Association and linkage mapping to unravel genetic architecture of phenological traits and lateral bearing in Persian walnut (Juglans regia L.)

Abstract

Background: Unravelling the genetic architecture of agronomic traits in walnut such as budbreak date and bearing habit, is crucial for climate change adaptation and yield improvement. A Genome-Wide Association Study (GWAS) using multi-locus models was conducted in a panel of 170 walnut accessions genotyped using the Axiom™ J. regia 700 K SNP array, with phenological data from 2018, 2019 and legacy data. These accessions come from the INRAE walnut germplasm collection which is the result of important prospecting work performed in many countries around the world. In parallel, an F 1 progeny of 78 individuals segregating for phenology-related traits, was genotyped with the same array and phenotyped for the same traits, to construct linkage maps and perform Quantitative Trait Loci (QTLs) detection.Results: Using GWAS, we found strong associations of SNPs located at the beginning of chromosome 1 with both budbreak and female flowering dates. These findings were supported by QTLs detected in the same genomic region. Highly significant associated SNPs were also detected using GWAS for heterodichogamy and lateral bearing habit, both on chromosome 11. We developed a Kompetitive Allele Specific PCR (KASP) marker for budbreak date in walnut, and validated it using plant material from the Walnut Improvement Program of the University of California, Davis, demonstrating its effectiveness for marker-assisted selection in Persian walnut. We found several candidate genes involved in flowering events in walnut, including a gene related to heterodichogamy encoding a sugar catabolism enzyme and a cell division related gene linked to female flowering date. Conclusions: This study enhances knowledge of the genetic architecture of important agronomic traits related to male and female flowering processes and lateral bearing in walnut. The new marker available for budbreak date, one of the most important traits for good fruiting, will facilitate the selection and development of new walnut cultivars suitable for specific climates.

Genome-Wide Association Study Reveals Candidate Genes Involved in Fruit Trait Variation in Persian Walnut (Juglans regia L.)

“…The INRAE walnut germplasm collection is publicly available and the GWAS panel is made of 170 unique J. regia accessions previously used to study phenological traits and lateral bearing genetic architecture ( Bernard et al, 2020b ). All the accessions are located in the Fruit Experimental Unit of the INRAE-Bordeaux research center, at Toulenne located 50 km south-west from Bordeaux, France.…”

“…The accessions were genotyped using the Axiom TM J. regia 700K SNP array containing 609,658 SNPs uniformly distributed over the 16 J. regia chromosomes ( Marrano et al, 2019a ). These SNPs were then filtered through several criteria described previously ( Bernard et al, 2020b ). Finally, 364,275 robust SNPs were retained for the GWAS.…”

“…The density plots ( Figure 2) showed a normal distribution for all traits, except for "x3 extreme groups" trait. Using a BIC, we defined that the best number of principal components to include in the models for accounting for structure was zero, as previously found for phenological traits (Bernard et al, 2020b). We also determined the best K as K = 2 with a group consisting of the accessions coming from Western Europe and America and the second with the accessions from Eastern Europe and Asia (Bernard et al, 2020b).…”

Table_1.XLSX

“…The population structure was investigated using the "fastSTRUCTURE" software and the most likely K was determined using the K method (Bernard et al, 2020b). The identity-by-descent (IBD) proportions between all pairwise comparisons are already described in Bernard et al (2020b).…”

“…GWAS was carried out using the R package "GAPIT" (Lipka et al, 2012). Two multi-locus models were tested using the BLUPs as phenotypic data: the Multi-Locus Mixed Model (MLMM) (Segura et al, 2012) and the Fixed and random model Circulating Probability Unification method (FarmCPU) (Liu et al, 2016), as already described (Bernard et al, 2020b). Familiar relatedness was accounted for using a kinship matrix estimated with the VanRaden algorithm implemented in GAPIT.…”