Genome-enabled prediction of genetic values using radial basis function neural networks

The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait-environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.     

Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle

Background

Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can capture cryptic relationships between SNPs (single nucleotide polymorphisms) and phenotypic values without the need of explicitly defining a genetic model. This concept is attractive for high-dimensional and noisy data, especially when the genetic architecture of the trait is unknown. However, the properties of ANN for the prediction of future outcomes of genomic selection using real data are not well characterized and, due to high computational costs, using whole-genome marker sets is difficult. We examined different non-linear network architectures, as well as several genomic covariate structures as network inputs in order to assess their ability to predict milk traits in three dairy cattle data sets using large-scale SNP data. For training, a regularized back propagation algorithm was used. The average correlation between the observed and predicted phenotypes in a 20 times 5-fold cross-validation was used to assess predictive ability. A linear network model served as benchmark.

Results

Predictive abilities of different ANN models varied markedly, whereas differences between data sets were small. Dimension reduction methods enhanced prediction performance in all data sets, while at the same time computational cost decreased. For the Holstein-Friesian bull data set, an ANN with 10 neurons in the hidden layer achieved a predictive correlation of r=0.47 for milk yield when the entire marker matrix was used. Predictive ability increased when the genomic relationship matrix (r=0.64) was used as input and was best (r=0.67) when principal component scores of the marker genotypes were used. Similar results were found for the other traits in all data sets.

Conclusion

Artificial neural networks are powerful machines for non-linear genome-enabled predictions in animal breeding. However, to produce stable and high-quality outputs, variable selection methods are highly recommended, when the number of markers vastly exceeds sample size.     

Genetic parameters for carcass weight,conformation and fat in five beef cattle breeds

Profitability of beef production can be increased by genetically improving carcass traits. To construct breeding value evaluations for carcass traits, breed-specific genetic parameters were estimated for carcass weight, carcass conformation and carcass fat in five beef cattle breeds in Finland (Hereford, Aberdeen Angus, Simmental, Charolais and Limousin). Conformation and fat were visually scored using the EUROP carcass classification. Each breed was separately analyzed using a multitrait animal model. A total of 6879–19 539 animals per breed had phenotypes. For the five breeds, heritabilities were moderate for carcass weight (h²=0.39 to 0.48, s.e.=0.02 to 0.04) and slightly lower for conformation (h²=0.30 to 0.44, s.e.=0.02 to 0.04) and carcass fat (h²=0.29 to 0.44, s.e.=0.02 to 0.04). The genetic correlation between carcass weight and conformation was favorable in all breeds (r_G=0.37 to 0.53, s.e.=0.04 to 0.05), heavy carcasses being genetically more conformed. The phenotypic correlation between carcass weight and carcass fat was moderately positive in all breeds (r_P=0.21 to 0.32), implying that increasing carcass weight was related to increasing fat levels. The respective genetic correlation was the strongest in Hereford (r_G=0.28, s.e.=0.05) and Angus (r_G=0.15, s.e.=0.05), the two small body-sized British breeds with the lowest conformation and the highest fat level. The correlation was weaker in the other breeds (r_G=0.08 to 0.14). For Hereford, Angus and Simmental, more conformed carcasses were phenotypically fatter (r_P=0.11 to 0.15), but the respective genetic correlations were close to zero (r_G=−0.05 to 0.04). In contrast, in the two large body-sized and muscular French breeds, the genetic correlation between conformation and fat was negative and the phenotypic correlation was close to zero or negative (Charolais: r_G=−0.18, s.e.=0.06, r_P=0.02; Limousin: r_G=−0.56, s.e.=0.04, r_P=−0.13). The results indicate genetic variation for the genetic improvement of the carcass traits, favorable correlations for the simultaneous improvement of carcass weight and conformation in all breeds, and breed differences in the correlations of carcass fat.     

Near infrared spectroscopy coupled with radial basis function neural network for at-line monitoring of Lactococcus lactis subsp. fermentation

In our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation.     

