Missing Heritability in the Tails of Quantitative Traits? A Simulation Study on the Impact of Slightly Altered True Genetic Models

Objective: Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations. Methods: We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression. Results: Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption. Conclusions: Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes. Copyright (C) 2011 S. Karger AG, Base

Identifying variants that contribute to linkage for dichotomous and quantitative traits in extended pedigrees

Compared to genome-wide association analysis, linkage analysis is less influenced by allelic heterogeneity. The use of linkage information in large families should provide a great opportunity to identify less frequent variants. We perform a linkage scan for both dichotomous and quantitative traits in eight extended families. For the dichotomous trait, we identified one linkage region on chromosome 4q. For quantitative traits, we identified two regions on chromosomes 4q and 6p for Q1 and one region on chromosome 6q for Q2. To identify variants that contribute to these linkage signals, we performed standard association analysis in genomic regions of interest. We also screened less frequent variants in the linkage region based on the risk ratio and phenotypic distribution among carriers. Two rare variants at VEGFC and one common variant on chromosome 4q conferred the greatest risk for the dichotomous trait. We identified two rare variants on chromosomes 4q (VEGFC) and 6p (VEGFA) that explain 12.4% of the total phenotypic variance of trait Q1. We also identified four variants (including one at VNN3) on chromosome 6q that are able to drop the linkage LOD from 3.7 to 1.0. These results suggest that the use of classical linkage and association methods in large families can provide a useful approach to identifying variants that are responsible for diseases and complex traits in families

Likelihood Ratio Test process for Quantitative Trait Locus detection

International audienceWe consider the likelihood ratio test (LRT) process related to the test of the absence of QTL (a QTL denotes a quantitative trait locus, i.e. a gene with quantitative effect on a trait) on the interval [0,T] representing a chromosome. The observation is the trait and the composition of the genome at some locations called ''markers''. We give the asymptotic distribution of this LRT process under the null hypothesis that there is no QTL on [0,T] and under local alternatives with a QTL at t* on [0,T]. We show that the LRT is asymptotically the square of some Gaussian process. We give a description of this process as an '' non-linear interpolated and normalized process ''. We propose a simple method to calculate the maximum of the LRT process using only statistics on markers and their ratio. This gives a new method to calculate thresholds for QTL detection

Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'

We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

Parameter Estimation and Quantitative Parametric Linkage Analysis with GENEHUNTER-QMOD

Objective: We present a parametric method for linkage analysis of quantitative phenotypes. The method provides a test for linkage as well as an estimate of different phenotype parameters. We have implemented our new method in the program GENEHUNTER-QMOD and evaluated its properties by performing simulations. Methods: The phenotype is modeled as a normally distributed variable, with a separate distribution for each genotype. Parameter estimates are obtained by maximizing the LOD score over the normal distribution parameters with a gradient-based optimization called PGRAD method. Results: The PGRAD method has lower power to detect linkage than the variance components analysis (VCA) in case of a normal distribution and small pedigrees. However, it outperforms the VCA and Haseman-Elston regression for extended pedigrees, nonrandomly ascertained data and non-normally distributed phenotypes. Here, the higher power even goes along with conservativeness, while the VCA has an inflated type I error. Parameter estimation tends to underestimate residual variances but performs better for expectation values of the phenotype distributions. Conclusion: With GENEHUNTER-QMOD, a powerful new tool is provided to explicitly model quantitative phenotypes in the context of linkage analysis. It is freely available at http://www.helmholtz-muenchen.de/genepi/downloads. Copyright (C) 2012 S. Karger AG, Base

Safe and complete contig assembly via omnitigs

Contig assembly is the first stage that most assemblers solve when reconstructing a genome from a set of reads. Its output consists of contigs -- a set of strings that are promised to appear in any genome that could have generated the reads. From the introduction of contigs 20 years ago, assemblers have tried to obtain longer and longer contigs, but the following question was never solved: given a genome graph $G$ (e.g. a de Bruijn, or a string graph), what are all the strings that can be safely reported from $G$ as contigs? In this paper we finally answer this question, and also give a polynomial time algorithm to find them. Our experiments show that these strings, which we call omnitigs, are 66% to 82% longer on average than the popular unitigs, and 29% of dbSNP locations have more neighbors in omnitigs than in unitigs.Comment: Full version of the paper in the proceedings of RECOMB 201

Feasibility of identifying families for genetic studies of birth defects using the National Health Interview Survey

BACKGROUND: The purpose of this study was to determine whether the National Health Interview Survey is a useful source to identify informative families for genetic studies of birth defects. METHODS: The 1994/1995 National Health Interview Survey (NHIS) was used to identify households where individuals with two or more birth defects reside. Four groups of households were identified: 1) single non-familial (one individual with one birth defect); 2) single familial (more than one individual with one birth defect); 3) multiple non-familial (one individual with more than one birth defect), and 4) multiple familial (more than one individual with more than one birth defect). The March 2000 U.S. Census on households was used to estimate the total number of households in which there are individuals with birth defects. RESULTS: Of a total of 28,094 households and surveyed about birth defects and impairments, 1,083 single non-familial, 55 multiple non-familial, 54 single familial, and 8 multiple familial households were identified. Based on the 2000 U.S. census, it is estimated that there are 4,472,385 households where at least one person has one birth defect in the United States and in 234,846 of them there are at least two affected individuals. Western states had the highest prevalence rates. CONCLUSIONS: Population-based methods, such as the NHIS, are modestly useful to identify the number and the regions where candidate families for genetic studies of birth defects reside. Clinic based studies and birth defects surveillance systems that collect family history offer better probability of ascertainment