The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.

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The biggest challenge for jatropha breeding is to identify superior genotypes that present high seed yield and seed oil content with reduced toxicity levels. Therefore, the objective of this study was to estimate genetic parameters for three important traits (weight of 100 seed, oil seed content, and phorbol ester concentration), and to select superior genotypes to be used as progenitors in jatropha breeding. Additionally, the genotypic values and the genetic parameters estimated under the Bayesian multi-trait approach were used to evaluate different selection indices scenarios of 179 half-sib families. Three different scenarios and economic weights were considered. It was possible to simultaneously reduce toxicity and increase seed oil content and weight of 100 seed by using index selection based on genotypic value estimated by the Bayesian multi-trait approach. Indeed, we identified two families that present these characteristics by evaluating genetic diversity using the Ward clustering method, which suggested nine homogenous clusters. Future researches must integrate the Bayesian multi-trait methods with realized relationship matrix, aiming to build accurate selection indices models.

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RESUMOT he a im of this stu dy wa s to eva lu a te the potentia l risk of monilia sis occu rrence a nd the impa cts of clima te cha nge on this disease in the coming decades, should this pathogen be introduced in Brazil. To this end, climate favora bility ma ps were devised for the oc cu r renc e o f m oni lia sis, bo th for the pr esen t a nd fu t u re tim e. The future scenarios (A2 and B2) focused on the deca des of 2 020, 2 0 5 0 a nd 2 0 8 0 . T hese scena r ios we re obt a ined from six glo ba l clima t e models (GC Ms) ma de a va ila ble by th e third a sse ssment re po rt o f In te rgo ve rn me nt a l Pa ne l on C li ma te Ch a n ge ( IP CC for m on i li a sis in Br a z il , e sp ec i a l l y i n r e g io n s a t h i gh ri sk o f in tro du c tio n o f t ha t pa tho gen . C onside rin g t he glo ba l wa rmi ng scena rios provided by the IPCC, the potentia l risk of monilia sis occurrence in Brazil will be reduced. This decrease is predicted for both futu re scenarios, bu t will occur more sharply in scenario A2. However, there will still be areas with favorable climate conditions for the development of the disease, pa rticu la rly in Bra zil's ma in producing regions. Moreover, pathogen and host alike may undergo a ltera tions du e to climate cha nge, which will a ffect the extent of their impa cts on this pa thosystem.Este traba lho teve como objetivo ava lia r o potencia l risco de ocorrên cia da monil ía se e os im pa ctos da s m u da nça s clim á tica s so bre esta doe nça na s déca das futura s, caso este patógeno seja m introduzida no Brasil. Para tal, elaboraram-se mapas de favorabilidade climática à ocorrência da monilíase no período atu al e fu tu ro. v.38, n.1, p.30-35, 2012. principa lmente em regiões qu e apresentam a lto risco de introdu ção do p a t óg en o. C on si de ra nd o os c en á r io s de a qu ec im en to g lo ba l previsto pelo IPCC, haverá a redu çã o do potencial risco climático de ocorr ência da monilí a se no Bra sil. Ta l redu çã o é pr edita em a mb os c ená r ios futuros, porém ocorrerá de forma mais a centua da a dmitindo-se o cená rio A2 . No enta nto, a inda ha verá á rea s qu e a presenta ra m condições de fa vora bilida de climá tica a o desenvolvimento da doença , principa lmente na s ma iores regiões produtoras do Brasil. Além disso, tanto o patógeno como o hospedeiro poderão sofrer alterações com mudanças climáticas, o que influenciará magnitu de dos seu s impa ctos sobre este pa tossistema .

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Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits.

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