Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance

Differences in body temperature regulation during heat stress and seasonal depression in milk yield between Holstein, Brown Swiss, and crossbred cows

It is not clear whether cattle that are genetically superior in regulation of body temperature during heat stress are also better able to sustain milk production during hot conditions. Objectives were to evaluate differences in body temperature regulation during heat stress between Holstein, Brown Swiss, and crossbred cows under semi-tropical conditions and test whether the seasonal depression in milk yield was greater for genetic groups less able to regulate body temperature. For the first objective, conducted during heat stress, vaginal temperature was measured at 15-min intervals for 5 d in 133 pregnant lactating cows. Vaginal temperatures were affected by time and interaction between genetic group and time. Vaginal temperatures were higher for Holsteins for most times of the day. Moreover, the maximum daily vaginal temperature was higher for Holstein (39.8 ± 0.1°C) than for Brown Swiss (39.3 ± 0.2°C) or crossbreds (39.2 ± 0.1°C). For the second objective, 6,179 lactation records from 2,976 cows were analyzed to determine effects of genetic group and season of calving (cool season = Oct to March; warm season = April to Sept) on 305-d milk yield. Milk yield was affected by genetic group and season but not by the interaction of genetic group and season. The difference in average 305-d milk yield between cows calving in cool versus hot weather was 310 kg (4% decrease) for Holstein, 480 kg (7% decrease) for Brown Swiss, and 420 kg (6% decrease) for crossbreds. In conclusion, Brown Swiss and crossbreds regulated body temperature during heat stress better than Holsteins but these breeds were not more resistant to heat stress with respect to milk yield. Thus, genetic differences in thermotolerance are likely to exist that are independent of regulation of body temperature.

Improving Genomic Selection for Heat Tolerance in Dairy Cattle: Current Opportunities and Future Directions

Heat tolerance is the ability of an animal to maintain production and reproduction levels under hot and humid conditions and is now a trait of economic relevance in dairy systems worldwide because of an escalating warming climate. The Australian dairy population is one of the excellent study models for enhancing our understanding of the biology of heat tolerance because they are predominantly kept outdoors on pastures where they experience direct effects of weather elements (e.g., solar radiation). In this article, we focus on evidence from recent studies in Australia that leveraged large a dataset [∼40,000 animals with phenotypes and 15 million whole-genome sequence variants] to elucidate the genetic basis of thermal stress as a critical part of the strategy to breed cattle adapted to warmer environments. Genotype-by-environment interaction (i.e., G × E) due to temperature and humidity variation is increasing, meaning animals are becoming less adapted (i.e., more sensitive) to changing environments. There are opportunities to reverse this trend and accelerate adaptation to warming climate by 1) selecting robust or heat-resilient animals and 2) including resilience indicators in breeding goals. Candidate causal variants related to the nervous system and metabolic functions are relevant for heat tolerance and, therefore, key for improving this trait. This could include adding these variants in the custom SNP panels used for routine genomic evaluations or as the basis to design specific agonist or antagonist compounds for lowering core body temperature under heat stress conditions. Indeed, it was encouraging to see that adding prioritized functionally relevant variants into the 50k SNP panel (i.e., the industry panel used for genomic evaluation in Australia) increased the prediction accuracy of heat tolerance by up to 10% units. This gain in accuracy is critical because genetic improvement has a linear relationship with prediction accuracy. Overall, while this article used data mainly from Australia, this could benefit other countries that aim to develop breeding values for heat tolerance, considering that the warming climate is becoming a topical issue worldwide.

Utilizing evolutionary conservation to detect deleterious mutations and improve genomic prediction in cassava

Introduction

Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression.

Methods

To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava.

Results

Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction.

Conclusion

We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones.