High-throughput computational methods and software for quantitative trait locus (QTL) mapping

De afgelopen jaren zijn vele nieuwe technologieen zoals Tiling arrays en High throughput DNA sequencing een belangrijke rol gaan spelen binnen het onderzoeksveld van de systeem genetica. Voor onderzoekers is het extreem belangrijk om te begrijpen dat deze methodes hun manier van werken zullen gaan beinvloeden. Deit proefschrift beschrijft mogelijke oplossingen voor deze 'Big Data' lawine die systemen genetica heeft getroffen.Dit proefschrift beschrijft de werkzaamheden uitgevoerd aan het Groningen Bioinformatics Centre om slimmere en geoptimaliseerde algoritmen zoals Pheno2Geno en MQM te ontwikkelen en een systeem om 'collaborative' research mogelijk te maken genaamd xQTL werkbank om door middel van high-throughput systemen genetica data te analyseren.In recent years many new technologies such as tiling arrays and high-throughput sequencinghave come to play an important role in systems genetics research. For researchers it is ofthe utmost importance to understand how this affects their research. This work describespossible solutions to this ‘Big Data’ avalanche which has hit systems genetics.This thesis describes the work carried out during the author’s 4 year PHD project at theGroningen Bioinformatics Centre to develop smarter and more optimized algorithms suchas Pheno2Geno and MQM, and to use a collaborative approach such as xQTL workbench tostore and analyse high-throughput systems genetics data

netgwas: An R Package for Network-Based Genome-Wide Association Studies

Graphical models are powerful tools for modeling and making statistical inferences regarding complex associations among variables in multivariate data. In this paper we introduce the R package netgwas, which is designed based on undirected graphical models to accomplish three important and interrelated goals in genetics: constructing linkage map, reconstructing linkage disequilibrium (LD) networks from multi-loci genotype data, and detecting high-dimensional genotype-phenotype networks. The netgwas package deals with species with any chromosome copy number in a unified way, unlike other software. It implements recent improvements in both linkage map construction (Behrouzi and Wit, 2018), and reconstructing conditional independence network for non-Gaussian continuous data, discrete data, and mixed discrete-and-continuous data (Behrouzi and Wit, 2017). Such datasets routinely occur in genetics and genomics such as genotype data, and genotype-phenotype data. We demonstrate the value of our package functionality by applying it to various multivariate example datasets taken from the literature. We show, in particular, that our package allows a more realistic analysis of data, as it adjusts for the effect of all other variables while performing pairwise associations. This feature controls for spurious associations between variables that can arise from classical multiple testing approach. This paper includes a brief overview of the statistical methods which have been implemented in the package. The main body of the paper explains how to use the package. The package uses a parallelization strategy on multi-core processors to speed-up computations for large datasets. In addition, it contains several functions for simulation and visualization. The netgwas package is freely available at https://cran.r-project.org/web/packages/netgwasComment: 32 pages, 9 figures; due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF fil

Finding the Optimal Imputation Strategy for Small Cattle Populations

The imputation from lower density SNP chip genotypes to whole-genome sequence level is an established approach to generate high density genotypes for many individuals. Imputation accuracy is dependent on many factors and for small cattle populations such as the endangered German Black Pied cattle (DSN), determining the optimal imputation strategy is especially challenging since only a low number of high density genotypes is available. In this paper, the accuracy of imputation was explored with regard to (1) phasing of the target population and the reference panel for imputation, (2) comparison of a 1-step imputation approach, where 50 k genotypes are directly imputed to sequence level, to a 2-step imputation approach that used an intermediate step imputing first to 700 k and subsequently to sequence level, (3) the software tools Beagle and Minimac, and (4) the size and composition of the reference panel for imputation. Analyses were performed for 30 DSN and 30 Holstein Frisian cattle available from the 1000 Bull Genomes Project. Imputation accuracy was assessed using a leave-one-out cross validation procedure. We observed that phasing of the target populations and the reference panels affects the imputation accuracy significantly. Minimac reached higher accuracy when imputing using small reference panels, while Beagle performed better with larger reference panels. In contrast to previous research, we found that when a low number of animals is available at the intermediate imputation step, the 1-step imputation approach yielded higher imputation accuracy compared to a 2-step imputation. Overall, the size of the reference panel for imputation is the most important factor leading to higher imputation accuracy, although using a larger reference panel consisting of a related but different breed (Holstein Frisian) significantly reduced imputation accuracy. Our findings provide specific recommendations for populations with a limited number of high density genotyped or sequenced animals available such as DSN. The overall recommendation when imputing a small population are to (1) use a large reference panel of the same breed, (2) use a large reference panel consisting of diverse breeds, or (3) when a large reference panel is not available, we recommend using a smaller same breed reference panel without including a different related breed

Review: Genetic and protein variants of milk caseins in goats

The milk casein genes in goats, are highly polymorphic genes with numerous synonymous and non-synonymous mutations. So far, 20 protein variants have been reported in goats for alpha-S1-casein, eight for beta-casein, 14 for alpha-S2-casein, and 24 for kappa-casein. This review provides a comprehensive overview on identified milk casein protein variants in goat and non-coding DNA sequence variants with some affecting the expression of the casein genes. The high frequency of some casein protein variants in different goat breeds and geographical regions might reflect specific breeding goals with respect to milk processing characteristics, properties for human nutrition and health, or adaptation to the environment. Because protein names, alongside the discovery of protein variants, go through a historical process, we linked old protein names with new ones that reveal more genetic variability. The haplotypes across the cluster of the four genetically linked casein genes are recommended as a valuable genetic tool for discrimination between breeds, managing genetic diversity within and between goat populations, and breeding strategies. The enormous variation in the casein proteins and genes is crucial for producing milk and dairy products with different properties for human health and nutrition, and for genetic improvement depending on local breeding goals

A 5′ UTR Mutation Contributes to Down-Regulation of Bbs7 in the Berlin Fat Mouse

The Bardet–Biedl Syndrome 7 (Bbs7) gene was identified as the most likely candidate gene causing juvenile obesity in the Berlin Fat Mouse Inbred (BFMI) line. Bbs7 expression is significantly lower in the brain, adipose tissue, and liver of BFMI mice compared to lean C57BL/6NCrl (B6N) mice. A DNA sequence comparison between BFMI and B6N revealed 16 sequence variants in the Bbs7 promoter region. Here, we tested if these mutations contribute to the observed differential expression of Bbs7. In a cell-based dual-luciferase assay, we compared the effects of the BFMI and the B6N haplotypes of different regions of the Bbs7 promotor on the reporter gene expression. A single-nucleotide polymorphism (SNP) was identified causing a significant reduction in the reporter gene expression. This SNP (rs29947545) is located in the 5′ UTR of Bbs7 at Chr3:36.613.350. The SNP is not unique to BFMI mice but also occurs in several other mouse strains, where the BFMI allele is not associated with lower Bbs7 transcript amounts. Thus, we suggest a compensatory mutation in the other mouse strains that keeps Bbs7 expression at the normal level. This compensatory mechanism is missing in BFMI mice and the cell lines tested

Weed Suppression in Only-Legume Cover Crop Mixtures

Weed suppression is a potential benefit of cover crop mixtures, as species diversity may allow for combining early and late-season competition with weeds. Here, we studied if this is possible for only-legume mixtures containing species with different growth rates, by testing two legumes, alsike clover (AC; Trifolium hybridum L.) and black medic (BM; Medicago lupulina L.) in two field trials sown in 2016 and 2017. Five AC:BM ratios (100:0, 67:33, 50:50, 33:67, and 0:100) were grown at three densities (50%, 100%, and 150% of recommended seed density). Cover crop and weed aboveground biomass (CCB and WB, respectively) were harvested three times, after establishment in spring (H1), in summer (H2), and in autumn after mulching (H3). Compared to fallow plots, all monocultures and mixtures showed early-season weed suppression in terms of biomass production and more efficiency over time with an average reduction of 42%, 52%, and 96% in 2016, and 39%, 55%, and 89% in 2017 at H1, H2, and H3, respectively. Out of 54 mixture treatments, only eight mixtures showed stronger weed suppression than monocultures. Mixtures reduced WB by 28%, as an average value, in 2017 compared to the respective monocultures, but not significantly in 2016, indicating that the crop diversity effect on weeds was dependent on the growing environment. Weed suppression was significantly higher at 100% and 150% seed density than 50%, but no significant differences were determined between 100% and 150% seed density. After mulching, no density effect was observed on CCB and WB. In conclusion, AC and BM can be used as a keystone species on weed suppression for sustainable agriculture as they possess plasticity to suppress weeds when higher biomass productivity is limited by environmental conditions. However, their diversity effects are time and condition dependent. Appropriate seed density and mulching can successfully be employed in weed management, but seed density may not have an effect after mulching

Genomic diversity and relationship analyses of endangered German Black Pied cattle (DSN) to 68 other taurine breeds based on whole-genome sequencing

German Black Pied cattle (Deutsches Schwarzbuntes Niederungsrind, DSN) are an endangered dual-purpose cattle breed originating from the North Sea region. The population comprises about 2,500 cattle and is considered one of the ancestral populations of the modern Holstein breed. The current study aimed at defining the breeds closest related to DSN cattle, characterizing their genomic diversity and inbreeding. In addition, the detection of selection signatures between DSN and Holstein was a goal. Relationship analyses using fixation index (FST), phylogenetic, and admixture analyses were performed between DSN and 68 other breeds from the 1000 Bull Genomes Project. Nucleotide diversity, observed heterozygosity, and expected heterozygosity were calculated as metrics for genomic diversity. Inbreeding was measured as excess of homozygosity (FHom) and genomic inbreeding (FRoH) through runs of homozygosity (RoHs). Region-wide FST and cross-population-extended haplotype homozygosity (XP-EHH) between DSN and Holstein were used to detect selection signatures between the two breeds, and RoH islands were used to detect selection signatures within DSN and Holstein. DSN showed a close genetic relationship with breeds from the Netherlands, Belgium, Northern Germany, and Scandinavia, such as Dutch Friesian Red, Dutch Improved Red, Belgian Red White Campine, Red White Dual Purpose, Modern Angler, Modern Danish Red, and Holstein. The nucleotide diversity in DSN (0.151%) was higher than in Holstein (0.147%) and other breeds, e.g., Norwegian Red (0.149%), Red White Dual Purpose (0.149%), Swedish Red (0.149%), Hereford (0.145%), Angus (0.143%), and Jersey (0.136%). The FHom and FRoH values in DSN were among the lowest. Regions with high FST between DSN and Holstein, significant XP-EHH regions, and RoH islands detected in both breeds harbor candidate genes that were previously reported for milk, meat, fertility, production, and health traits, including one QTL detected in DSN for endoparasite infection resistance. The selection signatures between DSN and Holstein provide evidence of regions responsible for the dual-purpose properties of DSN and the milk type of Holstein. Despite the small population size, DSN has a high level of diversity and low inbreeding. FST supports its relatedness to breeds from the same geographic origin and provides information on potential gene pools that could be used to maintain diversity in DSN