Milk protein composition in purebred Holsteins and in first/second-generation crossbred cows from Swedish Red,Montbeliarde and Brown Swiss bulls

The aim of this study was to analyze milk protein composition in purebred and crossbred dairy cattle and estimate the effects of individual sources of variation on the investigated traits. Milk samples were collected from 505 cows from three commercial farms located in Northern Italy, some of which had originated from crossbreeding programs, although most were purebred Holsteins (HO). The basic crossbreeding scheme was a three-breed rotational system using Swedish Red (SR) semen on HO cows (SR×HO), Montbeliarde (MO) semen on SR×HO cows (MO×(SR×HO)) and HO semen again on MO×(SR×HO) cows. A smaller number of purebred HO from each of the herds were mated inverting the breed order (MO×HO and SR×(MO×HO)) or using Brown Swiss (BS) bulls (BS×HO) then MO bulls (MO×(BS×HO)). Milk samples were analyzed by reverse-phase HPLC to obtain protein fraction amounts (g/l) and proportions (% of total true protein). Traits were analyzed using a linear model, which included the fixed effects of herd-test-day (HTD), parity, days in milk and breed combination. Results showed that milk protein fractions were influenced by HTD, stage of lactation, parity and breed combination. The increase in protein concentration during lactation was due in particular to β-casein (β-CN), α_S1-CN and β-lactoglobulin (β-LG). The higher protein content of primiparous milk was mainly due to higher concentrations of all casein fractions. The milk from crossbred cows had higher contents and proportions of κ-CN and α-lactalbumin (α-LA), lower proportions of β-LG and greater proportion of caseins/smaller in whey proteins on milk true protein than purebred HO. The three-way crossbreds differed from two-way crossbreds only in having greater proportions of α-LA in their milk. Of the three-way crossbreds, the SR sired cows yielded milk with a smaller content and proportion of β-LG than the MO sired cows, and, consequently, a higher proportion of caseins than whey proteins. Results from this study support the feasibility of using crossbreeding programs to alter milk protein profiles with the aim of improving milk quality and cheese-making properties.     

Association between the prion protein genotype and animal performance traits in a large multibreed sheep population

Genetic susceptibility to scrapie, a fatal disease of sheep and goats, is modulated by polymorphisms in the prion protein (PrP). Neither the frequency of the PrP genotypes nor their association with animal performance has been investigated in a large multibreed Irish sheep population. Scrapie genotypes were available on 16 416 animals; the breeds represented included purebred Belclare (733), Charollais (333), Suffolk (739), Texel (1 857), Vendeen (191), and crossbreds (12 563). Performance data on lambing, lamb and ewe performance as well as health traits were available. The association between alternative approaches of describing the PrP genotype (i.e. 15 individually called PrP genotypes, five genotype classes representing susceptibility to scrapie, or number of ARR haplotypes) and animal performance were quantified using animal linear mixed models. All 15 of the possible scrapie genotypes were detected, although the frequency differed by breed. The frequency of the five PrP haplotypes in the entire population were 0.70 (ARR), 0.15 (ARQ), 0.11 (ARH), 0.02 (AHQ) and 0.01 (VRQ); the most susceptible haplotype (VRQ) was only detected in purebred Texels and crossbreds. No association was detected between the PrP genotype of either the animal or dam and any of the lambing traits (i.e. lambing difficulty score, perinatal mortality and birth weight). With the exception of ultrasound muscle depth, no association between the PrP genotype and any of the lamb performance traits (i.e. lamb BW and carcass) was observed. Lambs carrying the category four PrP genotype (i.e. ARR/VRQ) had 1.20 (SE = 0.45) mm, 1.38 (SE = 0.12) mm, 1.47 (S = 0.25) mm shallower ultrasound muscle depth relative to lambs of the less susceptible scrapie categories of 1, 2, 3, respectively (P < 0.05). Nonetheless, no association between PrP genotype and lamb carcass conformation, the ultimate end goal of producers, was detected. Ewe litter size, body condition score or lameness did not differ by PrP genotype of the ewe (P > 0.05). For ewe mature BW, ARH/VRQ ewes differed from most other ewe PrP genotypes and were, on average, 3.79 (SE = 1.66) kg heavier than ARR/ARR genotype ewes. Lamb dag score differed by dam PrP genotype (P < 0.05), although the differences were small. Results from this study show that scrapie is segregating within the Irish sheep population, but the PrP genotype was not associated with most traits investigated and, where associations were detected, the biological significance was minimal. This suggests minimal impact of selection on PrP genotype on performance, at least for the traits investigated in the present study.     

