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Heidaritabar, M. (2016). Genomic selection in egg-laying chickens. PhD thesis, Wageningen University, the Netherlands In recent years, prediction of genetic values with DNA markers, or genomic selection (GS), has become a very intense field of research. Many initial studies on GS have focused on the accuracy of predicting the genetic values with different genomic prediction methods. In this thesis, I assessed several aspects of GS. I started with evaluating results of GS against results of traditional pedigree-based selection (BLUP) in data from a selection experiment that applied both methods side by side. The impact of traditional selection and GS on the overall genome variation as well as the overlap between regions selected by GS and the genomic regions predicted to affect the traits were assessed. The impact of selection on genome variation was assessed by measuring changes in allele frequencies that allowed the identification of regions in the genome where changes must be due to selection. These frequency changes were shown to be larger than what could be expected from random fluctuations, indicating that selection is really affecting the allele frequencies and that this effect is stronger in GS compared with BLUP. Next, concordance was tested between the selected regions and regions that affect the traits, as detected by a genome-wide association study. Results showed a low concordance overall between the associated regions and the selected regions. However, markers in associated regions did show larger changes in allele frequencies compared with the average changes across the genome. The selection experiment was performed using a medium density of DNA markers (60K). I subsequently explored the potential benefits of whole-genome sequence data for GS by comparing prediction accuracy from imputed sequence data with the accuracy obtained from the 60K genotypes. Before sequencing, the selection of key animals that should be sequenced to maximize imputation accuracy was assessed with the original 60K genotypes. The accuracy of genotype imputation from lower density panels using a small number of selected key animals as reference was compared with a scenario where random animals were used as the reference population. Even with a very small number of animals as reference, reasonable imputation accuracy could be obtained. Moreover, selecting key animals as reference considerably improved imputation accuracy of rare alleles compared with a set of random reference animals. While imputation from a small reference set was successful, imputation to whole-genome sequence data hardly improved genomic prediction accuracy compared with the predictions based on 60K genotypes. Using only those markers from the whole-genome sequence that are more likely to affect the phenotype was expected to remove noise from the data, but resulted in slightly lower prediction accuracy compared with the complete genome sequence. Finally, I evaluated the inclusion of dominance effects besides additive effects in GS models. The proportion of variance due to additive and dominance effects were estimated for egg production and egg quality traits of a purebred line of layers. The proportion of dominance variance to the total phenotypic variance ranged from 0 to 0.05 across traits. Also, the impact of fitting dominance besides additive effects on prediction accuracy was investigated, but was not found to improve accuracy of genomic prediction of breeding values.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior A seleção genômica (SG) e associação genômica ampla (GWAS) são métodos que exploram o desequilíbrio de ligação (LD) entre marcadores e loci de características quantitativas (QTL). Um dos fatores limitantes para a implementação da SG é a necessidade de um grande número de animais genotipados e fenotipados para obtenção de valores genéticos com alta acurácia. Essa limitação pode ser superada combinando dados de múltiplas populações ou utilizando dados de animais cruzados. O objetivo geral desta tese foi caracterizar os padrões de LD de diferentes populações de suínos. Além disso, avaliar em que medida as diferenças de LD se refletem na acurácia da seleção genômica quando utilizadas diferentes metodologias e arranjos para população de referência e validação. Os arranjos testados foram: utilização de subconjuntos da mesma população como referência e validação (within), populações diferentes nos conjuntos de referência e validação (across) e combinação de duas populações na referência (multi). Nessa tese foram utilizados dados de suínos de linhas puras e de animais cruzados, genotipados com o PorcineSNP60 BeadChip. A regressão Loess proporcionou melhor ajuste aos dados de LD, bem como em predições mais acuradas em comparação a regressão não linear. Mostrou-se também, que a regressão Loess pode ser utilizada para realizar uma comparação estatística do LD decay de diferentes populações. A persistência de fase do LD entre animais cruzados e as linhas puras parentais foi alta, o que nos leva a hipotetizar que associações marcador-QTL similares poderiam ser encontradas em animais cruzados e as linhas parentais e, portanto, esperava-se encontrar altas acurácias de predição genômica entre essas populações. Entre as linhas puras a persistência de fase foi baixa, logo painéis de SNPs de maior densidade deveriam ser utilizados para manter a mesma associação marcador-QTL entre essas linhas. Acurácias obtidas na predição genômica utilizando animais cruzados assim como os arranjos across e multi, não seguiram as expectativas baseadas em LD. Portanto, a consistência de fase de ligação entre populações pode não ser tão importante para a acurácia da seleção genômica como se pensava, mas sim a ação combinada de LD, arquitetura genética e frequências alélicas. Portanto, foi desenvolvida uma metodologia que leva em consideração differenças nas frequências alélicas, bem como informações dos GWAS para comtemplar a arquitetura genética da característica. Esta estratégia trouxe alguns benefícios para a predição genônima para os arranjos within e multi. Ponderações obtidas por meio de GWAS em diferentes conjuntos de dados (uma única população e combinando múltiplas populações) nem sempre resultou em aumento da acurácia, sendo dependente da linha que estava sob seleção. O uso de pesos advindos do GWAS ao se utilizar uma população combinada resultou nas melhores acurácias tanto para os arranjos within quanto multi. A avaliação e o entendimento de como diferenças de LD, frequências alélicas e arquitetura genética afetam a acurácia da predição genômica é fundamental para otimizar a inserção da seleção genômica no melhoramento de suínos. Genomic selection and genomic wide association studies (GWAS) are widely used methods that aim to exploit the linkage disequilibrium (LD) between markers and quantitative trait loci (QTL). Securing a sufficiently large set of genotypes and phenotypes can be a limiting factor when implementing genomic selection that may be overcome by combining data from multiple populations or using crossbred information. The overall objective of this thesis was to characterize LD patterns in different pig populations and to evaluate whether the differences in LD determine the accuracy of genomic predictions when using different reference sets (within-, across- and multi- population) and methodologies. In this thesis I used data from pure lines and crossbred pig populations genotyped with PorcineSNP60 BeadChip. Loess regression provided a better fit to the real LD data, and more accurate LD predictions could be made, compared to nonlinear regression. It was also shown that Loess regression can be used to statistically compare the LD decay of different populations. The persistence of LD phase between crosses and the parental pig lines was found to be high, from which it was hypothesized that similar marker-QTL associations would be found in a cross and in their purebred parent populations and therefore accuracies of genomic prediction across these populations should be high. Between the pure lines the persistence of phase was low, thus higher density panels should be used to have the same marker-QTL associations across these lines. Accuracies obtained from across- and multi-population genomic prediction and from using crossbred data did however not follow the expectations based on LD. Having the same LD phase may therefore not be as important for genomic prediction accuracy as previously thought but rather the interplay between LD, genetic architecture and allele frequencies also plays a major role. Differences in allele frequencies between lines and information from GWAS on the genetic architecture of traits for the different lines were taken into account in analyses developed in the later chapters. The use of weights, based on GWAS results, was expected to lead the GBLUP model towards the real genetic architecture of the traits. This strategy was shown to have some benefit for the genomic predictions with single- and multi-population data sets. Weights obtained from GWAS in different data sets (within and combining populations) did not always lead to increased accuracies of prediction, depending on which lines the weights are applied to. Using weights from GWAS in a combined population was the best approach, resulting in higher accuracy of GBLUP predictions within single- as well as in multi-population analysis. Understanding and evaluating how the accuracy of within-, across- and multi-population genomic prediction is affected by differences in LD, in genetic architecture and in allele frequencies is key to optimize the accuracy of genomic prediction in pig breeding.

There remains is a lot to be learned about the interpretation of genetic parameters and the biology of disease resistance and SCS. This PhD thesis aimed to obtain additional insight in disease resistance and SCS by: 1) quantifying the impact of genetics on innate immunity, represented by natural antibodies (NAb), through estimation of heritabilities and genetic correlations; 2) identifying the genomic regions involved in SCS and NAb levels; 3) quantifying the impact of genetics on environmental sensitivity for SCS. Natural antibody levels are heritable with heritabilities ranging from 0.06 to 0.55 and in general, heritabilities for NAb isotypes were higher than heritabilities for total NAb levels, the latter making no distinction between isotypes. Genetic correlations suggest that isotypes IgA and IgM have a common genetic basis, but that the genetic basis for IgG1 differs from that for IgA or IgM. An additional genome-wide association study for NAb levels showed that information can be gained when total NAb levels are further subdivided into isotype levels. A region on chromosome 23 was significantly associated with genetic variation in isotype IgM levels. The bovine major histocompatibility complex (MHC) is located near this region, making this a region of candidate gene(s) involved in NAb expression in dairy cows both from a functional and positional perspective. Results from the study on genetic parameters and the genome-wide association study suggest that NAb isotypes may provide a better characterization of different elements of the immune response or immune competence and enable more effective decisions when breeding programs start to include innate immune parameters. A genome-wide association study was not only performed for NAb levels, but also for SCS. Relatively few associations, however, were found, which suggests that SCS is controlled by multiple loci, each with a relatively small effect, distributed across the genome. Somatic cell score is partly under genetic control, but is also affected by the environment. Sensitivity to respond to environmental factors, however, can have a genetic origin. Environmental factors can be divided into known and unknown factors, referred to as macro- and micro environment, respectively. Macro-environmental sensitivity can be expressed as genetic variation in the slope of a reaction norm, whereas micro-environmental sensitivity can be expressed as differences in residual variance that have a genetic origin. Both macro- and micro-environmental sensitivity were found for SCS and these sensitivities were positively correlated. Knowledge on both forms of sensitivity can aid in optimization of selection as correlations between the additive genetic variance in intercept, slope and environmental variance were all away from unity. Selection for reduced environmental sensitivity has the potential to reduce variability in animal performance due to environmental factors and herewith increase predictability of performance across and within environments. Knowledge on disease biology is important to fully understand the processes involved when selecting for increased disease resistance, as a better understanding enables a better prediction of the consequences. In this context, the general discussion involved the phenotype definition and statistical modeling, influence of maternal effects and genetic variation in the MHC region. The discussion contained three conclusions: 1) analyses of cell types (detailed phenotypes) rather than SCS can provide further insight in the genetic control of SCS and mastitis; 2) no evidence was found for maternal genetic effects on NAb levels in milk. Maternal environmental effects, however, could play a role in NAb levels; 3) genetic diversity in the MHC region is maintained by natural selection. Selective breeding and farm management practices may affect this genetic diversity, which could bring about negative effects on animal fitness, such as fertility problems. Selective breeding for specific MHC haplotypes may therefore impose a risk for negative effects on animal health.

The turkey (Meleagris gallopavo) is an important agricultural species that is largely used as a meat type bird as egg production of this species is very low. Turkey is the second largest contributor to the world’s poultry meat production after chicken. Understanding the etiology and biology underlying production and health traits is very important for the genetic improvement of these traits in the desired direction and to avoid undesired side-effects. The aim of the research described in this thesis was to interrogate the genetics of turkey traits related to meat production and to investigate the genetic diversity of commercial and heritage turkey populations. Different analyses were performed that included the estimation of genetic and (common) environmental variances for growth (body weight as well as growth curve traits), breast meat yield and meat quality traits in turkeys. I describe the construction of a single nucleotide polymorphism (SNP) based linkage map of turkey and its comparison with the physical map of chicken to investigate genome structural differences between these highly important poultry production species. Two inter-, and 57 intra-chromosomal rearrangements between these two species were confirmed or discovered which is a low number in comparison to mammals and lead to the conclusion that turkey and chicken have highly conserved genomic structure. I used the linkage map of turkey together with individual phenotypes to map quantitative trait loci (QTL) in the same population for the traits described above. Results showed quantitative trait loci on 21 of the 27 turkey chromosomes covered by the linkage map. Forty-five quantitative trait loci were detected across all traits and these were found in 29 different regions on the 21 chromosomes. The next step, after the analyses on the reference population was to investigate the genomic variation in turkeys Next generation sequencing was used to investigate genome variation and the discovery of genome-wide signatures of selection in the turkey respectively. Sequencing was performed on 32 individuals from eleven different turkey populations (seven commercial, three heritage and a South Mexican wild population). Analysis of next generation sequencing data resulted in the detection of 5.49 million putative SNPs compared to the reference genome. The average frequency of heterozygous nucleotide positions in individual turkeys was 1.07 Kb-1 which is substantially lower than in chicken and pigs. The SNPs were subsequently used for the analysis of genetic diversity between the different populations. Genetic diversity analysis using pairwise Nei’s genetic distance among all the individuals from the 11 turkey populations showed that all of the commercial lines branched from a single node relative to the heritage varieties and the ancestral turkey population, indicating that commercial lines appear to share a common origin. After assessing genome wide variation and diversity between breeds, the SNP data from ten of the turkey populations (29 individuals) was used to detect selective sweep regions. Across the turkey populations, 54 genomic regions with significant evidence for a selective sweep were detected. These sweeps were distributed over 14 different chromosomes. This study has investigated the genetics i.e. analysis of variances and QTL mapping related to economically important traits in turkey production and the genomic variation of turkey. Furthermore, this study has also created resources e.g. millions of discovered SNPs for subsequent genomic work in the turkey such as to discover variant (s) for both minor and major effects on traits of economic importance, and a high-resolution linkage map can be developed.