DOI: 10.5061/dryad.kkwh70s8d
title: Growth traits of a tropical timber species at Southeast Asia, Shorea macrophylla, and scripts for genome wide association study and genomic prediction

description: Shorea 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. To 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 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 S. macrophylla breeding to improve initial
 growth and reduce genotyping costs for next generation seedlings.
description: CSV file named as "Pheno_raw.csv"
 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. 
 CSV file named as "Pheno_adujusted_rm.csv"
 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.
description: # Growth traits of a tropical timber species at Southeast Asia, Shorea
 macrophylla, and scripts for genome wide association study (GWAS) and
 genomic prediction (GP) ## Description of the data and file structure CSV
 file named as "Pheno_raw.csv" 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. CSV file named as "Pheno_adujusted_rm.csv"
 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. The missing values are shown as
 "n/a". No blank cells are included in the two CSV files. The
 missing values are not used for the scripts in 04: Spatial analysis and
 data correction, exclusion of outlier data. In the 04 analysis, these
 phenotypic data adjusted for the subsequent analyses in 05-07 (See
 Code/Software section). If The codes in 04 is not properly worked, please
 replace "n/a" to "NA". ## Code/Software The following
 scripts were used for the analyses, which were written using bash, R and
 python (shown by extension). In our analyses components were subdivided as
 follows and the scripts were classified by the left two digit number of
 the file name. 01: Filtering 02: Principle Coordinate Analysis (PCA) 03:
 Linkage disequilibrium (LD) 04: Spatial analysis and data correction,
 exclusion of outlier data 05: Narrow-sense genomic heritability 06:
 Genome-wide association study (GWAS) 07: Genomic prediction model
 0101_dDocent_filters_v2.9.4_mod.sh
 0102_remove_alone_snps_on_each_scaffold.R 0103_extract_estimated_nes.R
 0201_thin_vcf.sh 0202_convert_vcf_to_csv.py 0203_pca.R
 0301_calculate_ld.sh 0302_subset_ld_file.R 0303_optimize_loess_span.R
 0304_calculate_ld_trend_line.R 0305_plot_ld_decay.R
 0401_spatial_analysis.R 0402_remove_outlier.R 0501_genomic_heritability.R
 0601_convert_vcf_to_hmpdpl.sh 0602_gwas.R 0603_qq_manhattan_plot.R
 0701_requirements.txt 0702_split_data.py 0703_gwas_training_population.R
 0704_gblup.R 0705_bglr.R 0706_rf.py 0707_xgb.py 0708_lgb.py 0709_cnn1d.py
 0710_convert_vcf_to_netCDF.py 0711_cnn2d.py