Discovery,design, and synthesis of anti-metastatic lead phenylmethylene hydantoins inspired by marine natural products

The Red Sea sponge Hemimycale arabica afforded the known (Z)-5-(4-hydroxybenzylidene)-hydantoin (1), (R)-5-(4-hydroxybenzyl)hydantoin (2), and (Z)-5-((6-bromo-1H-indol-3-yl)methylene)-hydantoin (3). The natural phenylmethylene hydantoin (PMH) 1 and the synthetic (Z)-5-(4-(ethylthio)benzylidene)-hydantoin (4) showed potent in vitro anti-growth and anti-invasive properties against PC-3M prostate cancer cells in MTT and spheroid disaggregation assays. PMHs 1 and 4 also showed significant anti-invasive activities in orthotopic xenograft and transgenic mice models. To study the effect of electronic and lipophilic parameters on the activity, a wide array of several substituted aldehydes possessing electron-withdrawing (+σ), lipophilic (+π), electron-donating (?σ), and less lipophilic substituents (?π) were used to synthesize several PMHs. Few des-phenylmethylenehydantoins and 2-thiohydanoins were also synthesized and the anti-invasive activities of all compounds were evaluated. Comparative molecular field analysis (CoMFA) was then used to study the 3D QSAR. Predictive 3D QSAR model with conventional r² and cross validated coefficient (q²) values up to 0.910 and 0.651 were established. In conclusion, PMH is a novel antimetastatic lead class with potential to control metastatic prostate cancer.     

Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers

Four approaches using single-nucleotide polymorphism (SNP) information (F(infinity)-metric model, kernel regression, reproducing kernel Hilbert spaces (RKHS) regression, and a Bayesian regression) were compared with a standard procedure of genetic evaluation (E-BLUP) of sires using mortality rates in broilers as a response variable, working in a Bayesian framework. Late mortality (14-42 days of age) records on 12,167 progeny of 200 sires were precorrected for fixed and random (nongenetic) effects used in the model for genetic evaluation and for the mate effect. The average of the corrected records was computed for each sire. Twenty-four SNPs seemingly associated with late mortality were included in three methods used for genomic assisted evaluations. One thousand SNPs were included in the Bayesian regression, to account for markers along the whole genome. The posterior mean of heritability of mortality was 0.02 in the E-BLUP approach, suggesting that genetic evaluation could be improved if suitable molecular markers were available. Estimates of posterior means and standard deviations of the residual variance were 24.38 (3.88), 29.97 (3.22), 17.07 (3.02), and 20.74 (2.87) for E-BLUP, the linear model on SNPs, RKHS regression, and the Bayesian regression, respectively, suggesting that RKHS accounted for more variance in the data. The two nonparametric methods (kernel and RKHS regression) fitted the data better, having a lower residual sum of squares. Predictive ability, assessed by cross-validation, indicated advantages of the RKHS approach, where accuracy was increased from 25 to 150%, relative to other methods.     

Flux-Concentration Duality in Dynamic Nonequilibrium Biological Networks

The structure of dynamic states in biological networks is of fundamental importance in understanding their function. Considering the elementary reaction structure of reconstructed metabolic networks, we show how appreciation of a gradient matrix, G = dv/dx (where v is the vector of fluxes and x is the vector of concentrations), enables the formulation of dual Jacobian matrices. One is for concentrations, J_x = S·G, and the other is for fluxes, J_v = G·S. The fundamental properties of these two Jacobians and the underlying duality that relates them are delineated. We describe a generalized approach to decomposing reaction networks in terms of the thermodynamic and kinetic components in the context of the network structure. The thermodynamic and kinetic influences can be viewed in terms of direction-driver relationships in the network.     

