring speech-reception thresholds (SRTs) in 425 people from large families and ranging in age from 18 to 91 years. Roughly half the variance of SRTs was explained by genes (h 2 = 0.567). The genetic correlation between SRTs and hearing thresholds (HTs) was medium (Ï G = 0.392), suggesting that the genetic factors influencing cocktail-party listening were partially distinct from those influencing sound sensitivity. Aging and socioeconomic status also strongly influenced SRTs. These findings may represent a first step toward identifying genes for hidden hearing loss, or hearing problems in people with normal HTs
Genome-wide Linkage Analysis of Pulse Pressure in American Indians: The Strong Heart Study
Pulse pressure, a measure of central arterial stiffness and a predictor of cardiovascular mortality, has known genetic components
Linkage Analysis of Glomerular Filtration Rate in American Indians: The Strong Heart Family Study
American Indians have a disproportionately high rate of kidney disease likely due to a combination of increased environmental and genetic risk factors. In an attempt to localize genes influencing kidney disease risk factors, we performed a genome wide scan of estimated glomerular filtration rate on participants of the Strong Heart Family Study. Over 3 600 men and women from 13 American Indian tribes were recruited from 3 centers (Arizona, North and South Dakota, Oklahoma). Using SOLAR 2.1.2, multipoint variance component linkage analysis was performed in each center as well as the entire cohort after controlling for center effects. Two modeling strategies were utilized: model 1 incorporated age, sex and interaction terms and model 2 additionally controlled for diabetic status, systolic and diastolic blood pressure, body mass index, low density lipoproteins, high density lipoproteins, triglycerides and smoking status. Significant evidence for linkage in Arizona lay on 12p12.2 at 39cM nearest marker D12S310 (LOD=3.5). Additional loci with suggestive evidence for linkage were detected at 1p36.31 (LOD=2.0â2.3), 2q33.3 (LOD=1.8) and 9q34.2 (LOD=2.4). No significant evidence for additive interaction with diabetes, hypertension or obesity was noted. In conclusion, we found evidence for linkage of a quantitative trait locus influencing estimated glomerular filtration rate to a region of chromosome 12p in a large cohort of American Indians
Social- and Behavioral-Specific Genetic Effects on Blood Pressure Traits: The Strong Heart Family Study
Population studies have demonstrated an important role of social, behavioral, and environmental factors in blood pressure levels. Accounting for the genetic interaction of these factors may help to identify common blood pressure susceptibility alleles
Novel genetic loci associated with hippocampal volume
The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness
Linkage Analysis in the Presence of Errors III: Marker Loci and Their Map as Nuisance Parameters
this paper
Quantitative Trait Nucleotide Analysis Using Bayesian Model Selection
Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis
Contribution of Inbred Singletons to Variance Component Estimation of Heritability and Linkage
Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype
Type 2 diabetes (T2D) is a common, multifactorial disease that is influenced by genetic and environmental factors and their interactions. However, common variants identified by genome wide association studies (GWAS) explain only about 10% of the total trait variance for T2D and less than 5% of the variance for obesity, indicating that a large proportion of heritability is still unexplained. The transcriptomic approach described here uses quantitative gene expression and disease-related physiological data (deep phenotyping) to measure the direct correlation between the expression of specific genes and physiological traits. Transcriptomic analysis bridges the gulf between GWAS and physiological studies. Recent GWAS studies have utilized very large population samples, numbering in the tens of thousands (or even hundreds of thousands) of individuals, yet establishing causal functional relationships between strongly associated genetic variants and disease remains elusive. In light of the findings described below, it is appropriate to consider how and why transcriptomic approaches in small samples might be capable of identifying complex disease-related genes which are not apparent using GWAS in large samples