Simultaneous fine mapping of multiple closely linked quantitative trait Loci using combined linkage disequilibrium and linkage with a general pedigree

Bayesian model selection for multiple QTLs mapping combining linkage disequilibrium and linkage

Linkage disequilibrium (LD) mapping is able to localize quantitative trait loci (QTL) within a rather small region (e.g. 2 cM), which is much narrower than linkage analysis (LA, usually 20 cM). The multilocus LD method utilizes haplotype information around putative mutation and takes historical recombination events into account, and thus provides a powerful method for further fine mapping. However, sometimes there are more than one QTLs in the region being studied. In this study, the Bayesian model selection implemented via the Markov chain Monte Carlo (MCMC) method is developed for fine mapping of multiple QTLs using haplotype information in a small region. The method combines LD as well as linkage information. A series of simulation experiments were conducted to investigate the behavior of the method. The results showed that this new multiple QTLs method was more efficient in separating closely linked QTLs than single-marker association studies.

A fast expectation-maximum algorithm for fine-scale QTL mapping

The recent technology of the single-nucleotide-polymorphism (SNP) array makes it possible to genotype millions of SNP markers on genome, which in turn requires to develop fast and efficient method for fine-scale quantitative trait loci (QTL) mapping. The single-marker association (SMA) is the simplest method for fine-scale QTL mapping, but it usually shows many false-positive signals and has low QTL-detection power. Compared with SMA, the haplotype-based method of Meuwissen and Goddard who assume QTL effect to be random and estimate variance components (VC) with identity-by-descent (IBD) matrices that inferred from unknown historic population is more powerful for fine-scale QTL mapping; furthermore, their method also tends to show continuous QTL-detection profile to diminish many false-positive signals. However, as we know, the variance component estimation is usually very time consuming and difficult to converge. Thus, an extremely fast EMF (Expectation-Maximization algorithm under Fixed effect model) is proposed in this research, which assumes a biallelic QTL and uses an expectation-maximization (EM) algorithm to solve model effects. The results of simulation experiments showed that (1) EMF was computationally much faster than VC method; (2) EMF and VC performed similarly in QTL detection power and parameter estimations, and both outperformed the paired-marker analysis and SMA. However, the power of EMF would be lower than that of VC if the QTL was multiallelic.

QTL mapping in outbred half-sib families using Bayesian model selection

In this article, we propose a model selection method, the Bayesian composite model space approach, to map quantitative trait loci (QTL) in a half-sib population for continuous and binary traits. In our method, the identity-by-descent-based variance component model is used. To demonstrate the performance of this model, the method was applied to map QTL underlying production traits on BTA6 in a Chinese half-sib dairy cattle population. A total of four QTLs were detected, whereas only one QTL was identified using the traditional least square (LS) method. We also conducted two simulation experiments to validate the efficiency of our method. The results suggest that the proposed method based on a multiple-QTL model is efficient in mapping multiple QTL for an outbred half-sib population and is more powerful than the LS method based on a single-QTL model.

Using the realized relationship matrix to disentangle confounding factors for the estimation of genetic variance components of complex traits

BACKGROUND

In the analysis of complex traits, genetic effects can be confounded with non-genetic effects, especially when using full-sib families. Dominance and epistatic effects are typically confounded with additive genetic and non-genetic effects. This confounding may cause the estimated genetic variance components to be inaccurate and biased.

METHODS

In this study, we constructed genetic covariance structures from whole-genome marker data, and thus used realized relationship matrices to estimate variance components in a heterogenous population of approximately 2200 mice for which four complex traits were investigated. These mice were genotyped for more than 10,000 single nucleotide polymorphisms (SNP) and the variances due to family, cage and genetic effects were estimated by models based on pedigree information only, aggregate SNP information, and model selection for specific SNP effects.

RESULTS AND CONCLUSIONS

We show that the use of genome-wide SNP information can disentangle confounding factors to estimate genetic variances by separating genetic and non-genetic effects. The estimated variance components using realized relationship were more accurate and less biased, compared to those based on pedigree information only. Models that allow the selection of individual SNP in addition to fitting a relationship matrix are more efficient for traits with a significant dominance variance.

Bayesian shrinkage mapping of quantitative trait loci in variance component models

Background

In this article, I propose a model-selection-free method to map multiple quantitative trait loci (QTL) in variance component model, which is useful in outbred populations. The new method can estimate the variance of zero-effect QTL infinitely to zero, but nearly unbiased for non-zero-effect QTL. It is analogous to Xu's Bayesian shrinkage estimation method, but his method is based on allelic substitution model, while the new method is based on the variance component models.

Results

Extensive simulation experiments were conducted to investigate the performance of the proposed method. The results showed that the proposed method was efficient in mapping multiple QTL simultaneously, and moreover it was more competitive than the reversible jump MCMC (RJMCMC) method and may even out-perform it.

Conclusions

The newly developed Bayesian shrinkage method is very efficient and powerful for mapping multiple QTL in outbred populations.

