Editorial: Genomic selection: Lessons learned and perspectives

Genomic selection (GS) has been one of the most prominent Research Topics in breeding science in the last two decades after the milestone paper by Meuwissen et al. (2001). Its huge potential for increasing the efficiency of breeding programs attracted scientific curiosity and research funding. Many different statistical prediction methods have been tested, and different use cases have been explored. We organized this Research Topic to look both back and forward. The objectives were to review the developments of the last 20 years, to provide a snapshot of current hot topics, and potentially also to define areas on which more (or less) focus should be put in the future, thereby supporting readers with formulating and prioritizing their ideas for future research. Several questions were brought up when organizing this Research Topic including: How did GS change breeding schemes? Which impact did GS have on realized selection gain? What, considering the context of particularities of different crops, may be optimal breeding schemes to leverage the full potential of GS? What has been the impact of and what is the potential of hybrid prediction, statistical epistasis models, deep learning and other methods? What are the long-term effects of GS? Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way

Worldwide selection footprints for drought and heat in bread wheat (Triticum aestivum L.)

Genome–environment Associations (GEA) or Environmental Genome-Wide Association scans (EnvGWAS) have been poorly applied for studying the genomics of adaptive traits in bread wheat landraces (Triticum aestivum L.). We analyzed 990 landraces and seven climatic variables (mean temperature, maximum temperature, precipitation, precipitation seasonality, heat index of mean temperature, heat index of maximum temperature, and drought index) in GEA using the FarmCPU approach with GAPIT. Historical temperature and precipitation values were obtained as monthly averages from 1970 to 2000. Based on 26,064 high-quality SNP loci, landraces were classified into ten subpopulations exhibiting high genetic differentiation. The GEA identified 59 SNPs and nearly 89 protein-encoding genes involved in the response processes to abiotic stress. Genes related to biosynthesis and signaling are mainly mediated by auxins, abscisic acid (ABA), ethylene (ET), salicylic acid (SA), and jasmonates (JA), which are known to operate together in modulation responses to heat stress and drought in plants. In addition, we identified some proteins associated with the response and tolerance to stress by high temperatures, water deficit, and cell wall functions. The results provide candidate regions for selection aimed to improve drought and heat tolerance in bread wheat and provide insights into the genetic mechanisms involved in adaptation to extreme environments

Intracellular acidification reduces l-arginine transport via system y+L but not via system y+/CATs and nitric oxide synthase activity in human umbilical vein endothelial cells

l-Arginine is taken up via the cationic amino acid transporters (system y+/CATs) and system y+L in human umbilical vein endothelial cells (HUVECs). l-Arginine is the substrate for endothelial NO synthase (eNOS) which is activated by intracellular alkalization, but nothing is known regarding modulation of system y+/CATs and system y+L activity, and eNOS activity by the pHi in HUVECs. We studied whether an acidic pHi modulates l-arginine transport and eNOS activity in HUVECs. Cells loaded with a pH-sensitive probe were subjected to 0.1-20 mmol/L NH4Cl pulse assay to generate pHi 7.13-6.55. Before pHi started to recover, l-arginine transport (0-20 or 0-1000 μmol/L, 10 s, 37 °C) in the absence or presence of 200 μmol/L N-ethylmaleimide (NEM) (system y+/CATs inhibitor) or 2 mmol/L l-leucine (systemy+L substrate) was measured. Protein abundance for eNOS and serine1177 or threonine495 phosphorylated eNOS was determined. The results show that intracellular acidification reduced system y+L but not system y+/CATs mediated l-arginine maximal transport capacity due to reduced maximal velocity. Acidic pHi reduced NO synthesis and eNOS serine1177 phosphorylation. Thus, system y+L activity is downregulated by an acidic pHi, a phenomenon that may result in reduced NO synthesis in HUVECs

A linear profit function for economic weights of linear phenotypic selection indices in plant breeding

The profit function (net returns minus costs) allows breeders to derive trait economic weights to predict the net genetic merit (H) using the linear phenotypic selection index (LPSI). Economic weight is the increase in profit achieved by improving a particular trait by one unit and should reflect the market situation and not only preferences or arbitrary values. In maize (Zea mays L.) and wheat (Triticum aestivum) breeding programs, only grain yield has a specific market price, which makes application of a profit function difficult. Assuming the traits’ phenotypic values have multivariate normal distribution, we used the market price of grain yield and its conditional expectation given all the traits of interest to construct a profit function and derive trait economic weights in maize and wheat breeding. Using simulated and real maize and wheat datasets, we validated the profit function by comparing its results with the results obtained from a set of economic weights from the literature. The criteria to validate the function were the estimated values of the LPSI selection response and the correlation between LPSI and H. For our approach, the maize and wheat selection responses were 1,567.13 and 1,291.5, whereas the correlations were .87 and .85, respectively. For the other economic weights, the selection responses were 0.79 and 2.67, whereas the correlations were .58 and .82, respectively. The simulated dataset results were similar. Thus, the profit function is a good option to assign economic weights in plant breeding

Genetic analyses of tropical maize lines under artificial infestation of fall armyworm and foliar diseases under optimum conditions

Development and deployment of high-yielding maize varieties with native resistance to Fall armyworm (FAW), turcicum leaf blight (TLB), and gray leaf spot (GLS) infestation is critical for addressing the food insecurity in sub-Saharan Africa. The objectives of this study were to determine the inheritance of resistance for FAW, identity hybrids which in addition to FAW resistance, also show resistance to TLB and GLS, and investigate the usefulness of models based on general combining ability (GCA) and SNP markers in predicting the performance of new untested hybrids. Half-diallel mating scheme was used to generate 105 F1 hybrids from 15 parents and another 55 F1 hybrids from 11 parents. These were evaluated in two experiments, each with commercial checks in multiple locations under FAW artificial infestation and optimum management in Kenya. Under artificial FAW infestation, significant mean squares among hybrids and hybrids x environment were observed for most traits in both experiments, including at least one of the three assessments carried out for foliar damage caused by FAW. Interaction of GCA x environment and specific combining ability (SCA) x environment interactions were significant for all traits under FAW infestation and optimal conditions. Moderate to high heritability estimates were observed for GY under both management conditions. Correlation between GY and two of the three scorings (one and three weeks after infestation) for foliar damage caused by FAW were negative (-0.27 and -0.38) and significant. Positive and significant correlation (0.84) was observed between FAW-inflicted ear damage and the percentage of rotten ears. We identified many superior-performing hybrids compared to the best commercial checks for both GY and FAW resistance associated traits. Inbred lines CML312, CML567, CML488, DTPYC9-F46-1-2-1-2, CKDHL164288, CKDHL166062, and CLRCY039 had significant and positive GCA for GY (positive) and FAW resistance-associated traits (negative). CML567 was a parent in four of the top ten hybrids under optimum and FAW conditions. Both additive and non-additive gene action were important in the inheritance of FAW resistance. Both GCA and marker-based models showed high correlation with field performance, but marker-based models exhibited considerably higher correlation. The best performing hybrids identified in this study could be used as potential single cross testers in the development of three-way FAW resistance hybrids. Overall, our results provide insights that help breeders to design effective breeding strategies to develop FAW resistant hybrids that are high yielding under FAW and optimum conditions

Mediterranean diet and invasive breast cancer risk in the predimed trial

Trabajo presentado en el X Congreso Internacional de la Dieta Mediterránea, celebrado en Barcelona (España) del 02 al 03 de abril de 2014.[Introduction]: Rates of breast cancer incidence have been rising over the past 3 decades. Dietary factors may play a role in the risk of breast cancer. Some observational cohort studies have suggested that the Mediterranean diet may reduce the risk of breast cancer but no randomized controlled trial had investigated this issue. We aimed to evaluate the effect of two interventions with Mediterranean diet on the primary prevention of breast cancer in a randomized controlled trial. [Methods]: The PREDIMED study (Prevención con Dieta Mediterránea) is a randomized, singleblind, and controlled trial conducted in Spanish primary healthcare centres. Out of 4,282 women recruited (aged 60 to 80 years), 1,478 were assigned to a Mediterranean diet supplemented with extra-virgin olive oil, 1,288 to a Mediterranean diet supplemented with mixed nuts and 1,393 to a control diet (advice to reduce dietary fat). Primary analyses were performed on an intention-to-treat basis. Poisson regression analyses were used to assess the relationship between the nutritional intervention and the incidence of confirmed invasive breast cancer. [Results]: After a median of 4.3 years after randomization, participants in both Mediterranean diet groups (extra-virgin olive oil or nuts) had a 55% relative reduction (95%CI: 9% to 78%) in the risk of invasive breast cancer compared with participants assigned to a control group (with the recommendation to follow a low-fat diet). Observed rates (per 1000 person-years) were 1.14, 1.82 and 2.90 for the Mediterranean diet with extra-virgin olive oil group, the Mediterranean diet supplemented with nuts group and the control group, respectively. The multivariable-adjusted rate ratios versus the control group were 0.34 (95% CI: 0.14 to 0.83) for the Mediterranean diet with extra-virgin olive oil group, and 0.60 (95% CI: 0.26 to 1.35) for the Mediterranean diet supplemented with nuts group. [Conclusions]: This is the first large randomized trial assessing the role of a dietary pattern on breast cancer incidence. Our results suggest that an intervention promoting adherence to the Mediterranean dietary pattern, specially when it is supplemented with extra-virgin olive oil, may contribute to a substantial reduction in the incidence of invasive breast cancer risk in women 60 years and older. However, a longer follow-up of our participants is needed to obtain more precise estimates

Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data

Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative

Predictors of adherence to a Mediterranean-type diet in the PREDIMED trial.

BACKGROUND: Determinants of dietary changes obtained with a nutritional intervention promoting the Mediterranean diet have been rarely evaluated. AIM: To identify predictors of higher success of an intervention aimed to increase adherence to a Mediterranean diet (MeDiet) in individuals at high cardiovascular risk participating in a trial for primary prevention of cardiovascular disease: the PREDIMED (PREvención con DIeta MEDiterránea) trial. Candidate predictors included demographic and socioeconomic characteristics, cardiovascular risk factors, and baseline dietary habits. METHODS: A total of 1,048 asymptomatic subjects aged 55-80 years allocated to the active intervention groups (subjects in the control group were excluded). Participants' characteristics were assessed at baseline among subjects. Dietary changes were evaluated after 12 months. Main outcome measures were: attained changes in five dietary goals: increases in (1) fruit consumption, (2) vegetable consumption, (3) monounsaturated fatty acid (MUFA)/saturated fatty acid (SFA) ratio, and decreases in (4) sweets and pastries consumption, (5) and meat consumption. Univariate and multivariate logistic regression analyses were used to examine associations between the candidate predictors and likelihood of attaining optimum dietary change (improved adherence to a MeDiet). RESULTS: Among men, positive changes toward better compliance with the MeDiet were more frequent among non-diabetics, and among those with worse dietary habits at baseline (higher consumption of meat, higher SFA intake, lower consumption of fruit and vegetables). Among women, marital status (married) and worse baseline dietary habits (high in meats, low in fruits and vegetables) were the strongest predictors of success in improving adherence to the MeDiet. CONCLUSIONS: Some participant characteristics (marital status and baseline dietary habits) could contribute to predicting the likelihood of achieving dietary goals in interventions aimed to improve adherence to a MeDiet, and may be useful for promoting individualized long-term dietary changes and improving the effectiveness of dietary counseling

Longitudinal association of changes in diet with changes in body weight and waist circumference in subjects at high cardiovascular risk: the PREDIMED trial

Background: Consumption of certain foods is associated with long-term weight gains and abdominal fat accumulation in healthy, middle-aged and young, non-obese participants. Whether the same foods might be associated with changes in adiposity in elderly population at high cardiovascular risk is less known. Objective: Using yearly repeated measurements of both food habits and adiposity parameters, we aimed to investigate how changes in the consumption of specific foods were associated with concurrent changes in weight or waist circumference (WC) in the PREDIMED trial. Design: We followed-up 7009 participants aged 55-70 years at high cardiovascular risk for a median time of 4.8 years. A validated 137-item semi-quantitative Food Frequency Questionnaire was used for dietary assessment with yearly repeated measurements. We longitudinally assessed associations between yearly changes in food consumption (serving/d) and concurrent changes in weight (kg) or WC (cm). Results: Yearly increments in weight were observed with increased consumption (kg per each additional increase in 1 serving/d) for refined grains (0.32 kg/serving/d), red meat (0.24), potatoes (0.23), alcoholic beverages (0.18), processed meat (0.15), white bread (0.07) and sweets (0.04); whereas inverse associations were detected for increased consumption of low-fat yogurt (- 0.18), and low-fat milk (- 0.06). Annual WC gain (cm per each additional increase in 1 serving/d) occurred with increased consumption of snacks, fast-foods and pre-prepared dishes (0.28), processed meat (0.18), alcoholic beverages (0.13), and sweets (0.08); whereas increased consumption of vegetables (- 0.23), and nuts (- 0.17), were associated with reductions in WC. Conclusions: In this assessment conducted in high-risk subjects using yearly repeated measurements of food habits and adiposity, some ultra-processed foods, refined carbohydrates (including white bread), potatoes, red meats and alcohol were associated with higher weight and WC gain, whereas increases in consumption of low-fat dairy products and plant foods were associated with less gain in weight and WC

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected