Genome-wide Selection Research Articles

Selective efficiency of genome-wide selection in Coffea canephora breeding

Over the years, breeding programs have sought efficient strategies to select genotypes with superior performance. Genome-wide selection (GWS) emerged in 2001, aiming to increase efficiency and accelerate selection gain. This technique is considered essential in the breeding of perennial species, such as Coffea canephora, mainly due to the potential to increase the gain per unit of time. Thus, this study aimed to apply the GWS principle, evaluate the efficiency of this technique in C. canephora population using SNP molecular markers, and evaluate eight main phenotypic traits. A total of 165 genotypes were evaluated, being 51 of varietal group of Conilon, 32 of Robusta, and 82 intervarietal hybrids. Through the sequencing of the RAPiD Genomics company, 18,111 SNP markers were identified, of which 14,429 were used after quality analysis. All traits showed good predictive capacity, except for fruit maturation time, fruit size, and yield per plant. The lower values of genomic heritability found for these traits may justify the low values of predictive capacity obtained. The accuracy values estimated were considered as moderate to high, ranging from 67 to 82%. By shortening the cycle time from 6 to 3 years, GWS provided selective efficiency ranging from 22 to 146%. Results revealed that GWS provides higher gains per unit of time. Therefore, GWS proved to be a useful and promising tool for the breeding of C. canephora for accurately predicting the individuals’ genotypes, shortening the time required to complete the selection cycle and providing gains in selective efficiency per unit of time.

Genome-wide genetic structure and selection signatures for color in 10 traditional Chinese yellow-feathered chicken breeds

BackgroundYellow-feathered chickens (YFCs) have a long history in China. They are well-known for the nutritional and commercial importance attributable to their yellow color phenotype. Currently, there is a huge paucity in knowledge of the genetic determinants responsible for phenotypic and biochemical properties of these iconic chickens. This study aimed to uncover the genetic structure and the molecular underpinnings of the YFCs trademark coloration.ResultsThe whole-genomes of 100 YFCs from 10 major traditional breeds and 10 Huaibei partridge chickens from China were re-sequenced. Comparative population genomics based on autosomal single nucleotide polymorphisms (SNPs) revealed three geographically based clusters among the YFCs. Compared to other Chinese indigenous chicken genomes incorporated from previous studies, a closer genetic proximity within YFC breeds than between YFC breeds and other chicken populations is evident. Through genome-wide scans for selective sweeps, we identified RALY heterogeneous nuclear ribonucleoprotein (RALY), leucine rich repeat containing G protein-coupled receptor 4 (LGR4), solute carrier family 23 member 2 (SLC23A2), and solute carrier family 2 member 14 (SLC2A14), besides the classical beta-carotene dioxygenase 2 (BCDO2), as major candidates pigment determining genes in the YFCs.ConclusionWe provide the first comprehensive genomic data of the YFCs. Our analyses show phylogeographical patterns among the YFCs and potential candidate genes giving rise to the yellow color trait of the YFCs. This study lays the foundation for further research on the genome-phenotype cross-talks that define important poultry traits and for formulating genetic breeding and conservation strategies for the YFCs.

Entropy and mutual information in genome-wide selection: the splitting of k-fold cross-validation sets and implications for tree breeding

Random k-fold cross-validation in genome-wide selection (GWS) can help to estimate predictive ability ($$ {r}_{y\hat{y}} $$). Predictive ability tends to be higher when training, and validation sets present a high degree of kinship. However, many tree breeding populations are less genetically related to the training sets and have different levels of phenotypic diversity. Therefore, this study proposes methods of splitting k-fold cross-validation sets to optimize $$ {r}_{y\hat{y}} $$ estimates that are consistent with the breeding population and verify the impact of phenotypic and genotypic distribution on GWS. Using a simulated Eucalyptus trait (h2=0.5) and Pinus taeda L. data for diameter at breast height (h2=0.31), six methods were developed based on mutual information (I) and entropy (H) for measuring genetic similarity and phenotypic dissimilarity, respectively. All methods were evaluated for $$ {r}_{y\hat{y}} $$, bias, minimum squared error of prediction, and genomic heritability. The Pearson correlations of these parameters with the kinship coefficient, and I and H between and within training and validation sets were also estimated. Our results show that closer genetic similarity did not significantly increase $$ {r}_{y\hat{y}} $$ and that a lower H reduced $$ {r}_{y\hat{y}} $$ and overestimated genomic breeding values. Consequently, phenotypic diversity (high H) should be added to tree breeding populations to increase genetic gain and reduce bias. The new methods accurately fitted models according to the entropy of tree breeding populations and their genetic relationship to the training sets. Therefore, these methods provided usable estimates of genetic gain to produce consistent success of long-term tree breeding programs.

