Marker Density Research Articles

Evaluation of genomic selection methods for predicting fiber quality traits in Upland cotton.

The use of genomic selection (GS) has stimulated a new way to utilize molecular markers in breeding for complex traits in the absence of phenotypic data. GS can potentially decrease breeding cycle by selecting the progeny in the early stages. The objective of this study was to experimentally evaluate the potential value of genomic selection in Upland cotton breeding. Six fiber quality traits were obtained in 3years of replicated field trials in Starkville, MS. Genotyping-by-sequencing-based genotyping was performed using 550 recombinant inbred lines of the multi-parent advanced generation inter-cross population, and 6292 molecular markers were used for the GS analysis. Several methods were compared including genomic BLUP (GBLUP), ridge regression BLUP (rrBLUP), BayesB, Bayesian LASSO, and reproducing kernel hilbert spaces (RKHS). The average heritability (h2) ranged from 0.38 to 0.88 for all tested traits across the 3years evaluated. BayesB predicted the highest accuracies among the five GS methods tested. The prediction ability (PA) and prediction accuracy (PACC) varied widely across 3years for all tested traits and the highest PA and PACC were 0.65, and 0.69, respectively, in 2010 for fiber elongation. Marker density and training population size appeared to be very important factors for PA and PACC in GS. Results indicated that BayesB-based GS method could predict genomic estimated breeding value efficiently in Upland cotton fiber quality attributes and has great potential utility in breeding by reducing cost and time.

Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs.

Plant Resistance Gene, SNP and Genome Annotation an Overview

Plants have developed systems of effective and passive protection to safeguard themselves from pathogens. Active processes include kinds of immune responses that are adaptive and intrinsic. Adaptive immunity is focused on reaction type RNAi and works primarily against viruses. Biological immunity is more general and allows the plant to protect itself against a wide range of pathogens through bacterial and model resistance receptors (PPRs) and forms of resistance (R proteins). PPRs identify molecular models associated with microbes or pathogen that are preserved in a specific category of pathogens. With such big marker amounts, it has become feasible to scan the entire genome for interactions of individual markers with particular quantitatively hereditary traits called whole-genome studies (WGS), genome-wide association trials (GWAS), or association genetics at exceptionally elevated marker densities. Several SNP recognition methods have been used in a specified plant to identify significant amounts of SNPs. These include: identification of SNP based on EST feature information; identification of SNP from sequenced genomes; re-sequencing of amplicons; identification of SNP using sequencing techniques of the next generation and identification of SNP based on cluster tests. Pathologic recognizes parts of Genbank completely annotated genome and MetaCyc has been used as a database for the reference pathway. In contrast to sequence similarity information used in other systems, Pathologic uses Genbank annotation information and the EC assignment as evidence of the presence of each pathway in the genome of interest reference database. When the matching task is finished, pathologic will infer a number of reactions expected to take place in the destination genome and will determine which one of those pathways in the target genome are likely to exist.

Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees.

The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive-dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits.

Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

BackgroundSingle Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties (e.g. calling rate, minor allele frequency profile) and drawbacks (e.g. ascertainment bias). This lead us to study their complementarity and the consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits (grain yield, plant height and male flowering time) measured in 22 environments on a panel of 247 F1 hybrids obtained by crossing 247 diverse dent maize inbred lines with a same flint line. The 247 lines were genotyped using three genotyping technologies (Genotyping-By-Sequencing, Illumina Infinium 50 K and Affymetrix Axiom 600 K arrays).ResultsThe effects of ascertainment bias of the 50 K and 600 K arrays were negligible for deciphering global genetic trends of diversity and for estimating relatedness in this panel. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single Quantitative Trait Locus (QTL) or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distributions and densities, allowed us to detect more QTLs (gain in power) and potentially refine the localization of the causal polymorphisms (gain in resolution).ConclusionsConceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (SNP-arrays and re-sequencing), the genotypic data available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.

A High-Density Linkage Map of the Forage Grass Eragrostis curvula and Localization of the Diplospory Locus.

