Camila Ferreira Azevedo

Ridge, Lasso and Bayesian additive-dominance genomic models

BackgroundA complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes).ResultsG-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close.ConclusionsAmongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.

Regional heritability mapping and genome-wide association identify loci for complex growth, wood and disease resistance traits in Eucalyptus

Although genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64-89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP-trait associations. RHM and GWAS QTLs individually explained 5-15% and 4-6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.

Supervised independent component analysis as an alternative method for genomic selection in pigs

The objective of this work was to evaluate the efficiency of the supervised independent component regression (SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross-validation. These values were also compared with other methods generally used for the same purposes, such as RR-BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values.

Regressão via componentes independentes aplicada à seleção genômica para características de carcaça em suínos

The objective of this work was to evaluate the efficiency of the independent component regression (ICR) method for the estimation of genomic values and of SNP marker effects for carcass traits in a F2 pig population (Piau x commercial line). The methods were evaluated by the agreement between the genetic predicted values and the corrected phenotypes observed by cross‑validation, and they were also compared with other methods generally used for the same purposes, such as RR‑BLUP, PCR, and PLS. The ICR and PCR methods show similar results, but ICR has the highest accuracy prediction values.

Classificação multivariada de curvas de progresso da requeima do tomateiro entre acessos do banco de germaplasma de hortaliças da UFV

The objective of this paper was to present a methodology for the analysis of experiments in plant pathology that considers the comparison of disease progress curves in the presence of a large number of treatments by cluster analysis. Forty-two accessions were grown from the Germoplasma Vegetable Bank (BGH), of Universidade Federal de Vicosa (UFV). The exponential model was fitted to the data of late blight severity percentage, and the obtained parameter estimates obtained on the initial incidence of the disease (yo) and rate of disease progression (r) - were submitted to the multivariate analysis of variance (MANOVA). The adjusted means were submitted to the cluster analysis. An optimal number of six distinct groups was observed.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

Divergência genética e índice de seleção via BLUP em acessos de algodoeiro para características tecnológicas da fibra

Technological cotton fiber traits are essential for the quality and remuneration of its products. This study aimed to estimate the genetic divergence among cotton accessions and assort the best ones, based in a selection index combining all the important traits. A total of 248 cotton accessions were evaluated using multivariate analysis (Mahalanobis distance and Tocher grouping) and a selection index based in average rank via mixed models (REML/BLUP). The Tocher grouping analysis enabled the structuring of the accessions population by allocating them into 14 divergent groups. Selective accuracies were high for all traits, ranging from 0.89 to 0.94, indicating a favorable scenario for selection. Genetic correlations among the six traits were moderate to low, making impossible the breeding of a trait, via indirect selection, in another. The simultaneous selection for fiber traits, concerning the Mulamba and Mock selection index, showed to be promising. The best accessions for the six traits were simultaneously the 4S180, C96480, Giza75, 196Lasani11, Brown Egyptian, Early Fluff 316, C268-80 and 207MG-82607.

Genome wide selection in Citrus breeding

Genome wide selection (GWS) is essential for the genetic improvement of perennial species such as Citrus because of its ability to increase gain per unit time and to enable the efficient selection of characteristics with low heritability. This study assessed GWS efficiency in a population of Citrus and compared it with selection based on phenotypic data. A total of 180 individual trees from a cross between Pera sweet orange (Citrus sinensis Osbeck) and Murcott tangor (Citrus sinensis Osbeck x Citrus reticulata Blanco) were evaluated for 10 characteristics related to fruit quality. The hybrids were genotyped using 5287 DArT_seqTM (diversity arrays technology) molecular markers and their effects on phenotypes were predicted using the random regression - best linear unbiased predictor (rr-BLUP) method. The predictive ability, prediction bias, and accuracy of GWS were estimated to verify its effectiveness for phenotype prediction. The proportion of genetic variance explained by the markers was also computed. The heritability of the traits, as determined by markers, was 16-28%. The predictive ability of these markers ranged from 0.53 to 0.64, and the regression coefficients between predicted and observed phenotypes were close to unity. Over 35% of the genetic variance was accounted for by the markers. Accuracy estimates with GWS were lower than those obtained by phenotypic analysis; however, GWS was superior in terms of genetic gain per unit time. Thus, GWS may be useful for Citrus breeding as it can predict phenotypes early and accurately, and reduce the length of the selection cycle. This study demonstrates the feasibility of genomic selection in Citrus.

Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information.

Knowledge of dominance effects should improve ge-netic evaluations, provide the accurate selection of purebred animals, and enable better breeding strategies, including the exploitation of het-erosis in crossbreeds. In this study, we combined genomic and pedi-gree data to study the relative importance of additive and dominance genetic variation in growth and carcass traits in an F2 pig population. Two GBLUP models were used, a model without a polygenic effect (ADM) and a model with a polygenic effect (ADMP). Additive effects played a greater role in the control of growth and carcass traits than did dominance effects. However, dominance effects were important for all traits, particularly in backfat thickness. The narrow-sense and broad-sense heritability estimates for growth (0.06 to 0.42, and 0.10 to 0.51, respectively) and carcass traits (0.07 to 0.37, and 0.10 to 0.76, respec-tively) exhibited a wide variation. The inclusion of a polygenic effect in the ADMP model changed the broad-sense heritability estimates only for birth weight and weight at 21 days of age.

Mapeamento de QTL para características de crescimento de suínos por meio de modelos de regressão aleatória

The objective of this work was to determine the biological parameters and fertility life table of the strawberry sap beetle (Lobiopa insularis) reared on artificial diet in laboratory conditions. The duration and the average viability of the embryonic, larval, pupal, and egg-to-adult periods were: 4.1±1.5 days and 80.6%; 22.2±5.0 days and 60%; 10.8±2.3 days and 90%; and 37.1±8.8 days and 43.5%, respectively. The pre-oviposition, oviposition, and post-oviposition periods were 96±18.9, 133±27.5, and 77±16.3 days. The longevity of males (271±20.7 days) was lower than that of females (318±14.9 days). There is potential for using artificial diet based on strawberries for the multiplication of the strawberry sap beetle in laboratory.