Evaluating and improving heritability models using summary statistics.

There is currently much debate regarding the best model for how heritability varies across the genome. The authors of GCTA recommend the GCTA-LDMS-I model, the authors of LD Score Regression recommend the Baseline LD model, and we have recommended the LDAK model. Here we provide a statistical framework for assessing heritability models using summary statistics from genome-wide association studies. Based on 31 studies of complex human traits (average sample size 136,000), we show that the Baseline LD model is more realistic than other existing heritability models, but that it can be improved by incorporating features from the LDAK model. Our framework also provides a method for estimating the selection-related parameter α from summary statistics. We find strong evidence (P < 1 × 10-6) of negative genome-wide selection for traits, including height, systolic blood pressure and college education, and that the impact of selection is stronger inside functional categories, such as coding SNPs and promoter regions.

Genomewide selection utilizing historic datasets improves early stage selection accuracy and selection stability

AbstractOver the past decade, commercial maize (Zea mays L.) breeding programs have generated a large quantity of complimentary phenotypic and genotypic datasets on their recurrent breeding populations. As many genetic subgroups and alleles are recycled over the years, these data can be a valuable resource for predicting the performance of future breeding cycles. Our objective was to test the efficacy of using historic breeding data within a genomewide selection (GWS) framework to improve plant breeding selections in early stage testing of maize lines. Across a multiyear maize breeding dataset propriety of Syngenta Seeds, LLC., we compared the selection accuracy of GWS and phenotypic selection (PS). Genomewide selection improved selection accuracy relative to PS by 15% for grain yield, 21% for grain moisture, 3% for test weight, and 58% for ear height. Additionally, we demonstrate the stability of GWS for plant selections through a stratified sampling procedure in which loss of testing environments was simulated. Plant selections with GWS were robust and stable with the simulated loss of up to four testing environments (out of seven). Specifically, under this scenario, where four early stage testing environments were lost, the average GWS selection accuracies only decreased relatively by 10, 8 and 5% for grain yield, grain moisture, and test weight, respectively, compared with an average relative reduction for PS by 35, 13, and 12% for grain yield, grain moisture, and test weight, respectively. These results indicate that an abundance of historic genotypic and phenotypic data can compensate for a lack of preliminary yield trials.

Genome-Wide Selection Scan in an Arabian Peninsula Population Identifies a TNKS Haplotype Linked to Metabolic Traits and Hypertension.

Despite the extreme and varying environmental conditions prevalent in the Arabian Peninsula, it has experienced several waves of human migrations following the out-of-Africa diaspora. Eventually, the inhabitants of the peninsula region adapted to the hot and dry environment. The adaptation and natural selection that shaped the extant human populations of the Arabian Peninsula region have been scarcely studied. In an attempt to explore natural selection in the region, we analyzed 662,750 variants in 583 Kuwaiti individuals. We searched for regions in the genome that display signatures of positive selection in the Kuwaiti population using an integrative approach in a conservative manner. We highlight a haplotype overlapping TNKS that showed strong signals of positive selection based on the results of the multiple selection tests conducted (integrated Haplotype Score, Cross Population Extended Haplotype Homozygosity, Population Branch Statistics, and log-likelihood ratio scores). Notably, the TNKS haplotype under selection potentially conferred a fitness advantage to the Kuwaiti ancestors for surviving in the harsh environment while posing a major health risk to present-day Kuwaitis.

Finding all maximal perfect haplotype blocks in linear time

Recent large-scale community sequencing efforts allow at an unprecedented level of detail the identification of genomic regions that show signatures of natural selection. Traditional methods for identifying such regions from individuals’ haplotype data, however, require excessive computing times and therefore are not applicable to current datasets. In 2019, Cunha et al. (Advances in bioinformatics and computational biology: 11th Brazilian symposium on bioinformatics, BSB 2018, Niterói, Brazil, October 30 - November 1, 2018, Proceedings, 2018. https://doi.org/10.1007/978-3-030-01722-4_3) suggested the maximal perfect haplotype block as a very simple combinatorial pattern, forming the basis of a new method to perform rapid genome-wide selection scans. The algorithm they presented for identifying these blocks, however, had a worst-case running time quadratic in the genome length. It was posed as an open problem whether an optimal, linear-time algorithm exists. In this paper we give two algorithms that achieve this time bound, one conceptually very simple one using suffix trees and a second one using the positional Burrows–Wheeler Transform, that is very efficient also in practice.

Genome-Wide Natural Selection Signatures Are Linked to Genetic Risk of Modern Phenotypes in the Japanese Population.

Elucidation of natural selection signatures and relationships with phenotype spectra is important to understand adaptive evolution of modern humans. Here, we conducted a genome-wide scan of selection signatures of the Japanese population by estimating locus-specific time to the most recent common ancestor using the ascertained sequentially Markovian coalescent (ASMC), from the biobank-based large-scale genome-wide association study data of 170,882 subjects. We identified 29 genetic loci with selection signatures satisfying the genome-wide significance. The signatures were most evident at the alcohol dehydrogenase (ADH) gene cluster locus at 4q23 (PASMC = 2.2 × 10−36), followed by relatively strong selection at the FAM96A (15q22), MYOF (10q23), 13q21, GRIA2 (4q32), and ASAP2 (2p25) loci (PASMC < 1.0 × 10−10). The additional analysis interrogating extended haplotypes (integrated haplotype score) showed robust concordance of the detected signatures, contributing to fine-mapping of the genes, and provided allelic directional insights into selection pressure (e.g., positive selection for ADH1B-Arg48His and HLA-DPB1*04:01). The phenome-wide selection enrichment analysis with the trait-associated variants identified a variety of the modern human phenotypes involved in the adaptation of Japanese. We observed population-specific evidence of enrichment with the alcohol-related phenotypes, anthropometric and biochemical clinical measurements, and immune-related diseases, differently from the findings in Europeans using the UK Biobank resource. Our study demonstrated population-specific features of the selection signatures in Japanese, highlighting a value of the natural selection study using the nation-wide biobank-scale genome and phenotype data.

Genomewide predictions as a substitute for a portion of phenotyping in maize

AbstractWhen genomewide predictions are available, maize (Zea mays L.) breeders may consider foregoing first‐year phenotyping of testcrosses or, at the very least, reducing the number of locations used in phenotyping. Our objectives were to determine the equivalency between genomewide predictions and the number of locations used in phenotyping, and the extent to which genomewide predictions can reduce subsequent phenotyping in maize. For each of 21 test populations, we constructed half‐sib training populations from prior biparental populations evaluated in multiple environments. Marker data were available for 2911 single nucleotide polymorphism markers. We estimated the number of locations (LEq) for which the response to phenotypic selection was equal to the response to genomewide selection. The median analytical estimate of LEq (cross‐validation estimate of LEq in parentheses) was 1.1 (1) for yield, 1.8 (2) for moisture, and 3.0 (3) for test weight. The estimates of LEq varied widely among the test populations. We estimated the response to selection for an index that combined genomewide predictions and phenotypic data from different numbers of predictor locations (LP). The improvement in the response when LP increased from 0 (genomewide selection) to 1 was greater than the improvement in the response when LP increased from 1 to 2. This result suggested that phenotyping even at a single location captured signals that genomewide prediction did not capture. The analysis herein is helpful in designing breeding schemes that achieve a balance between the amount of genetic gain and the time and cost required to achieve such gain.

Genome-wide analyses reveal the role of noncoding variation in complex traits during rice domestication.

Genomes carry millions of noncoding variants, and identifying the tiny fraction with functional consequences is a major challenge for genomics. We assessed the role of selection on long noncoding RNAs (lncRNAs) for domestication-related changes in rice grains. Among 3363 lncRNA transcripts identified in early developing panicles, 95% of those with differential expression (329 lncRNAs) between Oryza sativa ssp. japonica and wild rice were significantly down-regulated in the domestication event. Joint genome and transcriptome analyses reveal that directional selection on lncRNAs altered the expression of energy metabolism genes during domestication. Transgenic experiments and population analyses with three focal lncRNAs illustrate that selection on these loci led to increased starch content and grain weight. Together, our findings indicate that genome-wide selection for lncRNA down-regulation was an important mechanism for the emergence of rice domestication traits.

Genome-wide association study and genomic selection for soybean chlorophyll content associated with soybean cyst nematode tolerance

BackgroundSoybean cyst nematode (SCN), Heterodera glycines Ichinohe, has been one of the most devastating pathogens affecting soybean production. In the United States alone, SCN damage accounted for more than $1 billion loss annually. With a narrow genetic background of the currently available SCN-resistant commercial cultivars, high risk of resistance breakdown can occur. The objectives of this study were to conduct a genome-wide association study (GWAS) to identify QTL, SNP markers, and candidate genes associated with soybean leaf chlorophyll content tolerance to SCN infection, and to carry out a genomic selection (GS) study for the chlorophyll content tolerance.ResultsA total of 172 soybean genotypes were evaluated for the effect of SCN HG Type 1.2.3.5.6.7 (race 4) on soybean leaf chlorophyll. The soybean lines were genotyped using a total of 4089 filtered and high-quality SNPs. Results showed that (1) a large variation in SCN tolerance based on leaf chlorophyll content indices (CCI); (2) a total of 22, 14, and 16 SNPs associated with CCI of non-SCN-infected plants, SCN-infected plants, and reduction of CCI SCN, respectively; (3) a new locus of chlorophyll content tolerance to SCN mapped on chromosome 3; (4) candidate genes encoding for Leucine-rich repeat protein, plant hormone signaling molecules, and biomolecule transporters; and (5) an average GS accuracy ranging from 0.31 to 0.46 with all SNPs and varying from 0.55 to 0.76 when GWAS-derived SNP markers were used across five models. This study demonstrated the potential of using genome-wide selection to breed chlorophyll-content-tolerant soybean for managing SCN.ConclusionsIn this study, soybean accessions with higher CCI under SCN infestation, and molecular markers associated with chlorophyll content related to SCN were identified. In addition, a total of 15 candidate genes associated with chlorophyll content tolerance to SCN in soybean were also identified. These candidate genes will lead to a better understanding of the molecular mechanisms that control chlorophyll content tolerance to SCN in soybean. Genomic selection analysis of chlorophyll content tolerance to SCN showed that using significant SNPs obtained from GWAS could provide better GS accuracy.

Dissecting the genetics of cold tolerance in a multiparental maize population.

We identify the largest amount of QTLs for cold tolerance in maize; mainly associated with photosynthetic efficiency, which opens new possibilities for genomic selection for cold tolerance in maize. Breeding for cold tolerance in maize is an important objective in temperate areas. The objective was to carry out a highly efficient study of quantitative trait loci (QTLs) for cold tolerance in maize. We evaluated 406 recombinant inbred lines from a multi-parent advanced generation intercross (MAGIC) population in a growth chamber under cold and control conditions, and in the field at early and normal sowing. We recorded cold tolerance-related traits, including the number of days from sowing to emergence, chlorophyll content and maximum quantum efficiency of photosystem II (Fv/Fm). Association mapping was based on genotyping with near one million single nucleotide polymorphism (SNP) markers. We found 858 SNPs significantly associated with all traits, most of them under cold conditions and early sowing. Most QTLs were associated with chlorophyll and Fv/Fm. Many candidate genes coincided between the current research and previous reports. These results suggest that (1) the MAGIC population is an efficient tool for identifying QTLs for cold tolerance; (2) most QTLs for cold tolerance were associated with Fv/Fm; (3) most of these QTLs were located in specific genomic regions, particularly bin 10.04; (4) the current study allows genetically improving cold tolerance with genome-wide selection.

Genetic Diversity and Signatures of Selection in 15 Chinese Indigenous Dog Breeds Revealed by Genome-Wide SNPs

There are dozens of recognized indigenous dog breeds in China. However, these breeds have not had extensive studies to describe their population structure, genomic linkage disequilibrium (LD) patterns, and selection signatures. Here, we systematically surveyed the genomes of 157 unrelated dogs that were from 15 diverse Chinese dog breeds. Canine 170K SNP chips were used to compare the genomic structures of Chinese and Western dogs. The genotyping data of 170K SNP chips in Western dogs were downloaded from the LUPA (a European initiative of canine genome project) database. Chinese indigenous dogs had lower LD and shorter accumulative runs of homozygosity (ROH) in the genome. The genetic distances between individuals within each Chinese breed were larger than those within Western breeds. Chinese indigenous and Western dog breeds were clearly differentiated into two separate clades revealed by the PCA and NJ-tree. We found evidence for historical introgression of Western dogs into Chinese Kazakhstan shepherd and Mongolia Xi dogs. We suggested that Greenland sledge dog, Papillon, and European Eurasier have Chinese dog lineages. Selection sweep analysis identified genome-wide selection signatures of each Chinese breed and three breed groups. We highlighted several genes including EPAS1 and DNAH9 that show signatures of natural selection in Qinghai-Tibetan plateau dogs and are likely important for genetic adaptation to high altitude. Comparison of our findings with previous reports suggested RBP7, NMNAT1, SLC2A5, and H6PD that exhibit signatures of natural selection in Chinese mountain hounds as promising candidate genes for the traits of endurance and night vision, and NOL8, KRT9, RORB, and CAMTA1 that show signals of selection in Xi dogs might be candidate genes influencing dog running speed. The results about genomic and population structures, and selection signatures of Chinese dog breeds reinforce the conclusion that Chinese indigenous dogs with great variations of phenotypes are important resources for identifying genes responsible for complex traits.

Comprehensive genome and transcriptome analyses reveal genetic relationship, selection signature, and transcriptome landscape of small-sized Korean native Jeju horse

The Jeju horse, indigenous to the Jeju Island in Korea may have originated from Mongolian horses. Adaptations to the local harsh environment have conferred Jeju horse with unique traits such as small-sized body, stocky head, and shorter limbs. These characteristics have not been studied previously at the genomic level. Therefore, we sequenced and compared the genome of 41 horses belonging to 6 breeds. We identified numerous breed-specific non-synonymous SNPs and loss-of-function mutants. Demographic and admixture analyses showed that, though Jeju horse is genetically the closest to the Mongolian breeds, its genetic ancestry is independent of that of the Mongolian breeds. Genome wide selection signature analysis revealed that genes such as LCORL, MSTN, HMGA2, ZFAT, LASP1, PDK4, and ACTN2, were positively selected in the Jeju horse. RNAseq analysis showed that several of these genes were also differentially expressed in Jeju horse compared to Thoroughbred horse. Comparative muscle fiber analysis showed that, the type I muscle fibre content was substantially higher in Jeju horse compared to Thoroughbred horse. Our results provide insights about the selection of complex phenotypic traits in the small-sized Jeju horse and the novel SNPs identified will aid in designing high-density SNP chip for studying other native horse breeds.

An Integrated Molecular and Conventional Breeding Scheme for Enhancing Genetic Gain in Maize in Africa.

Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers’ fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers’ livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners.

A Combined Multi-Cohort Approach Reveals Novel and Known Genome-Wide Selection Signatures for Wool Traits in Merino and Merino-Derived Sheep Breeds.

Merino sheep represents a valuable genetic resource worldwide. In this study, we investigated selection signatures in Merino (and Merino-derived) sheep breeds using genome-wide SNP data and two different approaches: a classical FST-outlier method and an approach based on the analysis of local ancestry in admixed populations. In order to capture the most reliable signals, we adopted a combined, multi-cohort approach. In particular, scenarios involving four Merino breeds (Spanish Merino, Australian Merino, Chinese Merino, and Sopravissana) were tested via the local ancestry approach, while nine pair-wise breed comparisons contrasting the above breeds, as well as the Gentile di Puglia breed, with non-Merino breeds from the same geographic area were tested via the FST-outlier method. Signals observed using both methods were compared with genome-wide patterns of distribution of runs of homozygosity (ROH) islands. Novel and known selection signatures were detected. The most reliable signals were observed on OAR 3 (MSRB3 and LEMD3), OAR10 (FRY and RXFP2), OAR 13 (RALY), OAR17 (FAM101A), and OAR18 (NFKBIA, SEC23A, and PAX9). All the above overlapped with known QTLs for wool traits, and evidences from the literature of their involvement in skin/hair/wool biology, as well as gene network analysis, further corroborated these results. The signal on OAR10 also contains well known evidence for association with horn morphology and polledness. More elusive biological evidences of association with the Merino phenotype were observed for a number of other genes, notably LOC101120019 and TMEM132B (OAR17), LOC105609948 (OAR3), LOC101110773 (OAR10), and EIF2S2 (OAR17). Taken together, the above results further contribute to decipher the genetic basis underlying the Merino phenotype.

Functional models in genome-wide selection.

The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F∞ populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.

Triple categorical regression for genomic selection: application to cassava breeding

Genome-wide selection (GWS) is currently a technique of great importance in plant breeding, since it improves efficiency of genetic evaluations by increasing genetic gains. The process is based on genomic estimated breeding values (GEBVs) obtained through phenotypic and dense marker genomic information. In this context, GEBVs of N individuals are calculated through appropriate models, which estimate the effect of each marker on phenotypes, allowing the early identification of genetically superior individuals. However, GWS leads to statistical challenges, due to high dimensionality and multicollinearity problems. These challenges require the use of statistical methods to approach the regularization of the estimation process. Therefore, we aimed to propose a method denominated as triple categorical regression (TCR) and compare it with the genomic best linear unbiased predictor (G-BLUP) and Bayesian least absolute shrinkage and selection operator (BLASSO) methods that have been widely applied to GWS. The methods were evaluated in simulated populations considering four different scenarios. Additionally, a modification of the G-BLUP method was proposed based on the TCR-estimated (TCR/G-BLUP) results. All methods were applied to real data of cassava (Manihot esculenta) with to increase efficiency of a current breeding program. The methods were compared through independent validation and efficiency measures, such as prediction accuracy, bias, and recovered genomic heritability. The TCR method was suitable to estimate variance components and heritability, and the TCR/G-BLUP method provided efficient GEBV predictions. Thus, the proposed methods provide new insights for GWS.

Indirect response in genome wide selection using selected markers

The goal of this work was to compare the effect of the accuracy and residual variance in genome wide selection using marker selection as well as using the effect of the indirect selection, using simulated and real data. In simulated data was used one sample with 200 individuals with 1,000 molecular markers in F2 population. The real data was obtained in maize with F2 population with 441 individuals and genotyping with 261 SSR markers. There was 11 traits evaluated (ear length, ear width, row number, kernels per row, 100-kernel weight, ear weight, grain yield, length of branch, number of branch, plant height and ear height). All data was analyzed using rrBLUP method and 10-fold cross-validation. In simulated and maize data the results were similar: the residual variance with few markers is lower than with the 1000 markers and the accuracy with few markers is bigger than with 1000 markers. For maize data multi trait selection, the accuracy increased when the correlation between traits is greater than 0.50 and residual variance decreased when the correlation is greater than 0.70. In this sense, these results showed that marker selection could be used as a first step in genome wide selection, improving the prediction and compute demand.

Whole-genome mapping of quantitative trait loci and accuracy of genomic predictions for resistance to columnaris disease in two rainbow trout breeding populations

BackgroundColumnaris disease (CD) is an emerging problem for the rainbow trout aquaculture industry in the US. The objectives of this study were to: (1) identify common genomic regions that explain a large proportion of the additive genetic variance for resistance to CD in two rainbow trout (Oncorhynchus mykiss) populations; and (2) estimate the gains in prediction accuracy when genomic information is used to evaluate the genetic potential of survival to columnaris infection in each population.MethodsTwo aquaculture populations were investigated: the National Center for Cool and Cold Water Aquaculture (NCCCWA) odd-year line and the Troutlodge, Inc., May odd-year (TLUM) nucleus breeding population. Fish that survived to 21 days post-immersion challenge were recorded as resistant. Single nucleotide polymorphism (SNP) genotypes were available for 1185 and 1137 fish from NCCCWA and TLUM, respectively. SNP effects and variances were estimated using the weighted single-step genomic best linear unbiased prediction (BLUP) for genome-wide association. Genomic regions that explained more than 1% of the additive genetic variance were considered to be associated with resistance to CD. Predictive ability was calculated in a fivefold cross-validation scheme and using a linear regression method.ResultsValidation on adjusted phenotypes provided a prediction accuracy close to zero, due to the binary nature of the trait. Using breeding values computed from the complete data as benchmark improved prediction accuracy of genomic models by about 40% compared to the pedigree-based BLUP. Fourteen windows located on six chromosomes were associated with resistance to CD in the NCCCWA population, of which two windows on chromosome Omy 17 jointly explained more than 10% of the additive genetic variance. Twenty-six windows located on 13 chromosomes were associated with resistance to CD in the TLUM population. Only four associated genomic regions overlapped with quantitative trait loci (QTL) between both populations.ConclusionsOur results suggest that genome-wide selection for resistance to CD in rainbow trout has greater potential than selection for a few target genomic regions that were found to be associated to resistance to CD due to the polygenic architecture of this trait, and because the QTL associated with resistance to CD are not sufficiently informative for selection decisions across populations.