Estimating taxonomic and functional structure along a tropical estuary: linking metabolic traits and aspects of ecosystem functioning.

Microbial life forms are among the most ubiquitous on Earth, yet many remain understudied in Caribbean estuaries. We report on the prokaryote community composition of the Urabá Estuary in the Colombian Caribbean using 16S rRNA gene-transcript sequencing. We also assessed potential functional diversity through 38 metabolic traits inferred from 16S rRNA gene data. Water samples were collected from six sampling stations at two depths with contrasting light-penetration conditions along an approximately 100 km transect in the Gulf of Urabá in December 2019. Non-metric multidimensional scaling analysis grouped the samples into two distinct clusters along the transect and between depths. The primary variables influencing the prokaryote community composition were the sampling station, depth, salinity, and dissolved oxygen levels. Twenty percent of genera (i.e., 58 out 285) account for 95% of the differences between groups along the transect and among depths. All of the 38 metabolic traits studied showed some significant relationship with the tested environmental variables, especially salinity and except with temperature. Another non-metric multidimensional scaling analysis, based on community-weighted mean of traits, also grouped the samples in two clusters along the transect and over depth. Biodiversity facets, such as richness, evenness, and redundancy, indicated that environmental variations-stemming from river discharges-introduce an imbalance in functional diversity between surface prokaryote communities closer to the estuary's head and bottom communities closer to the ocean. Our research broadens the use of 16S rRNA gene transcripts beyond mere taxonomic assignments, furthering the field of trait-based prokaryote community ecology in transitional aquatic ecosystems.IMPORTANCEThe resilience of a dynamic ecosystem is directly tied to the ability of its microbes to navigate environmental gradients. This study delves into the changes in prokaryote community composition and functional diversity within the Urabá Estuary (Colombian Caribbean) for the first time. We integrate data from 16S rRNA gene transcripts (taxonomic and functional) with environmental variability to gain an understanding of this under-researched ecosystem using a multi-faceted macroecological framework. We found that significant shifts in prokaryote composition and in primary changes in functional diversity were influenced by physical-chemical fluctuations across the estuary's environmental gradient. Furthermore, we identified a potential disparity in functional diversity. Near-surface communities closer to the estuary's head exhibited differences compared to deeper communities situated farther away. Our research serves as a roadmap for posing new inquiries about the potential functional diversity of prokaryote communities in highly dynamic ecosystems, pushing forward the domain of multi-trait-based prokaryote community ecology.

Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine.

BACKGROUND: Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. RESULTS: MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. CONCLUSIONS: The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle.

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm2), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Genetic evaluation for large data sets by random regression models in Nellore cattle / Avaliação genética para grandes massas de dados por meio de modelos de regressão aleatória em gado Nelore

Expected progeny differences (EPD) of Nellore cattle estimated by random regression model (RRM) and multiple trait model (MTM) were compared. Genetic evaluation data included 3,819,895 records of up nine sequential weights of 963,227 animals measured at ages ranging from one day (birth weight) to 733 days. Traits considered were weights at birth, ten to 110-day old, 102 to 202-day old, 193 to 293-day old, 283 to 383-day old, 376 to 476-day old, 551 to 651-day old, and 633 to 733-day old. Seven data samples were created. Because the parameters estimates biologically were better, two of them were chosen: one with 84,426 records and another with 72,040. Records preadjusted to a fixed age were analyzed by a MTM, which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were carried out by REML, with five traits at a time. The RRM included the effects of age of animal, contemporary group, age of dam class, additive direct, permanent environment, additive maternal, and maternal permanent environment. Different degree of Legendre polynomials were used to describe random effects. MTM estimated covariance components and genetic parameters for weight at birth and sequential weights and RRM for all ages. Due to the fact that correlation among the estimates EPD from MTM and all the tested RM were not equal to 1.0, it is not possible to recommend RRM to genetic evaluation to large data sets.

Compararam-se as diferenças esperadas nas progênies (DEPs) de gado Nelore, estimadas por meio de um modelo de características múltiplas (MTM), com um modelo de regressão aleatória (RRM). Foram utilizados 3.819.895 dados de peso corporal sequenciais para a avaliação genética de 963.227 animais, coletados do nascer aos 733 dias de idade. As características consideradas foram: peso ao nascer e pesos dos 10 aos 110, dos 102 aos 202, dos 193 aos 293, dos 283 aos 383, dos 376 aos 476, dos 467 aos 567, dos 551 aos 651, e dos 633 aos 733 dias. Sete amostras foram geradas. Duas amostras resultaram em estimativas de parâmetros mais consistentes do ponto de vista biológico, sendo, portanto consideradas representativas da população em estudo. A primeira amostra constituiu-se de 84.426 medidas, e a segunda, de 72.040. Os pesos pré-ajustados para as idades fixas foram analisados por meio de um MTM, com cinco características por processamento, no qual se incluíram efeito de grupo contemporâneo, classe de idade da vaca, aditivo direto, aditivo materno e ambiente materno permanente, utilizando-se a metodologia de máxima verossimilhança restrita (REML). Diferentes graus dos polinômios de Legendre foram utilizados em um RRM, para os efeitos aleatórios. As correlações entre as DEPs estimadas por meio do modelo para características múltiplas e de regressão aleatória não foram iguais a 1,0, portanto, não se recomenda a utilização dos modelos de regressão aleatória para avaliação genética para grande massa de dados.