Comparing genomic prediction accuracy from purebred,crossbred and combined purebred and crossbred reference populations in sheep

Background

The accuracy of genomic prediction depends largely on the number of animals with phenotypes and genotypes. In some industries, such as sheep and beef cattle, data are often available from a mixture of breeds, multiple strains within a breed or from crossbred animals. The objective of this study was to compare the accuracy of genomic prediction for several economically important traits in sheep when using data from purebreds, crossbreds or a combination of those in a reference population.

Methods

The reference populations were purebred Merinos, crossbreds of Border Leicester (BL), Poll Dorset (PD) or White Suffolk (WS) with Merinos and combinations of purebred and crossbred animals. Genomic breeding values (GBV) were calculated based on genomic best linear unbiased prediction (GBLUP), using a genomic relationship matrix calculated based on 48 599 Ovine SNP (single nucleotide polymorphisms) genotypes. The accuracy of GBV was assessed in a group of purebred industry sires based on the correlation coefficient between GBV and accurate estimated breeding values based on progeny records.

Results

The accuracy of GBV for Merino sires increased with a larger purebred Merino reference population, but decreased when a large purebred Merino reference population was augmented with records from crossbred animals. The GBV accuracy for BL, PD and WS breeds based on crossbred data was the same or tended to decrease when more purebred Merinos were added to the crossbred reference population. The prediction accuracy for a particular breed was close to zero when the reference population did not contain any haplotypes of the target breed, except for some low accuracies that were obtained when predicting PD from WS and vice versa.

Conclusions

This study demonstrates that crossbred animals can be used for genomic prediction of purebred animals using 50 k SNP marker density and GBLUP, but crossbred data provided lower accuracy than purebred data. Including data from distant breeds in a reference population had a neutral to slightly negative effect on the accuracy of genomic prediction. Accounting for differences in marker allele frequencies between breeds had only a small effect on the accuracy of genomic prediction from crossbred or combined crossbred and purebred reference populations.     

Synthesis and in vitro evaluation of 3-(4-nitrophenyl)coumarin derivatives in tumor cell lines

Coumarins are naturally-occurring compounds that have attracted considerable interest due to their numerous biological activities depending on their pattern of substitution on the coumarin molecule. In this present investigation, we synthesized 3-(4-nitrophenyl)coumarin derivatives (9a–e) and evaluated their in vitro cytotoxic effect on human lung (A549), breast (MDA-MB-231) and prostate (PC3) cancer cell lines for 48 h using crystal violet dye binding assay. Cytotoxic effects of the most active compound on normal human lung (MRC-9) and breast (MCF-10A) cell lines, cell cycle analysis using flow cytometry and mitochondrial membrane potential (MMP) using Tetramethyl Rhodamine Methyl Ester (TMRM; rhodamine-123) fluorescent dye were also examined. Among the compounds that were evaluated, 9c showed cytotoxic effect (active), caused significant cells arrest (p < 0.05) in G₀/G₁ and S phases of cell cycle and loss of MMP in A459, MDA-MB-231 and PC3 cell lines. Additionally, the cytotoxic effect of 9c was compared to reference drugs (Coumarin and Docetaxel) for comparative study. These results further demonstrate that acetoxy group at C-7 and C-8 positions of 9c are responsible for the observed cytotoxic effect in these cancer cell lines.     

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al. 2006, 2007; Oakey et al. 2006; Burgueño et al. 2007; Piepho et al. 2007).The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits. Parametric (e.g., Meuwissen et al. 2001) and semiparametric (e.g., Gianola et al. 2006; Gianola and van Kaam 2008) methods address these problems differently.In standard genetic models, phenotypic outcomes, , are viewed as the sum of a genetic value, , and a model residual, ; that is, . In parametric models for GS, is described as a regression on marker covariates (j = 1,  …  , p molecular markers) of the form , such that(or , in matrix notation), where is the regression of on the jth marker covariate .Estimation of via multiple regression by ordinary least squares (OLS) is not feasible when p > n. A commonly used alternative is to estimate marker effects jointly using penalized methods such as ridge regression (Hoerl and Kennard 1970) or the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani 1996) or their Bayesian counterpart. This approach yields greater accuracy of estimated genetic values and can be coupled with geostatistical techniques commonly used in plant breeding to model multienvironments trials (Piepho 2009).In ridge regression (or its Bayesian counterpart) the extent of shrinkage is homogeneous across markers, which may not be appropriate if some markers are located in regions that are not associated with genetic variance, while markers in other regions may be linked to QTL (Goddard and Hayes 2007). To overcome this limitation, many authors have proposed methods that use marker-specific shrinkage. In a Bayesian setting, this can be implemented using priors of marker effects that are mixtures of scaled-normal densities. Examples of this are methods Bayes A and Bayes B of Meuwissen et al. (2001) and the Bayesian LASSO of Park and Casella (2008).An alternative to parametric regressions is to use semiparametric methods such as reproducing kernel Hilbert spaces (RKHS) regression (Gianola and van Kaam 2008). The Bayesian RKHS regression regards genetic values as random variables coming from a Gaussian process centered at zero and with a (co)variance structure that is proportional to a kernel matrix K (de los Campos et al. 2009b); that is, , where , are vectors of marker genotypes for the ith and jth individuals, respectively, and is a positive definite function evaluated in marker genotypes. In a finite-dimensional setting this amounts to modeling the vector of genetic values, , as multivariate normal; that is, where is a variance parameter. One of the most attractive features of RKHS regression is that the methodology can be used with almost any information set (e.g., covariates, strings, images, graphs). A second advantage is that with RKHS the model is represented in terms of n unknowns, which gives RKHS a great computational advantage relative to some parametric methods, especially when p ≫ n.This study presents an evaluation of several methods for GS, using two extensive data sets. One contains phenotypic records of a series of wheat trials and recently generated genomic data. The other data set pertains to international maize trials in which different traits were measured in maize lines evaluated under severe drought and well-watered conditions.     

Novel thienopyridine derivatives as specific anti-hepatocellular carcinoma (HCC) agents: Synthesis,preliminary structure–activity relationships,and in vitro biological evaluation

Novel thienopyridine derivatives 1b–1r were synthesized, based on a hit compound 1a that was found in a previous cell-based screening of anticancer drugs. Compounds 1a–1r have the following features: (1) their anticancer activity in vitro was first reported by our group. (2) The most potent analog 1g possesses hepatocellular carcinoma (HCC)-specific anticancer activity. It can specifically inhibit the proliferation of the human hepatoma HepG2 cells with an IC₅₀ value of 0.016 μM (compared with doxorubicin as a positive control, whose IC₅₀ was 0.37 μM). It is inactive toward a panel of five different types of human cancer cell lines. (3) Compound 1g remarkably induces G₀/G₁ arrest and apoptosis in HepG2 cells in vitro at low micromolar concentrations. These results, especially the HCC-specific anticancer activity of 1g, suggest their potential in targeted chemotherapy for HCC.     

Prediction of dry-cured ham weight loss and prospects of use in a pig breeding program

Large ham weight losses (WL) in dry-curing are undesired as they lead to a loss of marketable product and penalise the quality of the dry-cured ham. The availability of early predictions of WL may ease the adaptation of the dry-curing process to the characteristics of the thighs and increase the effectiveness of selective breeding in enhancing WL. Aims of this study were (i) to develop Bayesian and Random Forests (RFs) regression models for the prediction of ham WL during dry-curing using on-site infrared spectra of raw ham subcutaneous fat, carcass and raw ham traits as predictors and (ii) to estimate genetic parameters for WL and their predictions (P-WL). Visible-near infrared spectra were collected on the transversal section of the subcutaneous fat of raw hams. Carcass traits were carcass weight, carcass backfat depth, lean meat content and weight of raw hams. Raw ham traits included measures of ham subcutaneous fat depth and linear scores for round shape, subcutaneous fat thickness and marbling of the visible muscles of the thigh. Measures of WL were available for 1672 hams. The best prediction accuracies were those of a Bayesian regression model including the average spectrum, carcass and raw ham traits, with R² values in validation of 0.46, 0.55 and 0.62, for WL at end of salting (23 days), resting (90 days) and curing (12 months), respectively. When WL at salting was used as an additional predictor of total WL, the R² in validation was 0.67. Bayesian regressions were more accurate than RFs models in predicting all the investigated traits. Restricted maximum likelihood (REML) estimates of genetic parameters for WL and P-WL at the end of curing were estimated through a bivariate animal model including 1672 measures of WL and 8819 P-WL records. Results evidenced that the traits are heritable (h² ± SE was 0.27 ± 0.04 for WL and 0.39 ± 0.04 for P-WL), and the additive genetic correlation is positive and high (r_a = 0.88 ± 0.03). Prediction accuracy of ham WL is high enough to envisage a future use of prediction models in identifying batches of hams requiring an adaptation of the processing conditions to optimise results of the manufacturing process. The positive and high genetic correlation detected between WL and P-WL at the end of dry-curing, as well as the estimated heritability for P-WL, suggests that P-WL can be successfully used as an indicator trait of the measured WL in pig breeding programs.     

Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate (FCR) in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model (Bayes A) and a semi-parametric approach (Reproducing kernel Hilbert Spaces regression, RKHS) using all available SNPs (p = 3481) were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs (p = 400) using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set, and predictions were made on 61 birds as testing set, which were sons of sires in the training set. Accuracy of prediction was measured as the Spearman correlation (r¯S) between observed and predicted phenotype, with its confidence interval assessed through a bootstrap approach. A large improvement of genome-assisted prediction (up to an almost 4-fold increase in accuracy) was found relative to pedigree index. Bayes A and RKHS regression were equally accurate (r¯S = 0.27) when all 3481 SNPs were included in the model. However, RKHS with 400 pre-selected informative SNPs was more accurate than Bayes A with all SNPs.     

A two-nucleotide deletion renders the mannose-binding lectin 2 (MBL2) gene nonfunctional in Danish Landrace and Duroc pigs

The mannose-binding lectins (MBLs) are central components of innate immunity, facilitating phagocytosis and inducing the lectin activation pathway of the complement system. Previously, it has been found that certain single-nucleotide polymorphisms (SNPs) in porcine MBL1 and MBL2 (pMBL1, pMBL2) affect mRNA expression, serum concentration, and susceptibility to disease, but the combinatory effect of pMBL1 and pMBL2 genotypes needs further elucidation. In the present study, pMBL1 and pMBL2 alleles, combined pMBL haplotypes, and MBL-A concentration in serum were analyzed in purebred Landrace (N?=?30) and Duroc (N?=?10) pigs. Furthermore, the combined pMBL haplotypes of 89 Piètrain × (Large White × Landrace) crossbred pigs were studied, and the genotypes of 67 crossbreds challenged with Escherichia coli were compared to their individual disease records. In the purebred animals, three non-synonymous SNPs and a two-nucleotide deletion were detected in the coding sequence of pMBL2. The two-nucleotide deletion was present at a frequency of 0.88 in the Landrace pigs and 0.90 in the Duroc pigs, respectively. In the crossbreds, the T allele of the SNP G949T in pMBL1—previously shown to have profound effect on MBL-A concentration even in the heterozygote condition—was detected in 47 % of the animals. Finally, an association was found between low-producing MBL genotypes and low body weight on the day of weaning in the same animals.     

The Relationship Between Serum 25-Hydroxyvitamin D and Glucose Homeostasis in Obese Children and Adolescents in Zhejiang,China

Objective: Evidence of the association between vitamin D, insulin resistance, and oral disposition index (oDI) in obese children and adolescents is limited. To fill this research gap, we measured serum 25-hydroxyvitamin D (25&lsqb;OH]D) levels in obese children and analyzed the relationship between serum 25(OH)D levels and glucose homeostasis.Methods: Altogether, 348 obese and 445 nonobese children and adolescents (age, 6 to 16 years) were enrolled in this study. Obese children were divided into 4 subgroups: normal glucose tolerance (NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and combined IFG and IGT (IFG+IGT) according to oral glucose tolerance test results. We measured serum 25(OH)D levels and calculated the homeostasis model assessment (HOMA) of insulin resistance (IR), the whole-body insulin sensitivity index (WBISI), and the disposition index.Results: The levels of 25(OH)D in the obese group were significantly lower than in the nonobese group; serum 25(OH)D level in the NGT subgroup was higher than those of the other 3 subgroups, and it was significantly inversely correlated with logHOMA-IR (r = -0.090; P = .045) and positively correlated with logWBISI and logHOMA-oDI (r = 0.091, P = .049; and r = 0.108, P = .046, respectively). Obese patients with vitamin D deficiency thus have a significantly higher risk of disturbances in glucose metabolism.Conclusion: 25(OH)D deficiency or insufficiency is quite common in obese children and adolescents in Zhejiang, China. Obese patients with 25(OH)D deficiency (<30 nmol/L) are shown to be at higher risk for abnormal glucose metabolism.Abbreviations: 25(OH)D = 25-hydroxyvitamin D ΔI₃₀/ΔG₃₀ = insulinogenic index BMI = body mass index CI = confidence interval HbA_1c = hemoglobin A_1c HOMA = homeostasis model assessment I_F = fasting insulin IFG = impaired fasting glucose IGT = impaired glucose tolerance IR = insulin resistance NGT = normal glucose tolerance oDI = oral disposition index OGTT = oral glucose tolerance test WBISI = whole-body insulin sensitivity index     

Indirect Genetic Effects for Survival in Domestic Chickens (Gallus gallus) Are Magnified in Crossbred Genotypes and Show a Parent-of-Origin Effect

Through social interactions, individuals can affect one another’s phenotype. The heritable effect of an individual on the phenotype of a conspecific is known as an indirect genetic effect (IGE). Although IGEs can have a substantial impact on heritable variation and response to selection, little is known about the genetic architecture of traits affected by IGEs. We studied IGEs for survival in domestic chickens (Gallus gallus), using data on two purebred lines and their reciprocal cross. Birds were kept in groups of four. Feather pecking and cannibalism caused mortality, as beaks were kept intact. Survival time was shorter in crossbreds than in purebreds, indicating outbreeding depression and the presence of nonadditive genetic effects. IGEs contributed the majority of heritable variation in crossbreds (87 and 72%) and around half of heritable variation in purebreds (65 and 44%). There was no evidence of dominance variance, neither direct nor indirect. Absence of dominance variance in combination with considerable outbreeding depression suggests that survival is affected by many loci. Direct–indirect genetic correlations were moderately to highly negative in crossbreds (−0.37 ± 0.17 and −0.83 ± 0.10), but low and not significantly different from zero in purebreds (0.20 ± 0.21 and −0.28 ± 0.18). Consequently, unlike purebreds, crossbreds would fail to respond positively to mass selection. The direct genetic correlation between both crosses was high (0.95 ± 0.23), whereas the indirect genetic correlation was moderate (0.41 ± 0.26). Thus, for IGEs, it mattered which parental line provided the sire and which provided the dam. This indirect parent-of-origin effect appeared to be paternally transmitted and is probably Z chromosome linked.     

Design,synthesis and activity evaluation of indole-based double – Branched HDAC1 inhibitors

Histone deacetylases inhibitors (HDACIs) represents effective treatments for cancer. In continuing our efforts to develop novel and potent HDACIs, a series of N-hydroxycinnamamide-based HDACIs with aromatic ring and various aliphatic linker have been successfully designed and synthesized. Biological evaluations established that compounds 4h, 4i, 4j, 4l, 4r showed superior inhibition on histone deacetylase and antiproliferative activity in some solid tumor cell lines [HeLa, SK-N-BE(2), PC-3] compared to the known inhibitor SAHA. Among these analogs, 4l exhibited selectivity to HDAC1.     

Genome-Wide Regression and Prediction with the BGLR Statistical Package

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis.