Single-trait Analyses Research Articles

Methods for meta-analysis of multiple traits using GWAS summary statistics.

Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses. Recently several multivariate methods have been proposed that require individual-level data. Here, we develop metaUSAT (where USAT is unified score-based association test), a novel unified association test of a single genetic variant with multiple traits that uses only summary statistics from existing GWAS. Although the existing methods either perform well when most correlated traits are affected by the genetic variant in the same direction or are powerful when only a few of the correlated traits are associated, metaUSAT is designed to be robust to the association structure of correlated traits. metaUSAT does not require individual-level data and can test genetic associations of categorical and/or continuous traits. One can also use metaUSAT to analyze a single trait over multiple studies, appropriately accounting for overlapping samples, if any. metaUSAT provides an approximate asymptotic P-value for association and is computationally efficient for implementation at a genome-wide level. Simulation experiments show that metaUSAT maintains proper type-I error at low error levels. It has similar and sometimes greater power to detect association across a wide array of scenarios compared to existing methods, which are usually powerful for some specific association scenarios only. When applied to plasma lipids summary data from the METSIM and the T2D-GENES studies, metaUSAT detected genome-wide significant loci beyond the ones identified by univariate analyses. Evidence from larger studies suggest that the variants additionally detected by our test are, indeed, associated with lipid levels in humans. In summary, metaUSAT can provide novel insights into the genetic architecture of a common disease or traits.

Accuracy and simultaneous selection gains for N-stress tolerance and N-use efficiency in maize tropical lines

Maize plants can be N-use efficient or N-stress tolerant. The first have high yields in favorable environments but is drastically affected under stress conditions; whereas the second show satisfactory yields in stressful environments but only moderate ones under optimal conditions. In this context, our aim was to assess the possibility of selecting tropical maize lines that are simultaneously N-stress tolerant and N-use efficient and check for differences between simultaneous selection statistical methods. Sixty-four tropical maize lines were evaluated for Nitrogen Agronomic Efficiency (NAE) and Low Nitrogen Tolerance (LNTI) response indices and two per se selection indices, Low Nitrogen Agronomic Efficiency (LNAE) and Harmonic Mean of Relative Performance (HMRP). We performed eight selection scenarios: LNAE; HMRP; Additive index; Mulamba-Mock index; and Independent culling levels. The last three was predicted by REML/BLUP single-trait and multi-trait using genotypic values of NAE and LNTI. The REML/BLUP multi-trait analysis was superior to the single-trait analysis due to high unfavorable correlation between NAE and LNTI. However, the accuracy and genotypic determination coefficient of NAE and LNTI were too low. Thus, neither single- nor multi-trait analysis achieved a good result for simultaneous selection nor N-use efficiency nor N-stress tolerance. LNAE obtained satisfactorily accurate values and genotypic determination coefficient, but its performance in selection gain was worse than HMRP, particularly in terms of N-use efficiency. Therefore, because of the superior performance in accuracy, genotypic determination coefficient and selection, HMRP was considered the best simultaneous selection methodology of the scenarios tested for N-use efficiency and N-stress tolerance.

Genetic parameters for semen quality and quantity traits in five pig lines.

We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnN), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnN), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (-0.66 ± 0.25), and between PROMOT and ABN (-0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnN, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (-0.60 ± 0.13), PROMOT (-0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

Direct additive, environmental, and phenotypic variances and covariances and coefficients of heritability for underline score in Beefmaster cattle

ABSTRACT The objective of this study was to determine estimates of heritability for underline score (UDLS) and genetic, environmental, and phenotypic correlations (rg, re, rp, respectively) of UDLS with conformation score (CONS), birth weight (BRWT), weaning weight (WNWT), and yearling weight (YRWT) in Beefmaster cattle born between 1976 and 2008. Records provided by Beefmaster Breeders United (n = 49,533) for UDLS and CONS and BRWT (n = 10,698), WNWT (n = 22,503), and YRWT (n = 8,316) were studied. Data were analyzed using single- or 2-trait animal models with DFREML ( Boldman et al., 1993 ) and DMU ( Madsen and Jensen, 2013 ). The mixed model included fixed contemporary group effects constructed using herd, birth year, and sex information. Age of dam and animal age at inspection were included as covariates. Coefficients of heritability were 0.44 ± 0.01, 0.43 ± 0.01, 0.75 ± 0.04, 0.60 ± 0.02, and 0.85 ± 0.06 for UDLS, CONS, BRWT, WNWT, and YRWT, respectively, from the single-trait analyses. From the 2-trait analyses, coefficients of heritability for UDLS with CONS, BRWT, WNWT, and YRWT were 0.43 ± 0.01, 0.35 ± 0.05, 0.39 ± 0.03, and 0.40 ± 0.06, respectively. Coefficient of rg for UDLS with CONS was −0.16 ± 0.02, UDLS with BRWT was 0.02 ± 0.07, ULDS with WNWT was 0.05 ± 0.04, and UDLS with YRWT was 0.13 ± 0.08 from the 2-trait analyses. Mean estimated breeding value of UDLS was 0.00 ± 0.40. These results suggest that artificial selection for UDLS would be effective in beef breeding programs.

GATE: an efficient procedure in study of pleiotropic genetic associations

BackgroundThe association studies on human complex traits are admittedly propitious to identify deleterious genetic markers. Compared to single-trait analyses, multiple-trait analyses can arguably make better use of the information on both traits and markers, and thus improve statistical power of association tests prominently. Principal component analysis (PCA) is a well-known useful tool in multivariate analysis and can be applied to this task. Generally, PCA is first performed on all traits and then a certain number of top principal components (PCs) that explain most of the trait variations are selected to construct the test statistics. However, under some situations, only utilizing these top PCs would lead to a loss of important evidences from discarded PCs and thus makes the capability compromised.MethodsTo overcome this drawback while keeping the advantages of using the top PCs, we propose a group accumulated test evidence (GATE) procedure. By dividing the PCs which is sorted in the descending order according to the corresponding eigenvalues into a few groups, GATE integrates the information of traits at the group level.ResultsSimulation studies demonstrate the superiority of the proposed approach over several existing methods in terms of statistical power. Sometimes, the increase of power can reach 25%. These methods are further illustrated using the Heterogeneous Stock Mice data which is collected from a quantitative genome-wide association study.ConclusionsOverall, GATE provides a powerful test for pleiotropic genetic associations.

Categorical Visual Score Traits of a Nellore Beef Cattle Population

Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h2) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.

Preweaning Calf Survival of a Nellore Beef Cattle Population

The preweaning calf survival (SW) is one of the main economic bottlenecks of beef cattle rearing systems, however there is still few quantitative studies approaching this issue. Being a binary trait, genetic parameters for SW can be estimated considering continuous or categorical data under frequentist and Bayesian methods providing support for the selection and mating of animals in breeding programs. Therefore, the objectives in this study were to obtain and compare the variance component estimates for preweaning calf survival of calves in single-trait analyses and their correlations with a continuous trait in two-trait analyses. An amount of 25 218 data of the categorical trait of calf survival until weaning (SW) and the continuous trait of weaning weight (WW) were collected between the years of 2000 and 2012 in six herds of Nellore cattle. Methods III of Henderson, Maximum Restricted Likelihood (REML), Bayesian Inference and Generalized Linear Mixed Model (GLMM) were tested. Variance components obtained in one-trait analyses were similar to those obtained in two-trait analyses. Estimates of heritability (h2) obtained with different models for SW ranged from 0.0206 to 0.2644. The comparison between different estimation methods in single or two-trait analysis models allows the conclusion that the most appropriate method for SW analysis was the Bayesian estimation under an animal model and assuming linear distribution for phenotypes of SW trait.

Multiple phenotype association tests using summary statistics in genome-wide association studies.

We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.

Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10−8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

Utilization of year-round data in the estimation of genetic parameters for internal parasite resistance traits in Dorper sheep

The objective of this study was to evaluate the effect on the estimates of heritability and permanent environmental effects as a proportion of phenotypic variance for FAMACHA score, fecal worm egg count and hematocrit value when year-round records are used. Records from 1008 Dorper sheep in a private South African flock comprised 17,711 FAMACHA scores, 6837 fecal worm egg counts (FEC; practically only Haemonchus contortus), and 4209 hematocrit (packed cell volume-PCV) values that were collected from 1997 to 2000. Animal models were used to conduct single-trait analyses. Data were analyzed in two sets: 1) warm season records only and 2) year-round records. Treatment (with anthelmintic) status was investigated as a 2-level fixed effect in both sets; additionally records of treated sheep were removed for another analysis of both data sets. In analyses of warm season records, estimates of heritability and permanent environmental variance as a proportion of phenotypic variance for FAMACHA score were 0.33±0.03 and 0.04±0.02, respectively, when treatment status was modeled, and 0.41±0.02 and 0, respectively, when treated records were excluded from the analysis. Heritability estimates for PCV were 0.22±0.06 (treatment status modeled) and 0.28±0.07 (treated records excluded), while permanent environmental variance as a proportion of phenotypic variance was, respectively, 0.13±0.05 and 0.09±0.06. Fecal worm egg count heritability estimates were 0.10±0.03 (treatment status modeled) and 0.13±0.04 (untreated records only). Permanent environmental variance for FEC was 0.04±0.03 when treated records were excluded and 0.05±0.03 when treatment status was included in the analyses. In analyses of year-round records, estimates of heritability and permanent environmental variance as a proportion of phenotypic variance for FAMACHA score were 0.32±0.03 and 0.03±0.02, respectively (treatment status modeled) and 0.36±0.03 and 0.02±0.02 (treated records excluded). Packed cell volume heritability and permanent environmental variance were, respectively, 0.19±0.05 and 0.18±0.05 in the analyses of untreated records, and 0.18±0.04 and 0.15±0.04, when treatment status was modeled. Heritability estimates for FEC were the same (0.11) for untreated records only, and when treated records were included. Permanent environmental variance was 0.04±0.02 (treated records included) and 0.03±0.02 (treated records excluded) for FEC. Collection and inclusion of cool season (that is, outside of the regular worm proliferation season) records in analyses may not substantially change estimates of genetic parameters for these traits.

Genome-wide association study for conformation traits in three Danish pig breeds

BackgroundSelection for sound conformation has been widely used as a primary approach to reduce lameness and leg weakness in pigs. Identification of genomic regions that affect conformation traits would help to improve selection accuracy for these lowly to moderately heritable traits. Our objective was to identify genetic factors that underlie leg and back conformation traits in three Danish pig breeds by performing a genome-wide association study followed by meta-analyses.MethodsData on four conformation traits (front leg, back, hind leg and overall conformation) for three Danish pig breeds (23,898 Landrace, 24,130 Yorkshire and 16,524 Duroc pigs) were used for association analyses. Estimated effects of single nucleotide polymorphisms (SNPs) from single-trait association analyses were combined in two meta-analyses: (1) a within-breed meta-analysis for multiple traits to examine if there are pleiotropic genetic variants within a breed; and (2) an across-breed meta-analysis for a single trait to examine if the same quantitative trait loci (QTL) segregate across breeds. SNP annotation was implemented through Sus scrofa Build 10.2 on Ensembl to search for candidate genes.ResultsAmong the 14, 12 and 13 QTL that were detected in the single-trait association analyses for the three breeds, the most significant SNPs explained 2, 2.3 and 11.4% of genetic variance for back quality in Landrace, overall conformation in Yorkshire and back quality in Duroc, respectively. Several candidate genes for these QTL were also identified, i.e. LRPPRC, WRAP73, VRTN and PPARD likely control conformation traits through the regulation of bone and muscle development, and IGF2BP2, GH1, CCND2 and MSH2 can have an influence through growth-related processes. Meta-analyses not only confirmed many significant SNPs from single-trait analyses with higher significance levels, but also detected several additional associated SNPs and suggested QTL with possible pleiotropic effects.ConclusionsOur results imply that conformation traits are complex and may be partly controlled by genes that are involved in bone and skeleton development, muscle and fat metabolism, and growth processes. A reliable list of QTL and candidate genes was provided that can be used in fine-mapping and marker assisted selection to improve conformation traits in pigs.

Genetic Parameters and Genetic Trends for Reproductive Traits of Santa Ines Sheep Kept in Extensive Environments in Brazil

Studying the Santa Ines sheep breed is important, as it is the most prevalent breed in Brazil and can adapt to many environments. This study included data were collected over a period of 12 years from 33 flocks in 10 Brazilian States. The estimation of variance components, genetic parameters and breeding values were obtained by DFREML method, using lineal mixed model in single-trait analysis. Traits of age at first lambing (AFL), lambing interval (LI), survivability (SU), litter size at birth (LSB), litter size at weaned (LSW),total litter weight at birth (TLWB) and total litter weight at weaning (TLWW) were assessed. The significance of the fixed effects to be included in the model was performed using statistic program R. The effects of breeder and lambing year were significant (P<0.001). A remarkable phenotypic and permanent environmental variability were found in all traits. Direct heritability estimates were 0.13, 0.04, 0.01, 0.12, 0.03, 0.16 and 0.18 for AFL, LI, SU, LSB, LSW, TLWB and TLWW, respectively. The estimated fractions of variance due to the permanent environmental effects in any traits were high magnitude (0.79 to 0.97). The annual genetic gain has a slightly negative trend in all of them except for AFL and SU. Results indicate that change in environmental conditions of flock in addition to the direct selection for TLWB and TLWW, can be used as selection criteria for improving the productivity of this sheep.

Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus).

Genetic parameters for reproductive traits in female Nile tilapia were estimated in this study using Bayesian inference method. The data set presented information from 451 Nile tilapia females that were evaluated at two different places in Maringá - Paraná - Brazil (hapas of 1 and 10 m³) and at one location in Alfenas - Minas Gerais - Brazil. A spawning examination was conducted once a week from October 2012 to March 2013 for a total of 23 weeks of evaluation. Single-trait analyses for spawning success, multiple spawning, spawning frequency, and volume of eggs/female were performed by using the software MTGSAM Threshold. The heritability estimates were 0.14, 0.16, 0.53, and 0.08 for spawning success, multiple spawning, spawning frequency and volume of eggs/female, respectively, indicating it is possible to achieve a substantial genetic gain using these reproductive traits as selection criteria.

Evaluation of body weight and visual scores for genetic improvement of Nelore cattle

The aims of our study were to estimate genetic parameters for body weight and visual scores and to evaluate their inclusion as selection criteria in the Nelore breeding program in Brazil. The traits studied were the body weight adjusted to 210 (W210) and to 450 (W450) days of age and visual scores for body structure, finishing precocity, and muscling evaluated at weaning (BSW, FPW, and MSW) and yearling (BSY, FPY, and MSY) ages. A total of 33,242, 26,259, 23,075, and 26,057 observations were considered to analyze W210, W450, and visual scores at weaning and yearling. The significant (P < 0.05) fixed effects for all traits were farm, birth season, birth year, sex, and management group. Single-trait analyses were performed to define the most fitting model to our data using the average information restricted maximum likelihood algorithm, for weaning traits. Subsequently, these models were used in single- and two-trait analyses considering the Bayesian inference algorithm. Two-trait Bayesian analyses resulted in average direct heritability estimates for BSW, FPW, MSW, W210, BSY, FPY, MSY, and W450 of 0.28, 0.30, 0.27, 0.28, 0.40, 0.44, 0.39, and 0.50, respectively. Genetic correlations varied from 0.40 to 0.96. Benefits to animal performance can best be achieved by considering body structure, finishing precocity, and muscling as selection criteria in the Nelore breeding programs. The decision to use visual scores measured at weaning should be considered in order to decrease generation interval and assist pre-selecting individuals, expecting carcass improvements in the future progeny.

Phenotypic differentiation among cattle populations based on biometric traits

An attempt was made to differentiate 3 cattle populations of Rajasthan state i.e. Kankrej (407), Sanchori (152) and Nari (175) by canonical discriminant analysis (CDA), a multivariate technique using 12 different biometric traits. Single trait analysis variance showed that Sanchori cows had largest size, Kankrej was intermediate and Nari was the smallest. The step-wise discriminant analysis showed that distance between hip bones, heart girth, horn length, face width, ear length, face width, horn diameter, height at withers, tail length without switch, tail length and body length were the most discriminating traits between these three cattle populations. The Mahalanobis distances between these cattle populations indicated that they are three distinct populations. The dendrogram based on the average linkage method showed that there are 2 clusters; cluster one includes Nari and Sanchori cows and cluster two Kankrej cows, clearly separated from cluster one. The individual assignment to different cattle populations by the cross-validation classification approach showed that 100% of the Kankrej cows, 98.08% of Sanchori cows and 98.98% of Nari cows were correctly assigned into their source populations. The present study revealed that the free canonical approach is proved useful and informative in differentiating between 3 cattle populations and suggested that Sanchori and Nari may be established/registers as a distinct breed.

Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals.

BackgroundTwo types of models have been used for single-step genomic prediction and genome-wide association studies that include phenotypes from both genotyped animals and their non-genotyped relatives. The two types are breeding value models (BVM) that fit breeding values explicitly and marker effects models (MEM) that express the breeding values in terms of the effects of observed or imputed genotypes. MEM can accommodate a wider class of analyses, including variable selection or mixture model analyses. The order of the equations that need to be solved and the inverses required in their construction vary widely, and thus the computational effort required depends upon the size of the pedigree, the number of genotyped animals and the number of loci.TheoryWe present computational strategies to avoid storing large, dense blocks of the MME that involve imputed genotypes. Furthermore, we present a hybrid model that fits a MEM for animals with observed genotypes and a BVM for those without genotypes. The hybrid model is computationally attractive for pedigree files containing millions of animals with a large proportion of those being genotyped.ApplicationWe demonstrate the practicality on both the original MEM and the hybrid model using real data with 6,179,960 animals in the pedigree with 4,934,101 phenotypes and 31,453 animals genotyped at 40,214 informative loci. To complete a single-trait analysis on a desk-top computer with four graphics cards required about 3 h using the hybrid model to obtain both preconditioned conjugate gradient solutions and 42,000 Markov chain Monte-Carlo (MCMC) samples of breeding values, which allowed making inferences from posterior means, variances and covariances. The MCMC sampling required one quarter of the effort when the hybrid model was used compared to the published MEM.ConclusionsWe present a hybrid model that fits a MEM for animals with genotypes and a BVM for those without genotypes. Its practicality and considerable reduction in computing effort was demonstrated. This model can readily be extended to accommodate multiple traits, multiple breeds, maternal effects, and additional random effects such as polygenic residual effects.

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape.

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

Evolutionary Models and Phylogenetic Signal Assessment via Mantel Test

Phylogenetically closely related species tend to be more similar to each other than to more distantly related ones, a pattern called phylogenetic signal. Appropriate tests to evaluate the association between phylogenetic relatedness and trait variation among species are employed in a myriad of eco-evolutionary studies. However, most tests available to date are only suitable for datasets describing continuous traits, and are most often applicable only for single trait analysis. The Mantel test is a useful method to measure phylogenetic signal for multiple (continuous, binary and/or categorical) traits. However, the classical Mantel test does not incorporate any evolutionary model (EM) in the analysis. Here, we describe a new analytical procedure, which incorporates explicitly an evolutionary model in the standard Mantel test (EM-Mantel). We run numerical simulations to evaluate its statistical properties, under different combinations of species pool size, trait type and number. Our results showed that EM-Mantel test has appropriate type I error and acceptable power, which increases with the strength of phylogenetic signal and with species pool size but depended on trait type. EM-Mantel test is a good alternative for measuring phylogenetic signal in binary and categorical traits and for datasets with multiple traits.

Parameter estimates for reproductive and carcass traits in Nelore beef cattle

The aim of this study was to estimate genetic parameters for scrotal circumference at 365 (SC365) and 450 (SC450) days of age, age at first calving (AFC), ribeye area (REA), backfat (BF) thickness, and rump fat (RF) thickness, in order to provide information on potential traits for Nelore cattle breeding program. Genetic parameters were estimated using the Average Information Restricted Maximum Likelihood method in single- and multitrait analyses. Four different animal models were tested for SC365, SC450, REA, BF, and RF in single-trait analyses. For SC365 and SC450, the maternal genetic effect was statistically significant (P < 0.01) and was included for multitrait analyses. The direct heritability estimates for SC365, SC450, AFC, REA, BF, and RF were equal to 0.31, 0.38, 0.24, 0.32, 0.16, and 0.19, respectively. Maternal heritability for SC365 and SC450 was equal to 0.06 and 0.08, respectively. The highest genetic correlations were found among the scrotal circumferences. Testing for the inclusion of maternal effects in genetic parameters estimation for scrotal circumference should be evaluated in the Nelore breeding program, mostly for correctly ranking the animal's estimated breeding values. Similar heritability estimates were observed for scrotal circumference, as well as favorable genetic correlations of this trait with AFC and carcass traits. Thus, scrotal circumference measured at 365 days of age could be a target trait for consideration in the Nelore selection index in order to improve most of the traits herein analyzed.

Genetic parameters for carcass traits and body size of meat sheep

The present study aimed to estimate variance components and heritability coefficients (h2) in Santa Inês sheep by single- and two-trait analyses for the traits loin eye area (LEA), length (LEL) and maximum depth (LED), in the longissimus dorsi muscle, subcutaneous fat thickness (SFT), fat thickness over the biceps femoris muscle (FTBF), croup height (CH), thoracic circumference (TC), thoracic depth (TD), body length (BL), and adult live weight (ALW). The restricted maximum likelihood (REML) was used to estimate variance components in an animal model. In both types of analyses, we estimated moderate heritability for all traits, with the exception of SFT, CH, TC, BL, and ALW (all of which yielded low h2 estimates). In two-trait analysis for body size, only TD yielded a lower h2 estimate, when compared to single-trait analysis. On the other hand, CH, TC, and BL yielded higher h2 estimates. Most estimates for variance components and h2 in two-trait analysis were higher than those in single-trait analysis. This finding suggests the possibility of a moderate response to selection for improvement in the carcass of meat sheep using the specific carcass traits evaluated (except SFT), highlighting LEA. The fat thickness over the biceps femoris muscle showed higher potential for response to direct selection for fat deposition than SFT. Thoracic depth showed higher potential for response to direct selection for body size than the other morphometric measurements. In the present study, adult live weight was not an effective criterion for direct selection.