Comparing modeling methods of genomic prediction for growth traits of a tropical timber species, Shorea macrophylla

IntroductionShorea macrophylla is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to S. macrophylla.MethodsTo build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of S. macrophylla in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for diameter at breast height and total height in the 7th year from planting (D7 and H7).Results and discussionThese traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 6 methods and their derivatives with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability for D7 and H7 with the sets of selected SNPs and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high predictive abilities for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. Predictive abilities were higher in H7 when smaller number of SNP subsets selected by GWAS p-value was used, However, D7 showed the contrary tendency, which might have originated from the difference in genetic architecture between primary and secondary growth of the species. This study suggests that GS with GWAS-based SNP selection can be used in breeding for non-cultivated tree species to improve initial growth and reduce genotyping costs for next-generation seedlings

Growth traits of a tropical timber species at Southeast Asia, Shorea macrophylla, and scripts for genome wide association study and genomic prediction

<p><em><span>Shorea macrophylla</span></em><span> is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to <em>S. macrophylla</em>. To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of <em>S. macrophylla</em> in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for breast height and total height in the 7th year from planting (D7 and H7). These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 12 methods with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability (PA) for D7 and H7 and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high PA for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. For almost all methods and both traits, PA was higher when SNPs were selected based on their GWAS P-values than when using the full set of SNPs. These results suggest that GS with GWAS-based SNP selection could be used in <em>S. macrophylla </em>breeding to improve initial growth and reduce genotyping costs for next generation seedlings.</span></p><p>Funding provided by: Japan International Research Center for Agricultural Sciences<br>Crossref Funder Registry ID: https://ror.org/005pdtr14<br>Award Number: a1A401b</p><p>Funding provided by: Japan Science and Technology Agency<br>Crossref Funder Registry ID: https://ror.org/00097mb19<br>Award Number: JPMJSA2101</p><p>CSV file named as "Pheno_raw.csv"</p> <p>This CSV file shows the measured diameter at breast height (from D1 to D12.5) and tree height (from H1 to H12.5) data of 940 Shorea macrophylla progenies which are initially planted in the field. </p> <p>CSV file named as "Pheno_adujusted_rm.csv"</p> <p>This CSV file shows the corrected data of diameter at breast height (from D1 to D12.5) and tree height (from H1 to H12.5) data of 360 individuals of the Shorea macrophylla progeny trial from year 1 to 12.5 years after transplanting. The units for diameter at breast height are in centimeters and the units for tree height are in meters. This data has already been corrected by spatial structure analysis.</p&gt

Growth traits of a tropical timber species at Southeast Asia, Shorea macrophylla, and scripts for genome wide association study and genomic prediction

<p><em><span>Shorea macrophylla</span></em><span> is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to <em>S. macrophylla</em>. To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of <em>S. macrophylla</em> in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for breast height and total height in the 7th year from planting (D7 and H7). These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 12 methods with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability (PA) for D7 and H7 and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high PA for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. For almost all methods and both traits, PA was higher when SNPs were selected based on their GWAS P-values than when using the full set of SNPs. These results suggest that GS with GWAS-based SNP selection could be used in <em>S. macrophylla </em>breeding to improve initial growth and reduce genotyping costs for next generation seedlings.</span></p><p>Funding provided by: Japan International Research Center for Agricultural Sciences<br>Crossref Funder Registry ID: https://ror.org/005pdtr14<br>Award Number: a1A401b</p><p>Funding provided by: Japan Science and Technology Agency<br>Crossref Funder Registry ID: https://ror.org/00097mb19<br>Award Number: JPMJSA2101</p><p>CSV file named as "Pheno_raw.csv"</p> <p>This CSV file shows the measured diameter at breast height (from D1 to D12.5) and tree height (from H1 to H12.5) data of 940 Shorea macrophylla progenies which are initially planted in the field. </p> <p>CSV file named as "Pheno_adujusted_rm.csv"</p> <p>This CSV file shows the corrected data of diameter at breast height (from D1 to D12.5) and tree height (from H1 to H12.5) data of 360 individuals of the Shorea macrophylla progeny trial from year 1 to 12.5 years after transplanting. The units for diameter at breast height are in centimeters and the units for tree height are in meters. This data has already been corrected by spatial structure analysis.</p&gt