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Although genome-wide association studies (GWAS) have identified several hundred loci associated with autoimmune diseases, their mechanistic insights are still poorly understood. The human genome is more complex than single nucleotide polymorphisms (SNPs) that are interrogated by GWAS arrays. Apart from SNPs, it also comprises genetic variations such as insertions-deletions, copy number variations, and somatic mosaicism. Although previous studies suggest that common copy number variations do not play a major role in autoimmune disease risk, it is possible that certain rare genetic variations with large effect sizes are relevant to autoimmunity. In addition, other layers of regulations such as gene-gene interactions, epigenetic-determinants, gene and environmental interactions also contribute to the heritability of autoimmune diseases. This review focuses on discussing why studying these elements may allow us to gain a more comprehensive understanding of the aetiology of complex autoimmune traits.

The main objective of plant breeders is to create and identify genotypes that are well-adapted to the target population of environments (TPE). The TPE corresponds to the future growing conditions in which the varieties produced by a breeding program will be grown. All possible genotypes that could be considered as selection candidates for a specific TPE are said to belong to the target population of genotypes, TPG. Genotypes commonly show different sensitivities to environmental gradients and then genotype by environment interaction (GxE) is observed. GxE can lead to changes in genotypic ranking, complicating the breeding process. The main aim of this thesis was to investigate statistical models and the combination of statistical and crop growth models to improve phenotype prediction across multiple environments. One aspect that determines the quality of phenotype prediction is the set of genotypes used to train the prediction model, especially when the TPG is structured. We proposed a method that uniformly covers the genetic space of the TPG, leading to a larger prediction accuracy than random sampling. We produced positive results for wheat, maize and rice. A second aspect that influences the accuracy of phenotype predictions is the choice of environments used to train the prediction model, which should capture the heterogeneity in the TPE. When accounting for heterogeneity in environmental quality, it is important to distinguish between repeatable and well predictable elements in the environmental conditions from those that are badly predictable. We proposed statistical methods based on the AMMI model and on mixed models to identify groups of environments that show repeatable GxE, illustrating our ideas with multi-environment wheat data in North-Western Europe. The importance of training set construction strategies and multi-environment genomic prediction models was also demonstrated for barley data. If breeders are interested in identifying the genetic basis of the target traits, it is advantageous to have a higher SNP density. In this thesis, we used exome sequence data of the EU-Whealbi-barley germplasm, which corresponds to a unique set of genotypes with a diverse origin, growth habit and breeding history. For this diverse data, we assessed the effects of QTLs and haplotypes across multiple environments for awn length, grain weight, heading date and plant height. Our results show that the EU-Whealbi-barley collection possesses a large diversity of promising alleles regulating the four traits we analysed. The last major topic addressed in this thesis is the use of a combination of statistical-genetic models and crop growth models (APSIM) as a strategy to assess the traits and phenotyping schemes to improve the prediction accuracy of a target trait like yield. We assess the potential of the combined modelling approach to characterize a sample of the TPG and TPE, and illustrate how trait correlations are modified by environmental conditions and by the genetic architecture of the sample of the TPE. We discuss the topics mentioned above, from a didactical perspective, proposing a list of subjects that should be covered in a GxE course for plant breeders. Finally, we discuss challenges and opportunities presented by the characterization of the TPE and TPG when using simulations based on statistical and crop growth models.

The role of MH class II B (Cyca-DAB1-like) genes in resistance of common carp (Cyprinus carpio L.) to Cyprinid herpesvirus-3 (CyHV-3), also known as koi herpesvirus (KHV) was analysed. The material consisted of 934 fish from six carp crosses. Fish were challenged with CyHV-3 at an age of 7 and 10 months. During challenge experiments the peak of mortality caused by CyHV-3 was observed at days 8-12 p.i. and the overall cumulative mortality reached 79.9%. Among six Cyca-DAB1-like genotypes, revealed by PCR-RF-SSCP analysis, one genotype (E) was found associated with higher resistance to CyHV-3. Three other genotypes (B, H and J) could be linked to higher susceptibility to CyHV-3. Analysis of the alleles that compose the Cyca-DAB1-like genotypes linked one particular allele (Cyca-DAB1*05) to significantly increased, and two alleles (Cyca-DAB1*02 and Cyca-DAB1*06) to significantly decreased resistance to CyHV-3. Our data indicate that MH class II B genes could be used as potential genetic markers in breeding of common carp for resistance to this virus.

Previous efforts to characterize conservation between the human and mouse genomes focused largely on sequence comparisons. These studies are inherently limited because they don’t account for gene structure differences, which may exist despite genomic sequence conservation. Recent high-throughput transcriptome studies have revealed widespread and extensive overlaps between genes, and transcripts, encoded on both strands of the genomic sequence. This overlapping gene organization, which produces sense-antisense gene pairs, is capable of effecting regulatory cascades through established mechanisms. We present an evolutionary conservation assessment of sense-antisense pairs, on three levels: genomic, transcriptomic, and structural. From a genome-wide dataset of human sense-antisense pairs, we first identified orthologous loci in the mouse genome, then assessed their transcription in the mouse, and finally compared the genomic structures of sense-antisense pairs expressed in both species. We found that approximately half of human sense-antisense loci have single orthologous locations in the mouse genome; however, only half of those orthologous locations have sense-antisense transcriptional activity in the mouse. This suggests that high human-mouse gene conservation overlooks widespread distinctions in sense-antisense pair incidence and expression. We compared gene structures at orthologous sense-antisense loci, finding frequent differences in gene structure between human and orthologous mouse sense-antisense pair members. Our categorization of human sense-antisense pairs with respect to mouse conservation of expression as well as structure points to limitations of mouse models. Gene structure differences, including at sense-antisense loci, may account for some of the phenotypic distinctions between primates and rodents. Genes in non-conserved sense-antisense pairs may contribute to evolutionary lineage-specific regulatory outcomes.

Plants are continuously exposed to a wide variety of pathogens. However, all plant species are non-hosts for the majority of the potential plant pathogens. The genetic dissection of non-host resistance is hampered by the lack of segregating population from crosses between host and non-host species, since hardly any non-host is crossable with a host. We have studied the non-host resistance in Lactuca saligna (wild lettuce) to lettuce downy mildew ( Bremia lactucae ). L. saligna is one of the few examples of a non-host species that is crossable with a related host species, L. sativa (lettuce). Based on this interspecific cross, segregating populations have been developed for genetical analysis of the non-host resistance. To map the resistance, we have used two strategies in which we make use of DNA markers to genotype plants. As no accurate linkage map was available for lettuce, we started with the construction of a linkage map of L. saligna´L. sativa . In Chapter 2, the development of an integrated linkage map, based on two populations, is described. To acquire DNA markers, AFLP analyses have been performed on the F 2 populations of the crosses L. saligna CGN 5271 ´L. sativa Olof and L. saligna CGN 11341´L. sativa Norden. Based on these AFLP analyses the polymorphism rate between L. saligna and L. sativa is estimated to be 81%. A linkage map was constructed that comprises 12 SSRs and 476 AFLP markers over 854 cM in nine linkage groups (n=9). Since the markers are randomly spread over all chromosomes, we assume this map is an accurate representative of both parental genomes and very useful for Marker Assisted Selection.The first mapping strategy for downy mildew resistance is described in Chapter 3. In that study, we have performed a QTL analysis on 126 F 2 plants of a cross between the resistant L. saligna CGN 5271 and the susceptible L. sativa Olof. For this QTL analysis all 126 F 2 plants have been tested for resistance in four disease tests with two complex Bremia races (NL14 and NL16). The F 2 population showed a wide and continuous range of resistance levels from completely resistant to completely susceptible. Evidence is presented for a quantitative resistance against both Bremia races as well as for a race-specific resistance against Bremia race NL16 and not against NL14. These disease test data sets have been combined with DNA marker data of all 126 F 2 plants that had already been obtained for the construction of the linkage map. QTL mapping revealed a qualitative gene ( R39 ) explaining the race-specific resistance and three QTLs ( RBQ1 , RBQ2 and RBQ3 ) explaining the quantitative resistance. The qualitative gene R39 is a dominant gene that gives nearly complete resistance to race NL16 in L. saligna CGN 5271 and therefore it shows features similar to Dm genes (dominant race specific genes that give a complete resistance to d owny m ildew). The three QTLs explain 51% of the quantitative resistance against NL14, which indicates that probably not all QTLs have been detected in this F 2 population.In addition to this rather classical F 2 mapping strategy, we have performed an alternative mapping strategy based on the development and characterization of a set of Backcross Inbred Lines (BILs). These BILs are genetically nearly completely like L. sativa but contain a single chromosome substitution segment of L. saligna CGN 5271 (Chapter 4). Starting from an F 1 plant, BILs have been developed by four to five generations of backcrosses and one generation of selfing. All backcrosses from F 1 to BC 4 were made randomly without intentional selection. Marker Assisted Selection was started in the BC 4 generation. Finally, a set of 29 lines was obtained that covers 95% of the L. saligna genome, comprising 16 lines with a single homozygous introgression (BILs), one line with two homozygous introgressions, five lines with heterozygous single introgressions and seven lines with two or more heterozygous introgressions. Several chromosome regions showed severe distorted segregation in the F 2 population. Based on segregation ratios in backcross lines, we were able to explain distorted segregations of three chromosome regions observed in the F 2 population by genetic loci that are involved in pollen- or egg cell fitness.When seed of the first developed BILs was available, a disease test had been set up to test if the BILs, which carried QTLs as identified in the F 2 population, showed enhanced levels of quantitative resistance indeed. Nine BILs (or nearly-BILs) have been tested for resistance to Bremia race NL16. They covered together 31% of the L. saligna parental genome. Two resistance loci detected in the F 2 population ( R39 and RBQ3 ) have been confirmed in the disease test on the BILs. R39 is a dominant gene, which gives a complete resistance against Bremia race NL16. RBQ3 reduces the infection severity of the susceptible L. sativa by 49% ten days post inoculation. The quantitative effects from the resistance genes in these BILs were higher than expected from the F 2 mapping results. No conclusive comparisons of RBQ2 could have been made, as the introgression in the backcross line was not homozygous. RBQ1 has not been tested. Most exciting, the BIL method revealed a new resistance locus on Chromosome 8 with a 77% reduction on the infection severity compared to the susceptible control ten days post inoculation. We conclude that the BIL mapping method can reveal new QTLs unnoticed in the F 2 mapping method and it enables a quantification of the resistance gene effect in a L. sativa background.To extend our knowledge about the non-host resistance of L. saligna to Bremia , we have compared the genetics of non-host resistance to Bremia in L. saligna CGN 5271 with another accession L. saligna CGN 11341. The two accessions show a 39% AFLP polymorphism rate. We have analyzed the non-host resistance of L. saligna CGN 11341 by disease tests and DNA marker analyses on an F 2 and BC 1 population. Disease tests with Bremia races NL14 and NL16 showed a wide range of infection severity scores from resistant to susceptible to both races. The majority of plants had a similar resistance level to both Bremia races. These findings imply that the resistance of L. saligna is quantitatively expressed and is probably race non-specific. A few F 2 and BC 1 plants were completely resistant against Bremia race NL16 and rather susceptible to race NL14. QTL mapping revealed that a major resistance gene that was located on Chromosome 9 explains this race-specific resistance. This gene is designated R39b , as it may be different from R39 .No additional QTLs have been detected in this small F 2 population (n= 54). However, F 2 plants with L. saligna CGN 11341 alleles at loci of RBQ1 , RBQ2 , RBQ3 and RBQ4 mapped in L. saligna CGN 5271, were more resistant than F 2 plants with L. sativa alleles at these loci. In conclusion, we state that it is very likely that the same genes explain the resistances to Bremia in both L. saligna accessions. A backcross program for a set of Backcross Inbred Lines (BIL) that cover R39b and loci for putative QTLs, is in progress.In the last chapter of this thesis the basic results of the study have been discussed. We adduce that non-host resistance in L. saligna is not explained by accumulation of race-specific major resistance genes ( Dm genes) but by a resistance mechanism based on QTLs. Further, we have made a comparison for efficiency of four breeding methods to introgress the resistance genes from L. saligna . Based on this study, we conclude that twice as many resistance genes are introgressed when Marker Assisted Selection is used. Finally, several recommendations concerning research on non-host resistance and the applications of Backcross Inbred Lines have been suggested.

Plants are continuously exposed to a wide variety of pathogens. However, all plant species are non-hosts for the majority of the potential plant pathogens. The genetic dissection of non-host resistance is hampered by the lack of segregating population from crosses between host and non-host species, since hardly any non-host is crossable with a host. We have studied the non-host resistance in Lactuca saligna (wild lettuce) to lettuce downy mildew ( Bremia lactucae ). L. saligna is one of the few examples of a non-host species that is crossable with a related host species, L. sativa (lettuce). Based on this interspecific cross, segregating populations have been developed for genetical analysis of the non-host resistance. To map the resistance, we have used two strategies in which we make use of DNA markers to genotype plants. As no accurate linkage map was available for lettuce, we started with the construction of a linkage map of L. saligna´L. sativa . In Chapter 2, the development of an integrated linkage map, based on two populations, is described. To acquire DNA markers, AFLP analyses have been performed on the F 2 populations of the crosses L. saligna CGN 5271 ´L. sativa Olof and L. saligna CGN 11341´L. sativa Norden. Based on these AFLP analyses the polymorphism rate between L. saligna and L. sativa is estimated to be 81%. A linkage map was constructed that comprises 12 SSRs and 476 AFLP markers over 854 cM in nine linkage groups (n=9). Since the markers are randomly spread over all chromosomes, we assume this map is an accurate representative of both parental genomes and very useful for Marker Assisted Selection.The first mapping strategy for downy mildew resistance is described in Chapter 3. In that study, we have performed a QTL analysis on 126 F 2 plants of a cross between the resistant L. saligna CGN 5271 and the susceptible L. sativa Olof. For this QTL analysis all 126 F 2 plants have been tested for resistance in four disease tests with two complex Bremia races (NL14 and NL16). The F 2 population showed a wide and continuous range of resistance levels from completely resistant to completely susceptible. Evidence is presented for a quantitative resistance against both Bremia races as well as for a race-specific resistance against Bremia race NL16 and not against NL14. These disease test data sets have been combined with DNA marker data of all 126 F 2 plants that had already been obtained for the construction of the linkage map. QTL mapping revealed a qualitative gene ( R39 ) explaining the race-specific resistance and three QTLs ( RBQ1 , RBQ2 and RBQ3 ) explaining the quantitative resistance. The qualitative gene R39 is a dominant gene that gives nearly complete resistance to race NL16 in L. saligna CGN 5271 and therefore it shows features similar to Dm genes (dominant race specific genes that give a complete resistance to d owny m ildew). The three QTLs explain 51% of the quantitative resistance against NL14, which indicates that probably not all QTLs have been detected in this F 2 population.In addition to this rather classical F 2 mapping strategy, we have performed an alternative mapping strategy based on the development and characterization of a set of Backcross Inbred Lines (BILs). These BILs are genetically nearly completely like L. sativa but contain a single chromosome substitution segment of L. saligna CGN 5271 (Chapter 4). Starting from an F 1 plant, BILs have been developed by four to five generations of backcrosses and one generation of selfing. All backcrosses from F 1 to BC 4 were made randomly without intentional selection. Marker Assisted Selection was started in the BC 4 generation. Finally, a set of 29 lines was obtained that covers 95% of the L. saligna genome, comprising 16 lines with a single homozygous introgression (BILs), one line with two homozygous introgressions, five lines with heterozygous single introgressions and seven lines with two or more heterozygous introgressions. Several chromosome regions showed severe distorted segregation in the F 2 population. Based on segregation ratios in backcross lines, we were able to explain distorted segregations of three chromosome regions observed in the F 2 population by genetic loci that are involved in pollen- or egg cell fitness.When seed of the first developed BILs was available, a disease test had been set up to test if the BILs, which carried QTLs as identified in the F 2 population, showed enhanced levels of quantitative resistance indeed. Nine BILs (or nearly-BILs) have been tested for resistance to Bremia race NL16. They covered together 31% of the L. saligna parental genome. Two resistance loci detected in the F 2 population ( R39 and RBQ3 ) have been confirmed in the disease test on the BILs. R39 is a dominant gene, which gives a complete resistance against Bremia race NL16. RBQ3 reduces the infection severity of the susceptible L. sativa by 49% ten days post inoculation. The quantitative effects from the resistance genes in these BILs were higher than expected from the F 2 mapping results. No conclusive comparisons of RBQ2 could have been made, as the introgression in the backcross line was not homozygous. RBQ1 has not been tested. Most exciting, the BIL method revealed a new resistance locus on Chromosome 8 with a 77% reduction on the infection severity compared to the susceptible control ten days post inoculation. We conclude that the BIL mapping method can reveal new QTLs unnoticed in the F 2 mapping method and it enables a quantification of the resistance gene effect in a L. sativa background.To extend our knowledge about the non-host resistance of L. saligna to Bremia , we have compared the genetics of non-host resistance to Bremia in L. saligna CGN 5271 with another accession L. saligna CGN 11341. The two accessions show a 39% AFLP polymorphism rate. We have analyzed the non-host resistance of L. saligna CGN 11341 by disease tests and DNA marker analyses on an F 2 and BC 1 population. Disease tests with Bremia races NL14 and NL16 showed a wide range of infection severity scores from resistant to susceptible to both races. The majority of plants had a similar resistance level to both Bremia races. These findings imply that the resistance of L. saligna is quantitatively expressed and is probably race non-specific. A few F 2 and BC 1 plants were completely resistant against Bremia race NL16 and rather susceptible to race NL14. QTL mapping revealed that a major resistance gene that was located on Chromosome 9 explains this race-specific resistance. This gene is designated R39b , as it may be different from R39 .No additional QTLs have been detected in this small F 2 population (n= 54). However, F 2 plants with L. saligna CGN 11341 alleles at loci of RBQ1 , RBQ2 , RBQ3 and RBQ4 mapped in L. saligna CGN 5271, were more resistant than F 2 plants with L. sativa alleles at these loci. In conclusion, we state that it is very likely that the same genes explain the resistances to Bremia in both L. saligna accessions. A backcross program for a set of Backcross Inbred Lines (BIL) that cover R39b and loci for putative QTLs, is in progress.In the last chapter of this thesis the basic results of the study have been discussed. We adduce that non-host resistance in L. saligna is not explained by accumulation of race-specific major resistance genes ( Dm genes) but by a resistance mechanism based on QTLs. Further, we have made a comparison for efficiency of four breeding methods to introgress the resistance genes from L. saligna . Based on this study, we conclude that twice as many resistance genes are introgressed when Marker Assisted Selection is used. Finally, several recommendations concerning research on non-host resistance and the applications of Backcross Inbred Lines have been suggested.