Eragrostis curvula (Schrad.) Nees (weeping lovegrass) is an apomictic species native to Southern Africa that is used as forage grass in semiarid regions of Argentina. Apomixis is a mechanism for clonal propagation through seeds that involves the avoidance of meiosis to generate an unreduced embryo sac (apomeiosis), parthenogenesis, and viable endosperm formation in a fertilization-dependent or -independent manner. Here, we constructed the first saturated linkage map of tetraploid E. curvula using both traditional (AFLP and SSR) and high-throughput molecular markers (GBS-SNP) and identified the locus controlling diplospory. We also identified putative regulatory regions affecting the expressivity of this trait and syntenic relationships with genomes of other grass species. We obtained a tetraploid mapping population from a cross between a full sexual genotype (OTA-S) with a facultative apomictic individual of cv. Don Walter. Phenotypic characterization of F1 hybrids by cytoembryological analysis yielded a 1:1 ratio of apomictic vs. sexual plants (34:27, X2 = 0.37), which agrees with the model of inheritance of a single dominant genetic factor. The final number of markers was 1,114 for OTA-S and 2,019 for Don Walter. These markers were distributed into 40 linkage groups per parental genotype, which is consistent with the number of E. curvula chromosomes (containing 2 to 123 markers per linkage group). The total length of the OTA-S map was 1,335 cM, with an average marker density of 1.22 cM per marker. The Don Walter map was 1,976.2 cM, with an average marker density of 0.98 cM/marker. The locus responsible for diplospory was mapped on Don Walter linkage group 3, with other 65 markers. QTL analyses of the expressivity of diplospory in the F1 hybrids revealed the presence of two main QTLs, located 3.27 and 15 cM from the diplospory locus. Both QTLs explained 28.6% of phenotypic variation. Syntenic analysis allowed us to establish the groups of homologs/homeologs for each linkage map. The genetic linkage map reported in this study, the first such map for E. curvula, is the most saturated map for the genus Eragrostis and one of the most saturated maps for a polyploid forage grass species.

Construction of an almond genetic linkage map using F1 population ‘Gulcan-2’ × ‘Lauranne’ by SSR markers

Almond is one of the most important nut crops in Turkey. Genetic linkage maps are important tools to develop markers linked to important phenotypic characters. We constructed a SSR-based linkage map in almond using a 'Gulcan-2' × 'Laurenne' F1 population. The consensus map included a total of 168 SSR markers in eight linkage groups (LGs). The number of mapped markers changed from 13 (LG6) to 31 (LG2), and the length of the LGs changed from 47.8 (LG6) to 84.6 cM (LG1). Average number of SSR markers per linkage group was 21. The marker density was 3.1 cM‑1. The linkage map will help to develop markers linked to important traits when phenotyping data was completed.

Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program.

Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities (rMP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in rMP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, rMP for the AP method (0.55, 0.30) approached rMP for the WP method (0.60, 0.52). Though comparable, rMP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in rMP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets.

Abstract 2410: Development of a custom genotyping platform and genetic prediction of prostate cancer risks in sub-Saharan Africa

Abstract Prostate cancer is a highly heritable disease that disproportionally affects African and African-American men. With this in mind, the MADCaP (Men of African Descent Carcinoma of the Prostate) Network has developed a custom genotyping platform. This array is optimized for detection of novel genetic associations in sub-Saharan African populations. The MADCaP Array contains approximately 1.6 million markers, many of which overlap the H3Africa Consortium Array and the OncoArray. It includes an imputation backbone that successfully tags 94% of common (MAF > 0.05) genetic variants in African populations (r2 threshold = 0.8). To aid in fine-mapping, the MADCaP Array has a high density of markers in genomic regions surrounding known cancer associations, including 8q24. Markers on the MADCaP Array also include over 27,000 prostate eQTLs. Using the MADCaP Array, we conducted a pilot study of 800 individuals with individual-level phenotype information. These samples include equal numbers of prostate cancer cases and controls, and they were collected from study sites in Ghana, Nigeria, Senegal, and South Africa. Here, we assess the extent to which polygenic risk scores are able to predict prostate cancer risks in African populations. We also identify non-African GWAS signals that replicate well in African populations. Additional analyses in this study include testing whether specific genetic ancestries are over-represented in cases, and quantifying the extent to which runs of homozygosity are found in the genomes of cases and controls. Citation Format: Joseph Lachance, Maxine Harlemon, Paidamoyo Kachambwa, Olabode Ajayi, Michelle Kim, Marcia Adams, Elizabeth Pugh, Lindsay Peterson, Timothy Rebbeck. Development of a custom genotyping platform and genetic prediction of prostate cancer risks in sub-Saharan Africa [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2410.

Quantitative Genetic Mapping and Genome Assembly in the Lesser Wax Moth Achroia grisella

Specific characteristics of the male Achroia grisella acoustic mating signal determine a male’s attractiveness toward females. These features are genetically variable in populations, and mapping experiments have been used to identify loci contributing to song variation, and understand the evolutionary forces acting on this important sexual trait. Here we built on this foundation and carried out QTL (Quantitative Trait Locus) mapping using >1,000 recombinant individuals, genotyping this large cohort at thousands of sequence-based markers covering the entire collection of 30 A. grisella chromosomes. This dense marker set, coupled with our development of an annotated, draft genome of A. grisella, allowed us to link >3,000 genome scaffolds, >10,000 predicted genes, and close to 275Mb of genome sequence to chromosomes. Our QTL mapping confirmed a fraction of the QTL identified in a previous study, and additionally revealed novel loci. Collectively, QTL explained only small fractions of the phenotypic variance, suggesting many more causative factors remain below the detection threshold of our study. A surprising, and ultimately challenging feature of our study was the low level of intrachromosomal recombination present in our mapping population. This led to difficulty ordering markers along linkage groups, necessitating a chromosome-by-chromosome mapping approach, rather than true interval mapping, and precluded confident ordering/orienting of scaffolds along each chromosome. Nonetheless, our study increased the genomic resources available for the A. grisella system. Enabled by ever more powerful technologies, future investigators will be able to leverage our data to provide more detailed genetic dissection of male song variation in A. grisella.

Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production

Genomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Côte d’Ivoire comprising 189 and 143 clones of the cross PB 260 × RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125–200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required.

Family-specific genotype arrays increase the accuracy of pedigree-based imputation at very low marker densities

BackgroundIn this paper, we evaluate the performance of using family-specific low-density genotype arrays to increase the accuracy of pedigree-based imputation. Genotype imputation is a widely used tool that decreases the costs of genotyping a population by genotyping the majority of individuals on a low-density array and using statistical regularities between the low-density and high-density individuals to fill in the missing genotypes. Previous work on population-based imputation has found that it is possible to increase the accuracy of imputation by maximizing the number of informative markers on an array. In the context of pedigree-based imputation, where the informativeness of a marker depends only on the genotypes of an individual’s parents, it may be beneficial to select the markers on each low-density array on a family-by-family basis.ResultsIn this paper, we examined four family-specific low-density marker selection strategies and evaluated their performance in the context of a real pig breeding dataset. We found that family-specific or sire-specific arrays could increase imputation accuracy by 0.11 at one marker per chromosome, by 0.027 at 25 markers per chromosome and by 0.007 at 100 markers per chromosome.ConclusionsThese results suggest that there may be room to use family-specific genotyping for very-low-density arrays particularly if a given sire or sire-dam pairing have a large number of offspring.

High marker density GWAS provides novel insights into the genomic architecture of terpene oil yield in Eucalyptus.

Terpenoid-based essential oils are economically important commodities, yet beyond their biosynthetic pathways, little is known about the genetic architecture of terpene oil yield from plants. Transport, storage, evaporative loss, transcriptional regulation and precursor competition may be important contributors to this complex trait. Here, we associate 2.39million single nucleotide polymorphisms derived from shallow whole-genome sequencing of 468 Eucalyptus polybractea individuals with 12 traits related to the overall terpene yield, eight direct measures of terpene concentration and four biomass-related traits. Our results show that in addition to terpene biosynthesis, development of secretory cavities, where terpenes are both synthesized and stored, and transport of terpenes were important components of terpene yield. For sesquiterpene concentrations, the availability of precursors in the cytosol was important. Candidate terpene synthase genes for the production of 1,8-cineole and α-pinene, and β-pinene (which comprised >80% of the total terpenes) were functionally characterized as a 1,8-cineole synthase and a β/α-pinene synthase. Our results provide novel insights into the genomic architecture of terpene yield and we provide candidate genes for breeding or engineering of crops for biofuels or the production of industrially valuable terpenes.

Genome-Wide Analysis and Prediction of Resistance to Goss's Wilt in Maize.

Goss's bacterial wilt and leaf blight is one of the most important foliar diseases of maize ( L.). To date, neither large-effect resistance genes, nor practical chemical controls exist to manage the disease. Thus, the importance of discovering durable host resistance necessitates additional genetic mapping for this disease. Unfortunately, because of the biology of the pathogen and the highly significant genotype-by-environment interaction effect observed with Goss's wilt, consistent phenotyping across multiple years poses a hurdle for genetic studies and conventional breeding methods. The objective of this study was to perform a genome-wide association study (GWAS) to identify regions of the genome associated with Goss's wilt resistance as well as to use genomic prediction models to evaluate the utility of genomic selection (GS) in predicting Goss's wilt phenotypes in a panel of diverse maize lines. Using genome-wide association mapping, we were unable to identify any variants significantly associated with Goss's wilt. However, using genomic prediction we were able to train a model with an accuracy of 0.69. Taken together, this suggests that resistance to Goss's wilt is highly polygenic. In addition, when evaluating the accuracy of our prediction model under reduced marker density, it was shown that only 10,000 single nucleotide polymorphisms (SNPs), or ∼20% of our total marker set, was necessary to achieve prediction accuracies similar to the full marker set. This is the first report of genomic prediction for a bacterial disease of maize, and these results highlight the potential of GS for disease resistance in maize.

Optimizing Selection of the Reference Population for Genotype Imputation From Array to Sequence Variants.

Imputation of high-density genotypes to whole-genome sequences (WGS) is a cost-effective method to increase the density of available markers within a population. Imputed genotypes have been successfully used for genomic selection and discovery of variants associated with traits of interest for the population. To allow for the use of imputed genotypes for genomic analyses, accuracy of imputation must be high. Accuracy of imputation is influenced by multiple factors, such as size and composition of the reference group, and the allele frequency of variants included. Understanding the use of imputed WGSs prior to the generation of the reference population is important, as accurate imputation might be more focused, for instance, on common or on rare variants. The aim of this study was to present and evaluate new methods to select animals for sequencing relying on a previously genotyped population. The Genetic Diversity Index method optimizes the number of unique haplotypes in the future reference population, while the Highly Segregating Haplotype selection method targets haplotype alleles found throughout the majority of the population of interest. First the WGSs of a dairy cattle population were simulated. The simulated sequences mimicked the linkage disequilibrium level and the variants’ frequency distribution observed in currently available Holstein sequences. Then, reference populations of different sizes, in which animals were selected using both novel methods proposed here as well as two other methods presented in previous studies, were created. Finally, accuracies of imputation obtained with different reference populations were compared against each other. The novel methods were found to have overall accuracies of imputation of more than 0.85. Accuracies of imputation of rare variants reached values above 0.50. In conclusion, if imputed sequences are to be used for discovery of novel associations between variants and traits of interest in the population, animals carrying novel information should be selected and, consequently, the Genetic Diversity Index method proposed here may be used. If sequences are to be used to impute the overall genotyped population, a reference population consisting of common haplotypes carriers selected using the proposed Highly Segregating Haplotype method is recommended.

Identification of quantitative trait loci associated with flowering time in perilla using genotyping-by-sequencing.

Understanding the transition to the reproductive period is important for crop breeding. This information can facilitate the production of novel varieties that are better adapted to local environments or changing climatic conditions. Here, we report the development of a high-density linkage map based on genotyping-by-sequencing (GBS) for the genus perilla. Through GBS library construction and Illumina sequencing of an F2 population, a total of 9607 single-nucleotide polymorphism (SNP) markers were developed. The ten-group linkage map of 1309.39cM contained 2518 markers, with an average marker density of 0.56cM per linkage group (LG). Using this map, a total of six QTLs were identified. Thesequantitative trait loci (QTLs) are associated with three traits related to flowering time: days to visible flower bud, days to flowering, and days to maturity. Ortholog analysis conducted with known genes involved in the regulation of flowering time among different crop species identified GI, CO and ELF4 as putative perilla orthologs that are closely linked to the QTL regions associated with flowering time. These results provide a foundation that will be useful for future studies of flowering time in perilla using fine mapping, and marker-assisted selection for the development of new varieties of perilla.

Validation of Genotyping by Sequencing Using Transcriptomics for Diversity and Application of Genomic Selection in Tetraploid Potato.

Potato is an important food crop due to its increasing consumption, and as a result, there is demand for varieties with improved production. However, the current status of breeding for improved varieties is a long process which relies heavily on phenotypic evaluation and dated molecular techniques and has little emphasis on modern genotyping approaches. Evaluation and selection before a cultivar is commercialized typically takes 10–15 years. Molecular markers have been developed for disease and pest resistance, resulting in initial marker-assisted selection in breeding. This study has evaluated and implemented a high-throughput transcriptome sequencing method for dense marker discovery in potato for the application of genomic selection. An Australian relevant collection of commercial cultivars was selected, and identification and distribution of high quality SNPs were examined using standard bioinformatic pipelines and a custom approach for the prediction of allelic dosage. As a result, a large number of SNP markers were identified and filtered to generate a high-quality subset that was then combined with historic phenotypic data to assess the approach for genomic selection. Genomic selection potential was predicted for highly heritable traits and the approach demonstrated advantages over the previously used technologies in terms of markers identified as well as costs incurred. The high-quality SNP list also provided acceptable genome coverage which demonstrates its applicability for much larger future studies. This SNP list was also annotated to provide an indication of function and will serve as a resource for the community in future studies. Genome wide marker tools will provide significant benefits for potato breeding efforts and the application of genomic selection will greatly enhance genetic progress.

A local score approach improves GWAS resolution and detects minor QTL: application to Medicago truncatula quantitative disease resistance to multiple Aphanomyces euteiches isolates.

Quantitative trait loci (QTL) with small effects, which are pervasive in quantitative phenotypic variation, are difficult to detect in genome-wide association studies (GWAS). To improve their detection, we propose to use a local score approach that accounts for the surrounding signal due to linkage disequilibrium, by accumulating association signals from contiguous single markers. Simulations revealed that, in a GWAS context with high marker density, the local score approach outperforms single SNP p-value-based tests for detecting minor QTL (heritability of 5-10%) and is competitive with regard to alternative methods, which also aggregate p-values. Using more than five million SNPs, this approach was applied to identify loci involved in Quantitative Disease Resistance (QDR) to different isolates of the plant root rot pathogen Aphanomyces euteiches, from a GWAS performed on a collection of 174 accessions of the model legume Medicago truncatula. We refined the position of a previously reported major locus, underlying MYB/NB-ARC/tyrosine kinase candidate genes conferring resistance to two closely related A. euteiches isolates belonging to pea pathotype I. We also discovered a diversity of minor resistance QTL, not detected using p-value-based tests, some of which being putatively shared in response to pea (pathotype I and III) and/or alfalfa (race 1 and 2) isolates. Candidate genes underlying these QTL suggest pathogen effector recognition and plant proteasome as key functions associated with M. truncatula resistance to A. euteiches. GWAS on any organism can benefit from the local score approach to uncover many weak-effect QTL.

The effect of genotype imputation and some important factors on the accuracy of genomic prediction and its persistency over time

This study was conducted to investigate the effect of genotype imputation on accuracy of genomic prediction. In addition, the effect of heritability, marker density, and reference population size on the accuracy of genomic prediction and its persistency over time was also studied. To this end, a genome comprised of 3 chromosomes on which 6000 bi-allelic SNP with equal initial frequency of 0.5 were arrayed was simulated. For assessing the genotype imputation, respectively, 5%, 30%, 50%, 70%, 90% and 95% of genotypes were masked and then imputed with Random Forest algorithm. Two reference population size of 1000 and 5000 animals and two marker densities of 20 SNPs and 100 SNPs per centimorgan were considered. Correlation coefficient between the true and predicted genomic breeding values (rp,t) was used as the prediction accuracy. The imputation accuracy (IA) decreased in parallel to increase in the percentage of imputed genotypes. It was ≈1 when only 5% of genotypes were imputed; however, as the proportion of masked genotypes increased beyond 70%, a rapid decline in IA was observed. In addition, by increasing the percentage of masked genotypes, rp,t decreased in such a way that it decreased from 0.78 to, respectively, 0.77, 0.75, 0.73, 0.70, 0.61, and 0.51 when, respectively, 5%, 30%, 50%, 70%, 90% and 95% of genotypes were imputed. Therefore, genotype imputation could be used effectively to decrease the cost of genomic selection. Significant increases were observed in accuracy of genomic prediction following increase in heritability, marker density and reference population size. With a heritability level of 0.2, marker density of 20 SNPs/cM and a reference population size of 1000 individuals, after 9 generations derived from the reference population, the accuracy decreased by 26% (0.78 to 0.57). By increasing marker density to 100 SNP/cM, the rp,t decreased by 16% (from 0.88 to 0.72). Moreover, by increasing reference population size from 1000 to 5000 individuals, the accuracy decreased by 14% (from 0.92 to 0.79) after 9 generations. In addition, by increasing heritability from 0.2 to 0.5, after 9 generations, there was 14% decrease in rp,t. It was concluded that increasing the reference population size and marker density could be effective strategies to prevent decrease in accuracy of genomic evaluation over time.

Whole Genome Linkage Disequilibrium and Effective Population Size in a Coho Salmon (Oncorhynchus kisutch) Breeding Population Using a High-Density SNP Array.

The estimation of linkage disequilibrium between molecular markers within a population is critical when establishing the minimum number of markers required for association studies, genomic selection, and inferring historical events influencing different populations. This work aimed to evaluate the extent and decay of linkage disequilibrium in a coho salmon breeding population using a high-density SNP array. Linkage disequilibrium was estimated between a total of 93,502 SNPs found in 64 individuals (33 dams and 31 sires) from the breeding population. The markers encompass all 30 coho salmon chromosomes and comprise 1,684.62 Mb of the genome. The average density of markers per chromosome ranged from 48.31 to 66 per 1 Mb. The minor allele frequency averaged 0.26 (with a range from 0.22 to 0.27). The overall average linkage disequilibrium among SNPs pairs measured as r2 was 0.10. The Average r2 value decreased with increasing physical distance, with values ranging from 0.21 to 0.07 at a distance lower than 1 kb and up to 10 Mb, respectively. An r2 threshold of 0.2 was reached at distance of approximately 40 Kb. Chromosomes Okis05, Okis15 and Okis28 showed high levels of linkage disequilibrium (>0.20 at distances lower than 1 Mb). Average r2 values were lower than 0.15 for all chromosomes at distances greater than 4 Mb. An effective population size of 43 was estimated for the population 10 generations ago, and 325, for 139 generations ago. Based on the effective number of chromosome segments, we suggest that at least 74,000 SNPs would be necessary for an association mapping study and genomic predictions. Therefore, the SNP panel used allowed us to capture high-resolution information in the farmed coho salmon population. Furthermore, based on the contemporary Ne, a new mate allocation strategy is suggested to increase the effective population size.