Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle

Abstract

Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments.

Coherent synthesis of genomic associations with phenotypes and home environments

“…Recent advances in association models have included explicit modeling of categorical G×E (Murcray et al 2009;Thomas 2010;Korte et al 2012;Marigorta & Gibson 2014;Li et al 2014;Kooperberg et al 2016;Windle 2016), but to our knowledge there are no published genome-wide association studies accounting for genotype interactions with 570 continuous environmental gradients (a reaction norm approach, cf. Jarquín et al 2014;Tiezzi et al 2017). By employing a reaction norm approach to G×E (as we did here), models can be applied to prediction at new sites, which is not possible using categorical, correlated trait approaches to G×E (Falconer 1952;Korte et al 2012) where sites are treated as idiosyncratic.…”

“…Existing approaches to genome-wide association studies (GWAS) with G×E (sometimes referred to as genome-wide interaction studies, GWIS) have dealt with categorical nominal environments (Murcray et al 2009;Thomas 2010;125 Korte et al 2012;Gauderman et al 2013;Marigorta & Gibson 2014), benefiting from the statistical convenience of modeling phenotypes in different environments as correlated traits (Falconer 1952). Association models have not been applied to G×E along continuous environmental gradients, such as modeling SNP associations with reaction norms (Jarquín et al 2014;Tiezzi et al 2017). Despite the existence of studies where 130 fitness was measured in multiple common gardens for diverse genotyped accessions (Fournier-Level et al 2011a), studies where linkage mapping was conducted for fitness at multiple sites (Ågren et al 2013), and studies where authors conducted association mapping for G×E effects on phenotypes (Li et al 2014), we found no example of association studies of G×E for fitness, which is the basis of local adaptation.…”

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

“…Genotypes can vary in their response to measurable and non-measurable environmental factors, called macro-and micro-environmental sensitivity, respectively (Mulder, Rönnegård, Fikse, Veerkamp, & Strandberg, 2013). Genetic variation in macro-environmental sensitivity exists for instance in response to temperature (see Carabaño et al, 2017 for a review), seasonality (Sevillano, Mulder, Rashidi, Mathur, & Knol, 2016), disease outbreaks (Herrero-Medrano et al, 2015;Mathur et al, 2014;Rashidi, Mulder, Mathur, Arendonk, & Knol, 2014) or management level of the farm (Calus, Groen, & Jong, 2002;Hammami et al, 2009;Kolmodin, Strandberg, Jorjani, & Danell, 2003;Tiezzi, Campos, Parker Gaddis, & Maltecca, 2017). Such macro-environmental factors affect the whole farm and can be studied using reaction norms (Falconer, 1990;Lynch & Walsh, 1998).…”

Spatial modelling improves genetic evaluation in smallholder breeding programs

“…Accuracy was expectedly low in this extreme setting, though surprisingly not very low (see the next sub-section on possible reasons). These scenarios might seem too extreme, but they are a reflection of real situation in many countries around the world (e.g., Lawrence et al, 2015) Spatial modelling has a long tradition and has been used before in animal breeding (e.g., Saebø and Frigessi, 2004;Tiezzi et al, 2017). We have used it in the extreme scenario of small herds and for this reason choose to use the geostatistical approach to use all the available information.…”

“…Genotype-by-environment interactions have been modelled in several studies (Strandberg et al, 2009;Hayes et al, 2009;Tiezzi et al, 2017;Yao et al, 2017;Schultz and Weigel, 2019) and such interactions are likely substantial in smallholder systems, in particular when native and exotic breeds are used (Ojango et al, 2019). We ignored these interactions in our study.…”