Reference

rrBLUP (rrBLUP [R-Package])

Endelman,J.B. (2011) Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP. Plant Genome, 4, 250–255.

GBLUP (sommer [R-Package])

Covarrubias-Pazaran,G. (2016) Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer. PLoS ONE, 11, e0156744.

Bayes (hibayes [R-Package])

Yin,L., Zhang,H., Li,X., Zhao,S. and Liu,X. (2022) hibayes: An R Package to Fit Individual-Level, Summary-Level and Single-Step Bayesian Regression Models for Genomic Prediction and Genome-Wide Association Studies. bioRxiv, 10.1101/2022.02.12.480230.

LightGBM (lightgbm [Python-package])

Yan,J., Xu,Y., Cheng,Q., Jiang,S., Wang,Q., Xiao,Y., Ma,C., Yan,J. and Wang,X. (2021) LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biol, 22, 271.

Training Population

ID	GSTP008
Species	Rice
Sample type	Inbred Line
Sample number	705
SNP number	7,048,490
Traits	18
Paper	Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions. https://pubmed.ncbi.nlm.nih.gov/32303966/

User submitted samples

_static_gstool_task_TPP000210_demo_Rice_705Inbred

Sample	Calling SNP ratio
Rice1	94.85%
Rice2	98.49%
Rice3	96.28%
Rice4	97.86%
Rice5	96.06%
Rice6	95.72%

Note:
* Calling SNP ratio: the ratio of the total number of SNPs in [ the sample mapped to the reference dataset ] / [ the total number of SNPs in the reference dataset ]

Plant Height (cm) -- Best Linear Unbiased Prediction

Models

Model	Prediction accuracy	MSE	RMSE
rrBLUP	0.763	52.46	7.14
GBLUP	0.768	52.09	7.13
BayesCpi	0.768	50.79	7.08
BayesL	0.770	49.64	7.00
BayesR	0.766	51.25	7.11
LightGBM	0.756	52.65	7.26

Prediction

Sample	rrBLUP		GBLUP		BayesCpi		BayesL		BayesR		LightGBM
Sample	Prediction	Pred/Max	Prediction	Pred/Max	Prediction	Pred/Max	Prediction	Pred/Max	Prediction	Pred/Max	Prediction	Pred/Max
Rice1	93.14	69.32%	91.89	68.38%	92.00	68.47%	93.21	69.36%	92.42	68.78%	97.99	72.92%
Rice2	91.39	68.01%	91.82	68.33%	90.86	67.62%	91.77	68.30%	91.29	67.94%	87.09	64.81%
Rice3	96.58	71.87%	96.53	71.84%	97.23	72.36%	95.37	70.98%	95.87	71.35%	95.63	71.17%
Rice4	91.49	68.09%	92.16	68.59%	91.26	67.92%	91.42	68.04%	89.52	66.62%	92.36	68.73%
Rice5	108.81	80.98%	109.98	81.85%	109.83	81.74%	110.29	82.08%	107.87	80.28%	106.07	78.93%
Rice6	99.33	73.92%	98.46	73.28%	100.62	74.88%	98.86	73.57%	99.29	73.89%	100.07	74.47%

LeadSNP

The lead SNPs for GWAS significant signals based on our CropGS-Hub GWAS database are also listed below for users who are interested in traditional marker-assisted selection (MAS)

SNPid	Chromosome	Position	Ref	Alt	P value	Genetic Effect (Alt - Ref)
Chr1_22261818	Chr1	22261818	T	C	6.9007e-07	2.2805
Chr1_24388159	Chr1	24388159	T	A	7.9088e-07	3.6184
Chr1_32879690	Chr1	32879690	G	A	6.0629e-08	5.9446
Chr1_33071815	Chr1	33071815	T	G	1.1346e-07	4.4474
Chr1_40399153	Chr1	40399153	G	T	1.2742e-06	3.9945
Chr2_7378326	Chr2	7378326	T	G	7.6800e-08	-4.1843
Chr2_29822260	Chr2	29822260	G	C	5.3157e-08	4.4923
Chr4_19373249	Chr4	19373249	T	C	2.6256e-07	3.4109
Chr6_10837867	Chr6	10837867	A	C	1.4117e-06	2.8994
Chr6_14068241	Chr6	14068241	A	G	6.6725e-07	-3.2908
Chr6_16023180	Chr6	16023180	C	T	8.5371e-07	-3.1884
Chr6_24817517	Chr6	24817517	T	C	7.9473e-07	4.0076
Chr7_10992926	Chr7	10992926	C	A	1.0543e-06	-5.6017
Chr8_14483606	Chr8	14483606	T	C	1.1864e-06	7.6625
Chr9_993867	Chr9	993867	A	G	1.2481e-06	5.1024
Chr9_4307285	Chr9	4307285	C	T	6.3062e-07	5.4491
Chr9_6905151	Chr9	6905151	T	C	7.3720e-08	4.5672
Chr9_15967723	Chr9	15967723	T	C	4.7518e-08	5.3430
Chr10_18175505	Chr10	18175505	A	G	1.3933e-07	6.5712
Chr12_27253752	Chr12	27253752	C	G	5.7311e-08	2.3405

Lead SNPs in this database have been idefined using PLINK. The parameters are set as follows:
--clump-p1 $cutt-off --clump-p2 0.05 --clump-r2 0.1 --clump-kb 1000