Spatial heterogeneity analysis for estimating breeding values of tree height in a hybrid larch progeny test plantation

ABSTRACT Microenvironmental heterogeneity in forest stands results in spatial variations in growth traits. Especially in progeny tests in tree breeding, this spatial variation can prevent the accurate estimation of genetic parameters, including breeding values. To quantify spatial heterogeneity, genetic models incorporating spatial coordinate information have been effectively used to accurately estimate breeding values of individuals. In this study, we measured the height of all Japanese larch and hybrid larch individuals in a progeny trial at 1, 2, 5, 10, and 15 years after planting and calculated genetic parameters, including breeding values, using genetic models. According to the fittings of the candidate models, a genetic model incorporating spatial coordinate information is more suitable for estimating genetic parameters. Overall, a genetic model incorporating spatial coordinate information could be effective in explaining the genetic effects of tree height, which will be useful for estimating more accurate breeding values in hybrid larch breeding programs.

[1]  Kazutaka Kusunoki,et al.  Pollen dispersal patterns and male reproductive success based on pedigree reconstruction in a hybrid larch (Larix gmelinii var. japonica × L. kaempferi) open-pollinated seed orchard , 2022, Tree Genetics & Genomes.

[2]  Xuening Chen,et al.  Genome-wide genetic diversity and IBD analysis reveals historic dissemination routes of pear in China , 2021, Tree Genetics & Genomes.

[3]  Xiaomei Sun,et al.  Spatial and competition models increase the progeny testing efficiency of Japanese larch , 2020, Canadian Journal of Forest Research.

[4]  Y. Hiraoka,et al.  Effectiveness of spatial analysis in Cryptomeria japonica D. Don (sugi) forward selection revealed by validation using progeny and clonal tests , 2018, Annals of Forest Science.

[5]  N. Borralho,et al.  Estimation of genetic parameters using spatial analysis of Pinus elliottii Engelm. var. elliottii second-generation progeny trials in Argentina , 2018, New Forests.

[6]  A. Helmersson,et al.  Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden , 2018, Annals of Forest Science.

[7]  Jisen Shi,et al.  Spatial analysis increases efficiency of progeny testing of Chinese fir , 2017, Journal of Forestry Research.

[8]  A. Yanchuk,et al.  Estimation of genetic parameters for height using spatial analysis in Tsuga heterophylla full-sibling family trials in British Columbia , 2015 .

[9]  K. Kita,et al.  Estimated amount of carbon accumulation of hybrid larch in three 31-year-old progeny test plantations , 2009, Journal of Wood Science.

[10]  H. Piepho,et al.  BLUP for phenotypic selection in plant breeding and variety testing , 2008, Euphytica.

[11]  K. Jayawickrama,et al.  Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest , 2008, Tree Genetics & Genomes.

[12]  K. Uchiyama,et al.  Enhanced hybridization rates in a Larix gmelinii var. japonica × L. kaempferi interspecific seed orchard with a single maternal clone revealed by cytoplasmic DNA markers , 2008, Tree Genetics & Genomes.

[13]  Hiroyuki Yamamoto,et al.  Estimation of Wood Stiffness and Strength Properties of Hybrid Larch by Near-Infrared Spectroscopy , 2007, Applied spectroscopy.

[14]  A. Gilmour,et al.  Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials , 2006 .

[15]  R. Zas,et al.  Iterative kriging for removing spatial autocorrelation in analysis of forest genetic trials , 2006, Tree Genetics & Genomes.

[16]  A. Gilmour,et al.  Spatial analysis methods for forest genetic trials , 2002 .

[17]  Gw Dutkowski,et al.  Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual , 2001 .

[18]  G. Namkoong,et al.  Spatial patterns of tree height variations in a series of Douglas-fir progeny trials: implications for genetic testing , 1999 .

[19]  Cavell Brownie,et al.  Estimating Spatial Variation in Analysis of Data from Yield Trials: A Comparison of Methods , 1993 .

[20]  Timothy L. White,et al.  Predicting Breeding Values with Applications in Forest Tree Improvement , 1989, Forestry Sciences.

[21]  Facundo Muñoz,et al.  breedR: Statistical methods for forest genetic resources analysts , 2015 .

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  A. Hamann,et al.  Improving Precision of Breeding Values by Removing Spatially Autocorrelated Variation in Forestry Field Experiments , 2002 .

[24]  D. Joyce,et al.  Spatial Patterns of Tree Height Variations in a Black Spruce Farm-Field Progeny Test and Neighbors-adjusted Estimations of Genetic Parameters , 2002 .

[25]  Walter W. Stroup,et al.  Removing Spatial Variation from Wheat Yield Trials: A Comparison of Methods , 1994 .

[26]  S. T. Friedman,et al.  Handbook of Quantitative Forest Genetics , 1992, Forestry Sciences.

[27]  B. Weir,et al.  Spatial patterns of genetic variation within plant populations. , 1990 .

[28]  A. Kurahashi.  Improvement of larches by species hybridization. , 1988 .

[29]  Dorothy L. Robinson,et al.  Estimation and Use of Variance Components , 1987 .

[30]  J. D. Matthews.  Forest Genetics , 1951, Nature.