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  • 1. Nelson, Ronald M.
    et al.
    Shen, Xia
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software2011Inngår i: BMC Research Notes, ISSN 1756-0500, E-ISSN 1756-0500, Vol. 4, nr 154Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background

    qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl.

    Findings

    Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL.

    Conclusion

    qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.

  • 2.
    Rönnegård, Lars
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Shen, Xia
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Alam, Moudud
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Hglm: A package for fitting hierarchical generalized linear models2010Inngår i: The R Journal, ISSN 2073-4859, E-ISSN 2073-4859, Vol. 2, nr 2, s. 20-28Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

  • 3.
    Shen, Xia
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    A Monte Carlo Full Likelihood Approach in Variance Component Quantitative Trait Loci Analysis2009Inngår i: 13th QTLMAS Workshop, Wageningen, The Netherlands, 2009Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The identity-by-descent (IBD) matrix is the core of the variance component QTL model. The true IBD matrix comes from a distribution of IBD matrices given the marker information, but its expectation is normally used in QTL analysis. This gives incorrect likelihood values since the extra uncertainty in estimating the IBD matrix is not included. Previous studies have concentrated on small pedigrees where the correct likelihood can be derived. For large pedigrees this approach is not feasible. We therefore developed a Monte Carlo method for calculating the likelihood in- corporating the uncertainty of the estimated IBD matrix. The aim of this study is to implement the Monte Carlo Full Likelihood (MCFL) algorithm and to compare the true likelihood with the like- lihood based on the expected IBD matrix for large pedigrees. Our simulation results show that the likelihood based on the expected IBD matrix approximates the true likelihood well and may there- fore justify the use of the expected IBD matrix in empirical QTL analysis. Our MCFL method can actually be computationally more efficient than the expectation method for large pedigrees with a small founder generation, because the rank of the true IBD matrix is much lower than the rank of the expected IBD matrix, especially when the genetic markers are highly informative. Using the IBD matrices produced in our MCFL method we may also simplify the modeling of epistasis for linked QTL.

  • 4.
    Shen, Xia
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Novel Statistical Methods in Quantitative Genetics: Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation2012Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models.

    In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision. 

    Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction.

    A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes. 

    The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

  • 5.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Alam, Moudud
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Fikse, Freddy
    Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    A novel generalized ridge regression method for quantitative genetics2013Inngår i: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 193, nr 4, s. 1255-1268Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithmfor situations where the number of parameters largely exceeds the number of observations. The computationally demanding parts of the method depend mainly on the number ofobservations and not the number of parameters. The algorithm was implemented in the R package bigRR based on the previously developed package hglm. Using such an approach, a heteroscedastic effects model (HEM) was also developed, implemented and tested. Theefficiency for different data sizes were evaluated via simulation. The method was tested for a bacteria-hypersensitive trait in a publicly available Arabidopsis dataset including 84 inbred lines and 216 130 SNPs. The computation of all the SNP effects required less than10 seconds using a single 2.7 GHz core. The advantage in run-time makes permutationtest feasible for such a whole-genome model, so that a genome-wide significance threshold can be obtained. HEM was found to be more robust than ordinary RR (a.k.a. SNPBLUP) in terms of QTL mapping, because SNP specific shrinkage was applied instead of acommon shrinkage. The proposed algorithm was also assessed for genomic evaluation and was shown to give better predictions than ordinary RR.

  • 6.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. Swedish University of Agricultural Sciences.
    Carlborg, Örjan
    Swedish University of Agricultural Sciences.
    Beware of risk for increased false positive rates in genome-wide association studies for phenotypic variability2013Inngår i: Frontiers in Genetics, ISSN 1664-8021, E-ISSN 1664-8021, nr 4Artikkel i tidsskrift (Fagfellevurdert)
  • 7.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Pettersson, Mats
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana2012Inngår i: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 8, nr 8, artikkel-id e1002839Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The phenotypic effect of a gene is normally described by the mean-difference between alternative genotypes. A gene may, however, also influence the phenotype by causing a difference in variance between genotypes. Here, we reanalyze a publicly available Arabidopsis thaliana dataset [1] and show that genetic variance heterogeneity appears to be as common as normal additive effects on a genomewide scale. The study also develops theory to estimate the contributions of variance differences between genotypes to the phenotypic variance, and this is used to show that individual loci can explain more than 20% of the phenotypic variance. Two well-studied systems, cellular control of molybdenum level by the ion-transporter MOT1 and flowering-time regulation by the FRI-FLC expression network, and a novel association for Leaf serration are used to illustrate the contribution of major individual loci, expression pathways, and gene-by-environment interactions to the genetic variance heterogeneity.

  • 8.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty2010Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

  • 9.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps2011Inngår i: BMC Proceedings, ISSN 1753-6561, E-ISSN 1753-6561, Proc. 14th European Workshop on QTL Mapping and Marker Assisted Selection (QTL-MAS), nr 5(Suppl 3)Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background Genome-wide dense markers have been used to detect genes and estimate relative genetic values. Among many methods, Bayesian techniques have been widely used and shown to be powerful in genome-wide breeding value estimation and association studies. However, computation is known to be intensive under the Bayesian framework, and specifying a prior distribution for each parameter is always required for Bayesian computation. We propose the use of hierarchical likelihood to solve such problems. Results Using double hierarchical generalized linear models, we analyzed the simulated dataset provided by the QTLMAS 2010 workshop. Marker-specific variances estimated by double hierarchical generalized linear models identified the QTL with large effects for both the quantitative and binary traits. The QTL positions were detected with very high accuracy. For young individuals without phenotypic records, the true and estimated breeding values had Pearson correlation of 0.60 for the quantitative trait and 0.72 for the binary trait, where the quantitative trait had a more complicated genetic architecture involving imprinting and epistatic QTL. Conclusions Hierarchical likelihood enables estimation of marker-specific variances under the likelihoodist framework. Double hierarchical generalized linear models are powerful in localizing major QTL and computationally fast.

  • 10.
    Shen, Xia
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carlborg, Örjan
    How to deal with genotype uncertainty in variance component quantitative trait loci analyses2011Inngår i: Genetics Research, ISSN 0016-6723, Vol. 93, nr 5, s. 333-342Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Dealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

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