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Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder’s equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F 2:4 bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125–0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied. Electronic supplementary material The online version of this article (10.1007/s00122-019-03327-y) contains supplementary material, which is available to authorized users.

To investigate factors related to glycemic management among members of a professional cycling team with type 1 diabetes over a 7-day Union Cycliste Internationale World Tour stage race. RESEARCH DESIGN AND METHODS An observational evaluation of possible factors related to glycemic management and performance in six male professional cyclists with type 1 diabetes (HbA 1c 6.4 6 0.6%) during the 2019 Tour of California. RESULTS In-ride time spent in euglycemia (3.9-10.0 mmol/L glucose) was 63 6 11%, with a low percentage of time spent in level 1 (3.0-3.9 mmol/L; 0 6 1% of time) and level 2 (<3.0 mmol/L; 0 6 0% of time) hypoglycemia over the 7-day race. Riders spent 25 6 9% of time in level 1 (10.1-13.9 mmol/L) and 11 6 9% in level 2 (>13.9 mmol/L) hyperglycemia during races. Bolus insulin use was uncommon during races, despite high carbohydrate intake (76 6 23 g • h 21). Overnight, the riders spent progressively more time in hypoglycemia from day 1 (6 6 12% in level 1 and 0 6 0% in level 2) to day 7 (12 6 12% in level 1 and 2 6 4% in level 2) (x 2 [1] > 4.78, P < 0.05). CONCLUSIONS Professional cyclists with type 1 diabetes have excellent in-race glycemia, but significant hypoglycemia during recovery overnight, throughout a 7-day stage race. Athletes with type 1 diabetes have considerable challenges with glycemic control, particularly around training and competition (1). Despite these challenges, the Team Novo Nordisk (TNN) professional athletes compete in elite cycling stage races around the world. This study investigated the glycemic control and performance metrics of TNN athletes over a 7-day Union Cycliste Internationale World Tour stage race. RESEARCH DESIGN AND METHODS Six riders from TNN (mean 6 SD age 29 6 3 years; duration of type 1 diabetes 13 6 7 years; body mass 70.0 6 5.3 kg; HbA 1c 6.4 6 0.6%; _ VO 2max 72.2 6 5.0 mL z kg 21 z min 21 peak power 426 6 36 W) cycled between 3 and 7 h and covered 128-219 km on each of the 7 days of the Tour of California (Table 1). Each rider was equipped with a mobile power meter (Pioneer, Aliso Viejo, CA), a G6 continuous glucose monitor (Dexcom, San Diego, CA), and a Wahoo cycle

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IntroductionThis prospective observational study sought to establish the glycemic, physiological and dietary demands of strenuous exercise training as part of a 9-day performance camp in a professional cycling team with type 1 diabetes (T1D).Research design and methodsSixteen male professional cyclists with T1D on multiple daily injections (age: 27±4 years; duration of T1D: 11±5 years; body mass index: 22±2 kg/m2; glycated hemoglobin: 7%±1% (50±6 mmol/mol); maximum rate of oxygen consumption: 73±4 mL/kg/min) performed road cycle sessions (50%–90% of the anaerobic threshold, duration 1–6 hours) over 9 consecutive days. Glycemic (Dexcom G6), nutrition and physiological data were collected throughout. Glycemic data were stratified into predefined glycemic ranges and mapped alongside exercise physiology and nutritional parameters, as well as split into daytime and night-time phases for comparative analysis. Data were assessed by means of analysis of variance and paired t-tests. A p value of ≤0.05 (two-tailed) was statistically significant.ResultsHigher levels of antecedent hypoglycemia in the nocturnal hours were associated with greater time spent in next-day hypoglycemia overall (p=0.003) and during exercise (p=0.019). Occurrence of nocturnal hypoglycemia was associated with over three times the risk of next-day hypoglycemia (p<0.001) and a twofold risk of low glucose during cycling (p<0.001). Moreover, there was trend for a greater amount of time spent in mild hypoglycemia during the night compared with daytime hours (p=0.080).ConclusionThe higher prevalence of nocturnal hypoglycemia was associated with an increased risk of next-day hypoglycemia, which extended to cycle training sessions. These data highlight the potential need for additional prebed carbohydrates and/or insulin dose reduction strategies around exercise training in professional cyclists with T1D.Trial registration numberDRKS00019923.