Accelerated matrix-vector multiplications for matrices involving genotype covariates with applications in genomic prediction.

In the last decade, a number of methods have been suggested to deal with large amounts of genetic data in genomic predictions. Yet, steadily growing population sizes and the suboptimal use of computational resources are pushing the practical application of these approaches to their limits. As an extension to the C/CUDA library , we have developed tailored solutions for the computation of genotype matrix multiplications which is a critical bottleneck in the empirical evaluation of many statistical models.

Assessing breed integrity of Göttingen Minipigs.

Background: Göttingen Minipigs (GMP) is the smallest commercially available minipig breed under a controlled breeding scheme and is globally bred in five isolated colonies. The genetic isolation harbors the risk of stratification which might compromise the identity of the breed and its usability as an animal model for biomedical and human disease. We conducted whole genome re-sequencing of two DNA-pools per colony to assess genomic differentiation within and between colonies.

MoBPS - Modular Breeding Program Simulator.

The R-package MoBPS provides a computationally efficient and flexible framework to simulate complex breeding programs and compare their economic and genetic impact. Simulations are performed on the base of individuals. MoBPS utilizes a highly efficient implementation with bit-wise data storage and matrix multiplications from the associated R-package miraculix allowing to handle large scale populations.

Best Prediction of the Additive Genomic Variance in Random-Effects Models.

The additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as estimating the unconditional (or prior) expectation of this random additive genomic variance, and result in a negligence of the contribution of linkage disequilibrium (LD). We introduce a novel best prediction (BP) approach for the additive genomic variance in both the current and the base population in the framework of genomic prediction using the genomic best linear unbiased prediction (gBLUP) method.

HaploBlocker: Creation of Subgroup-Specific Haplotype Blocks and Libraries.

The concept of haplotype blocks has been shown to be useful in genetics. Fields of application range from the detection of regions under positive selection to statistical methods that make use of dimension reduction. We propose a novel approach ("HaploBlocker") for defining and inferring haplotype blocks that focuses on linkage instead of the commonly used population-wide measures of linkage disequilibrium.

A Model for Carrier-Mediated Biological Signal Transduction Based on Equilibrium Ligand Binding Theory.

Different variants of a mathematical model for carrier-mediated signal transduction are introduced with focus on the odor dose-electrophysiological response curve of insect olfaction. The latter offers a unique opportunity to observe experimentally the effect of an alteration in the carrier molecule composition on the signal molecule-dependent response curve. Our work highlights the role of involved carrier molecules, which have largely been ignored in mathematical models for response curves in the past.

A Scale-Corrected Comparison of Linkage Disequilibrium Levels between Genic and Non-Genic Regions.

The understanding of non-random association between loci, termed linkage disequilibrium (LD), plays a central role in genomic research. Since causal mutations are generally not included in genomic marker data, LD between those and available markers is essential for capturing the effects of causal loci on localizing genes responsible for traits. Thus, the interpretation of association studies requires a detailed knowledge of LD patterns.

Correction: Accounting for Genetic Architecture Improves Sequence Based Genomic Prediction for a Drosophila Fitness Trait.

[This corrects the article DOI: 10.1371/journal.pone.

Accounting for genetic architecture improves sequence based genomic prediction for a Drosophila fitness trait.

The ability to predict quantitative trait phenotypes from molecular polymorphism data will revolutionize evolutionary biology, medicine and human biology, and animal and plant breeding. Efforts to map quantitative trait loci have yielded novel insights into the biology of quantitative traits, but the combination of individually significant quantitative trait loci typically has low predictive ability. Utilizing all segregating variants can give good predictive ability in plant and animal breeding populations, but gives little insight into trait biology.

Gene-based mapping and pathway analysis of metabolic traits in dairy cows.

The metabolic adaptation of dairy cows during the transition period has been studied intensively in the last decades. However, until now, only few studies have paid attention to the genetic aspects of this process. Here, we present the results of a gene-based mapping and pathway analysis with the measurements of three key metabolites, (1) non-esterified fatty acids (NEFA), (2) beta-hydroxybutyrate (BHBA) and (3) glucose, characterizing the metabolic adaptability of dairy cows before and after calving.

Origin Detection During Food-borne Disease Outbreaks - A Case Study of the 2011 EHEC/HUS Outbreak in Germany.

The key challenge during food-borne disease outbreaks, e.g. the 2011 EHEC/HUS outbreak in Germany, is the design of efficient mitigation strategies based on a timely identification of the outbreak's spatial origin.

A network-based kernel machine test for the identification of risk pathways in genome-wide association studies.

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity.

A novel kernel for correcting size bias in the logistic kernel machine test with an application to rheumatoid arthritis.

Objectives: The logistic kernel machine test (LKMT) is a testing procedure tailored towards high-dimensional genetic data. Its use in pathway analyses of case-control genome-wide association studies results from its computational efficiency and flexibility in incorporating additional information via the kernel. The kernel can be any positive definite function; unfortunately, its form strongly influences the test's power and bias.

Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster.

Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.

Predicting genetic values: a kernel-based best linear unbiased prediction with genomic data.

Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different gene-action scenarios. Two different kriging methods ["universal kriging" (UK) and "simple kriging" (SK)] are presented.

A risk index for characterising flow pattern in soils using dye tracer distributions.

A novel risk index for the vulnerability of groundwater by pollutants is defined as the form parameter of the Pareto distribution and estimated from dye tracer experiments. The Pareto distribution appears as the limit distribution of the extreme value theory, which has been applied to an idealized model of drops that run along a path. The properties of the risk index are investigated by a Monte Carlo study, where the paths are modelled by means of Gaussian random fields.