Understanding the use of Bayes factor for testing candidate genes

After quantitative trait loci (QTL) detection, one of the main objectives of research is to identify the causal mutation explaining phenotypic differences. Candidate genes are usually selected according to the physiological mechanism of the trait and their location within the same region of the QTL. After detection of any polymorphism at the candidate gene sequence, it is important to determine whether the detected mutation is the one that causes the phenotypic variation. This is not, however, an easy task, because of the linkage disequilibrium between the genes located in the same region. Several methods have been proposed that consider the neutral marker information in validating the involvement of candidate genes. However, some statistical information may be lost because of the presence of both the QTL and candidate gene effects in the model of analysis. Here, the Bayes factor is suggested as an alternative and a procedure for its calculation between candidate gene and QTL models is presented. The procedure is illustrated with a simulation study and with an example consisting of three SNPs detected at the leptin receptor (LEPR) in an experimental intercross between Iberian and Landrace pigs. The results indicate that the Bayes factor procedure is more powerful than the classical approach.

Look before you leap: a new approach to mapping QTL

In this paper, we present an innovative and powerful approach for mapping quantitative trait loci (QTL) in experimental populations. This deviates from the traditional approach of (composite) interval mapping which uses a QTL profile to simultaneously determine the number and location of QTL. Instead, we look before we leap by employing separate detection and localization stages. In the detection stage, we use an iterative variable selection process coupled with permutation to identify the number and synteny of QTL. In the localization stage, we position the detected QTL through a series of one-dimensional interval mapping scans. Results from a detailed simulation study and real analysis of wheat data are presented. We achieve impressive increases in the power of QTL detection compared to composite interval mapping. We also accurately estimate the size and position of QTL. An R library, DLMap, implements the methods described here and is freely available from CRAN ( http://cran.r-project.org/ ).

Bayesian shrinkage mapping for multiple QTL in half-sib families

Recently, an effective Bayesian shrinkage estimation method has been proposed for mapping QTL in inbred line crosses. However, with regard to outbred populations, such as half-sib populations with maternal information unavailable, it is not straightforward to utilize such a shrinkage estimation for QTL mapping. The reasons are: (1) the linkage phase of markers in the outbred population is usually unknown; and (2) only paternal genotypes can be used for inferring QTL genotypes of offspring. In this article, a novel Bayesian shrinkage method was proposed for mapping QTL under the half-sib design using a mixed model. A simulation study clearly demonstrated that the proposed method was powerful for detecting multiple QTL. In addition, we applied the proposed method to map QTL for economic traits in the Chinese dairy cattle population. Two or more novel QTL harbored in the chromosomal region were detected for each trait of interest, whereas only one QTL was found using traditional maximum likelihood analyses in our earlier studies. This further validated that our shrinkage estimation method could perform well in empirical data analyses and had practical significance in the field of linkage studies for outbred populations.

Genetic mapping of quantitative trait loci for resistance to Haemonchus contortus in sheep

This paper presents results from a mapping experiment to detect quantitative trait loci (QTL) for resistance to Haemonchus contortus infestation in merino sheep. The primary trait analysed was faecal worm egg count in response to artificial challenge at 6 months of age. In the first stage of the experiment, whole genome linkage analysis was used for broad-scale mapping. The animal resource used was a designed flock comprising 571 individuals from four half-sib families. The average marker spacing was about 20 cM. For the primary trait, 11 QTL (as chromosomal/family combinations) were significant at the 5% chromosome-wide level, with allelic substitution effects of between 0.19 and 0.38 phenotypic standard deviation units. In general, these QTL did not have a significant effect on faecal worm egg count recorded at 13 months of age. In the second stage of the experiment, three promising regions (located on chromosomes 1, 3 and 4) were fine-mapped. This involved typing more closely spaced markers on individuals from the designed flock as well as an additional 495 individuals selected from a related population with a deeper pedigree. Analysis was performed using a linkage disequilibrium-linkage approach, under additive, dominant and multiple QTL models. Of these, the multiple QTL model resulted in the most refined QTL positions, with resolutions of <10 cM achieved for two regions. Because of the moderate size of effect of the QTL, and the apparent age and/or immune status specificity of the QTL, it is suggested that a panel of QTL will be required for significant genetic gains to be achieved within industry via marker-assisted selection.

Predicting unobserved phenotypes for complex traits from whole-genome SNP data

Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs.

Results from recent genome-wide association studies indicate that for most complex traits, there are many loci that contribute to variation in observed phenotype and that the effect of a single variant (single nucleotide polymorphism, SNP) on a phenotype is small. Here, we propose a method that combines the effects of multiple SNPs to make a prediction of a phenotype that has not been observed. We apply the method to data on mice, using phenotypic and genomic data from some individuals to predict phenotypes in other, either related or unrelated, individuals. We find that correlations between predicted and actual phenotypes are in the range of 0.4 to 0.9. The method also shows that the SNPs used in the prediction appear in regions that are known to contain genes associated with the traits studied. The prediction of unobserved phenotypes from high-density SNP data and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial breeding programs.