Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden

Abstract

Genotype-by-environment (G × E) interaction for tree height measured at ages 7 to 13 was investigated in 20 large open-pollinated progeny trials for Norway spruce (Picea abies (L.) H. Karst.) in southern and central Sweden. Factor analytic method using spatially adjusted data and a reduced animal model was used to explore the pattern of G × E interaction. Extended factor analyses captured 93.0% of additive G × E interaction variances using three factors. The mean daily temperature less than 3.2°C in May and June explained 27.8% G × E interaction, and it was moderately correlated with the first factor, indicating that spring or autumn frost weather condition could be a main driver for G × E interaction in Norway spruce. Cluster analysis has divided 20 trials into either 6 clusters or 3 clusters. Both sets of clusters reflected the geography of the trials (climates) and the genetic connectedness among testing series, indicating that more trials with better connectedness are required to examine whether current delineation of breeding or seed zones is optimal. Parental stability using latent regression could be used to locate best parents that have the highest breeding values and are highly stable across trials.

Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

“…In Norway spruce, we have used spatial analyses as a first-stage analyses for individual sites, and adjusted data have been used for genotype-bysite (G×E) interaction study to dissect the patterns and causes of significant G×E (Chen et al 2017). Here, we summarized genetic parameters, autocorrelation coefficients, accuracy of breeding value predictions for parental and offspring, and genetic gain for nine types of trait classes from base and spatial models in this study of Norway spruce in Sweden.…”

“…The large physical area needed for a progeny trial usually exhibits considerable variation in environmental conditions (Bian et al 2017;Dutkowski et al 2006;Chen et al 2017). To reduce such environmental heterogeneity, an experimental design subdividing the trial into blocks is usually used (Williams et al 2002).…”

“…Spatial analysis can capture both local gradient variations within blocks (patches) and a global gradient trend along the row and column of the trial, so it is becoming a popular method to use for both agricultural and forestry field trials (Anekonda and Libby 1996;Brownie and Gumpertz 1997;Cullis et al 1998;Cullis and Gleeson 1989;Cullis and Gleeson 1991;Fox et al 2007a, b;Gilmour et al 1997;Qiao et al 2000;Yang et al 2004;Ye and Jayawickrama 2008;Chen et al 2017). In forestry, there are several spatial analysis methods used, such as post-blocking (Dutkowski et al 2002;Ericsson 1997), nearest-neighbor adjustment (Anekonda and Libby 1996;Joyce et al 2002;Wright 1978), and kriging (Hamann et al 2002;Zas 2006).…”

Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application

“…The FA models outlined above are equivalent to the extended factor analytic models specified by Meyer (2009). Latent regression plots were used to show genetic responses to trial loadings, indicating the magnitude of G×E (or stability) of selection candidates across multiple environments in the FA models (Chen et al 2017;Cullis et al 2014;Smith et al 2015; Table 1). A latent regression of a selection candidate with a higher slope means that the candidate is more sensitive to the environment.…”

“…Karst. ; Chen et al 2017). Despite its statistical power, the FA approach has not yet delivered in identifying the roles of specific environmental factors in driving G×E (B.…”