Uncovering Evolutionary Adaptations in Common Warthogs through Genomic Analyses.

In the Suidae family, warthogs show significant survival adaptability and trait specificity. This study offers a comparative genomic analysis between the warthog and other Suidae species, including the Luchuan pig, Duroc pig, and Red River hog. By integrating the four genomes with sequences from the other four species, we identified 8868 single-copy orthologous genes. Based on 8868 orthologous protein sequences, phylogenetic assessments highlighted divergence timelines and unique evolutionary branches within suid species. Warthogs exist on different evolutionary branches compared to DRCs and LCs, with a divergence time preceding that of DRC and LC. Contraction and expansion analyses of warthog gene families have been conducted to elucidate the mechanisms of their evolutionary adaptations. Using GO, KEGG, and MGI databases, warthogs showed a preference for expansion in sensory genes and contraction in metabolic genes, underscoring phenotypic diversity and adaptive evolution direction. Associating genes with the QTLdb-pigSS11 database revealed links between gene families and immunity traits. The overlap of olfactory genes in immune-related QTL regions highlighted their importance in evolutionary adaptations. This work highlights the unique evolutionary strategies and adaptive mechanisms of warthogs, guiding future research into the distinct adaptability and disease resistance in pigs, particularly focusing on traits such as resistance to African Swine Fever Virus.

High Gamma-Aminobutyric Acid (GABA) Oolong Tea Alleviates High-Fat Diet-Induced Metabolic Disorders in Mice.

Obesity and overweight are associated with an increasing risk of developing health conditions and chronic non-communicable diseases, including cardiovascular diseases, cancer, musculoskeletal problems, respiratory problems, and mental health, and its prevalence is rising. Diet is one of three primary lifestyle interventions. Many bioactive components in tea especially oolong tea, including flavonoids, gamma-aminobutyric acid (GABA), and caffeine were reported to show related effects in reducing the risk of obesity. However, the effects of GABA oolong tea extracts (OTEs) on high-fat diet (HFD)-induced obesity are still unclear. Therefore, this study aims to explore whether the intervention of GABA OTEs can prevent HFD-induced obesity and decipher its underlying mechanisms using male C57BL/6 J mice. The result indicated that GABA OTEs reduced leptin expression in epididymal adipose tissue and showed a protective effect on nonalcoholic fatty liver disease. It promoted thermogenesis-related protein of uncoupling protein-1 and peroxisome proliferator-activated receptor-gamma coactivator (PGC-1α), boosted lipid metabolism, and promoted fatty acid oxidation. It also reduced lipogenesis-related protein levels of sterol regulatory element binding protein, acetyl-CoA carboxylase, and fatty acid synthase and inhibited hepatic triglyceride (TG) levels. These data suggest that regular drinking of GABA oolong tea has the potential to reduce the risk of being overweight, preventing obesity development through thermogenesis, lipogenesis, and lipolysis.

Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice.

The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice.

Natural variation of OsML1, a mitochondrial transcription termination factor, contributes to mesocotyl length variation in rice.

Mesocotyl length (ML) is a crucial factor in determining the establishment and yield of rice planted through dry direct seeding, a practice that is increasingly popular in rice production worldwide. ML is determined by the endogenous and external environments, and inherits as a complex trait. To date, only a few genes have been cloned, and the mechanisms underlying mesocotyl elongation remain largely unknown. Here, through a genome-wide association study using sequenced germplasm, we reveal that natural allelic variations in a mitochondrial transcription termination factor, OsML1, predominantly determined the natural variation of ML in rice. Natural variants in the coding regions of OsML1 resulted in five major haplotypes with a clear differentiation between subspecies and subpopulations in cultivated rice. The much-reduced genetic diversity of cultivated rice compared to the common wild rice suggested that OsML1 underwent selection during domestication. Transgenic experiments and molecular analysis demonstrated that OsML1 contributes to ML by influencing cell elongation primarily determined by H2 O2 homeostasis. Overexpression of OsML1 promoted mesocotyl elongation and thus improved the emergence rate under deep direct seeding. Taken together, our results suggested that OsML1 is a key positive regulator of ML, and is useful in developing varieties for deep direct seeding by conventional and transgenic approaches.

Genomic prediction of rice mesocotyl length indicative of directing seeding suitability using a half-sib hybrid population.

Direct seeding has been widely adopted as an economical and labor-saving technique in rice production, though problems such as low seedling emergence rate, emergence irregularity and poor lodging resistance are existing. These problems are currently partially overcome by increasing seeding rate, however it is not acceptable for hybrid rice due to the high seed cost. Improving direct seeding by breeding is seen as the ultimate solution to these problems. For hybrid breeding, identifying superior hybrids among a massive number of hybrids from crossings between male and female parental populations by phenotypic evaluation is tedious and costly. Contrastingly, genomic selection/prediction (GS/GP) could efficiently detect the superior hybrids capitalizing on genomic data, which holds a great potential in plant hybrids breeding. In this study, we utilized 402 rice inbred varieties and 401 hybrids to investigate the effectiveness of GS on rice mesocotyl length, a representative indicative trait of direct seeding suitability. Several GP methods and training set designs were studied to seek the optimal scenario of hybrid prediction. It was shown that using half-sib hybrids as training set with the phenotypes of all parental lines being fitted as a covariate could optimally predict mesocotyl length. Partitioning the molecular markers into trait-associated and -unassociated groups based on genome-wide association study using all parental lines and hybrids could further improve the prediction accuracy. This study indicates that GS could be an effective and efficient method for hybrid breeding for rice direct seeding.

Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.

Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping.

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders' main interest - response to selection - was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates.

KEY MESSAGE: A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.

Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain.

Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.

Genetic Contribution of Emmer Wheat (Triticum dicoccon Schrank) to Heat Tolerance of Bread Wheat.

Rising global temperatures cause substantial yield losses in many wheat growing environments. Emmer wheat (Triticum dicoccon Schrank), one of the first wheat species domesticated, carries significant variation for tolerance to abiotic stresses. This study identified new genetic variability for high-temperature tolerance in hexaploid progeny derived from crosses with emmer wheat. Eight hexaploid and 11 tetraploid parents were recombined in 43 backcross combinations using the hexaploid as the recurrent parent. A total of 537 emmer-based hexaploid lines were developed by producing approximately 10 doubled haploids on hexaploid like BC1F1 progeny and subsequent selection for hexaploid morphology. These materials and 17 commercial cultivars and hexaploid recurrent parents were evaluated under two times of sowing in the field, in 2014-2016. The materials were genotyped using a 90K SNP platform and these data were used to estimate the contribution of emmer wheat to the progeny. Significant phenotypic and genetic variation for key agronomical traits including grain yield, TKW and screenings was observed. Many of the emmer derived lines showed improved performance under heat stress (delayed sowing) compared with parents and commercial cultivars. Emmer derived lines were the highest yielding material in both sowing dates. The emmer wheat parent contributed between 1 and 44% of the genome of the derived lines. Emmer derived lines with superior kernel weight and yield generally had a greater genetic contribution from the emmer parent compared to those with lower trait values. The study showed that new genetic variation for key traits such as yield, kernel weight and screenings can be introduced to hexaploid wheat from emmer wheat. These genetic resources should be explored more systematically to stabilize grain yield and quality in a changing climate.

Extracting genotype information of Arabidopsis thaliana recombinant inbred lines from transcript profiles established with high-density oligonucleotide arrays.

KEY MESSAGE: Polymorphic probes identified via a sequence-based approach are suitable to infer the genotypes of recombinant inbred lines from hybridisation intensities of GeneChip ® transcript profiling experiments. The sequences of the probes of the ATH1 GeneChip® exactly match transcript sequences of the Arabidopsis thaliana reference genome Col-0, whereas nucleotide differences and/or insertions/deletions may be observed for transcripts of other A. thaliana accessions. Individual probes of the GeneChip® that show sequence polymorphisms between different A. thaliana accessions may serve as single-feature polymorphism (SFP) markers, provided that the sequence changes cause differences in hybridisation intensity for the accessions of interest. A sequence-based approach identified features on the high-density oligonucleotide array that showed sequence polymorphisms between A. thaliana accessions Col-0 and C24. Hybridisation intensities of polymorphic probes were extracted from genome-wide transcript profiles of Col-0/C24 and C24/Col-0 recombinant inbred lines and assessed after standardisation via sliding window analyses to identify SFP markers. The genotypes of the recombinant inbred lines were determined with the SFP markers and the resulting data were integrated with information, which had been established previously with single nucleotide polymorphism and insertion/deletion markers, to enrich the linkage map of the Col-0/C24 and C24/Col-0 recombinant inbred populations. Congruence between the molecular marker map and the sequence maps of the A. thaliana Col-0 chromosomes proved the reliability of the genotype information which was deduced from the transcript profiles of the Col-0/C24 and C24/Col-0 recombinant inbred lines.

Use of genotyping-by-sequencing to determine the genetic structure in the medicinal plant chamomile, and to identify flowering time and alpha-bisabolol associated SNP-loci by genome-wide association mapping.

BACKGROUND: Chamomile (Matricaria recutita L.) has a long history of use in herbal medicine with various applications, and the flower heads contain numerous secondary metabolites which are medicinally active. In the major crop plants, next generation sequencing (NGS) approaches are intensely applied to exploit genetic resources, to develop genomic resources and to enhance breeding. Here, genotyping-by-sequencing (GBS) has been used in the non-model medicinal plant chamomile to evaluate the genetic structure of the cultivated varieties/populations, and to perform genome wide association study (GWAS) focusing on genes with large effect on flowering time and the medicinally important alpha-bisabolol content. RESULTS: GBS analysis allowed the identification of 6495 high-quality SNP-markers in our panel of 91 M. recutita plants from 33 origins (2-4 genotypes each) and 4 M. discoidea plants as outgroup, grown in the greenhouse in Gatersleben, Germany. M. recutita proved to be clearly distinct from the outgroup, as was demonstrated by different cluster and principal coordinate analyses using the SNP-markers. Chamomile genotypes from the same origin were mostly genetically similar. Model-based cluster analysis revealed one large group of tetraploid genotypes with low genetic differentiation including 39 plants from 14 origins. Tetraploids tended to display lower genetic diversity than diploids, probably reflecting their origin by artificial polyploidisation from only a limited set of genetic backgrounds. Analyses of flowering time demonstrated that diploids generally flowered earlier than tetraploids, and the analysis of alpha-bisabolol identified several tetraploid genotypes with a high content. GWAS identified highly significant (P < 0.01) SNPs for flowering time (9) and alpha-bisabolol (71). One sequence harbouring SNPs associated with flowering time was described to play a role in self-pollination in Arabidopsis thaliana, whereas four sequences harbouring SNPs associated with alpha-bisabolol were identified to be involved in plant biotic and abiotic stress response in various plants species. CONCLUSIONS: The first genomic resource for future applications to enhance breeding in chamomile was created, andanalyses of diversity will facilitate the exploitation of these genetic resources. The GWAS data pave the way for future research towards the genetics underlying important traits in chamomile, the identification of marker-trait associations, and development of reliable markers for practical breeding.

Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.

KEY MESSAGE: Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and presence of local epistatic effects. The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a diverse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they individually explained only 2.5% of the genetic variation. Despite this, we found no stable marker-trait associations when validating in independent subsets. A two-dimensional scan for marker-marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.

Dissecting the Genetic Basis of Resistance to Soybean Cyst Nematode Combining Linkage and Association Mapping.

A set of 585 informative single-nucleotide polymorphism (SNP) loci was used to genotype both a panel of diverse accessions and a set of recombinant inbred lines (RILs) bred from the cross Zhongpin03-5373 (ZP; resistant to SCN) × Zhonghuang13 (ZH; susceptible). The SNP loci are mostly sited within genic sequence in regions of the soybean [ (L.) Merr.] genome thought to harbor genes determining resistance to the soybean cyst nematode (SCN, Ichinohe). The three strongest quantitative trait nucleotides (QTNs) identified by association mapping (AM) involved the genes (a component of the multigene locus ), and (an paralog), as well as some other loci with smaller effects. The linkage mapping (LM) analysis performed using the RILs revealed two putative quantitative trait loci (QTL): one mapping to and the other to an paralog; both of these loci were also identified by AM. The former locus explained 25.5% of the phenotypic variance for SCN resistance and the latter 5.8%. In combination, the two major loci acted nonadditively, providing a high level of SCN resistance.

Genomic Prediction of Barley Hybrid Performance.

Hybrid breeding in barley ( L.) offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two-way and 408 three-way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP) array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five-fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA) effects was moderate, amounting to 0.56 and 0.48 for two- and three-way hybrids, respectively. The potential of GCA-based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross-validated prediction abilities of 0.51 and 0.58 for two- and three-way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two-way hybrids as training population and the three-way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley.

Genomic selection in a commercial winter wheat population.

KEY MESSAGE: Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability. We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.

Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast Central European elite winter wheat population.

KEY MESSAGE: Fusarium head blight and Septoria tritici blotch resistances are complex traits and can be improved efficiently by genomic selection modeling main and epistatic effects. Enhancing the resistance against Fusarium head blight (FHB) and Septoria tritici blotch (STB) is of central importance for a sustainable wheat production. Our study is based on a large experimental data set of 2325 inbred lines genotyped with 12,642 SNP markers and phenotyped in multi-environmental trials for FHB and STB resistance as well as for plant height. Our objectives were to (1) investigate the impact of plant height on FHB and STB severity, (2) examine the potential of marker-assisted selection, and (3) study the prediction ability of genomic selection modeling main and epistatic effects. We observed low correlations between plant height and FHB (r = -0.15; P < 0.05) as well as STB severity (r = -0.17; P < 0.05) suggesting negligible morphological resistances. Cross-validation in combination with association mapping revealed absence of large effect QTL impeding an efficient pyramiding of different resistance loci through marker-assisted selection. The prediction ability of genomic selection was high amounting to 0.6 for FHB and 0.5 for STB resistance. Therefore, genomic selection is a promising tool to improve FHB and STB resistance in wheat.

Prospects and limits of marker imputation in quantitative genetic studies in European elite wheat (Triticum aestivum L.).

BACKGROUND: The main goal of our study was to investigate the implementation, prospects, and limits of marker imputation for quantitative genetic studies contrasting map-independent and map-dependent algorithms. We used a diversity panel consisting of 372 European elite wheat (Triticum aestivum L.) varieties, which had been genotyped with SNP arrays, and performed intensive simulation studies. RESULTS: Our results clearly showed that imputation accuracy was substantially higher for map-dependent compared to map-independent methods. The accuracy of marker imputation depended strongly on the linkage disequilibrium between the markers in the reference panel and the markers to be imputed. For the decay of linkage disequilibrium present in European wheat, we concluded that around 45,000 markers are needed for low cost, low-density marker profiling. This will facilitate high imputation accuracy, also for rare alleles. Genomic selection and diversity studies profited only marginally from imputing missing values. In contrast, the power of association mapping increased substantially when missing values were imputed. CONCLUSIONS: Imputing missing values is especially of interest for an economic implementation of association mapping in breeding populations.

Targeted resequencing of GWAS loci reveals novel genetic variants for milk production traits.

BACKGROUND: Genome wide association study (GWAS) has been proven to be a powerful tool for detecting genomic variants associated with complex traits. However, the specific genes and causal variants underlying these traits remain unclear. RESULTS: Here, we used target-enrichment strategy coupled with next generation sequencing technique to study target regions which were found to be associated with milk production traits in dairy cattle in our previous GWAS. Among the large amount of novel variants detected by targeted resequencing, we selected 200 SNPs for further association study in a population consisting of 2634 cows. Sixty six SNPs distributed in 53 genes were identified to be associated significantly with on milk production traits. Of the 53 genes, 26 were consistent with our previous GWAS results. We further chose 20 significant genes to analyze their mRNA expression in different tissues of lactating cows, of which 15 were specificly highly expressed in mammary gland. CONCLUSIONS: Our study illustrates the potential for identifying causal mutations for milk production traits using target-enrichment resequencing and extends the results of GWAS by discovering new and potentially functional mutations.