WGPD: Wheat Genotype and Phenotype Database

This database contains genome-wide gene expression levels among 406 bread wheat accessions, 342,987 SNPs, 259,979 gene expression (TPM), 44,979 delimited linkage disequilibrium (LD) blocks, phenotype values of 15 above-ground traits and 5 below-ground traits, the estimated genetic effects of each LD block on a given trait.

What's Inside

342,987

High quality SNPs

259,979

Gene expression

44,979

LD blocks

30

Phenotype

Modules

The database contains six modules that can search for the genetic effects of a given genomic block and the related genomic blocks for a given trait.

Sample info

You can query the sample information and phenotypic values of 406 wheat accessions.

Variation

You can query the SNP information among 406 wheat accessions based on physical intervals or Block ID.

Expression

You can batch query the gene expression levels among the 406 wheat accessions with Gene ID.

Genetic effects

You can query the genetic effects of the LD blocks located in the provided physical interval or Block ID.

Phenotype blocks

You can query the LD blocks that have genetics effects on the provided traits.

Download

You can download the genotype, gene expression, and phenotype data.

About this database

The current release of WGPD was constructed by the laboratory of wheat abiotic stress tolerance at Northwest A&F University. All users are free to query and download the data.

For any bugs/issues/suggestions, please send emails to: Dr. Xiaoming Wang and Peng Zhao (pengzhao@nwafu.edu.cn).

Citation

Database:

Zhao P, Liu Z, Shi X, et al. Modern wheat breeding selection synergistically improves above- and belowground traits. Plant Physiology, 2024.

Genotype, gene expression and below-ground phenotypic data:

Zhao P, Ma X, Zhang R, et al. Integration of genome-wide association study, linkage analysis, and population transcriptome analysis to reveal the TaFMO1-5B modulating seminal root growth in bread wheat.. The Plant Journal, 2023.

Wang X, Zhao P, Guo X, et al. Population Transcriptome and Phenotype Reveal that theRht-D1bContribute a Larger Seedling Roots to Modern Wheat Cultivars. bioRxiv, 2022.

Above-ground phenotypic data:

Liu Z, Zhao P, Lai X, et al. The selection and application of peduncle length QTL QPL_6D.1 in modern wheat (Triticum aestivum L.) breeding.. Theoretical and Applied Genetics, 2023.

Liu Z, Hu Z, Lai X, et al. Multi-environmental population phenotyping suggest the higher risks of wheat Rht-B1b and Rht-D1b cultivars in global warming scenarios. bioRxiv, 2022.

Reference

Some codes in the database refers to https://github.com/YTLogos/BnaSNPDB.

 

Sample information and phenotypic values of 406 wheat accessions


Results:

Download Data

High quality SNPs in 406 wheat accessions


Results:

Download Data

Gene expression (TPM) of 406 wheat accessions in the seeding roots


Results:

Download Data

Genetic effects of genomic linkage blocks


Results:

Download Data

LD blocks that have genetics effects on the provided traits


Results:

Download Data

Genotypic data


The genotypic data contains the raw and imputed SNPs of 406 bread wheat accessions. The sequencing data were mapped to IWGSC RefSeq v1.0.


Download SNPs

Gene expression data


The gene expression data contains the expression level (Transcripts Per Million, TPM) of high and low confidence genes. A total of 406 RNA-Seq samples derived from seeding roots at 14 days after germination were mapped to the IWGSC RefSeq Annotations v1.1 using Kallisto.


Download Gene Expression Data

Above-ground phenotypic data


All accessions were planted in ten environments (E1-E10) at Yangling (34.28'N, 108.07'E, altitude 517 m) and Chongzhou (30.63'N, 103.67'E, altitude 1300 m) of China in 2018-2022. E1, E6, normal sowing at Sichuan province in 2018 and 2020; E2, E4, E7, E9 normal sowing at Shaanxi Province in 2018, 2019, 2020, 2021; E3, E5, E8, E10 late sowing at Shaanxi Province in 2018, 2019, 2020, 2021. Twenty-seven agronomic traits on plant structure, yield, growth period and grain quality components were investigated in ten environments. The traits of plant structure and yield were measured at physiological maturity, each sample was measured thrice.

1.Plant structure traits: Plant height (PH), Peduncle length (PL), Total tiller number (TTN), Productive tiller number (PTN), Flag leaf length (FLL), Flag leaf width (FLW), Flag leaf area (FLA); Flag leaf angle (FLANG);

2.Plant yield traits: Spike length (SL), Total spikelet number (TSN), Fertile spikelet number (FSN), Infertile spikelet ratio (ISR), Spikelet spacing (SS), Kernel number per spike (KNS), Kernel number per spikelet (KPS), Thousand kernel weight (TKW), Total tiller number (TTN), Productive tiller number (PTN), Kernel length (KL), Kernel width (KW), Ratio of kernel length to width (RKLW);

3.Growth period traits: Heading date (HD), Flowering date (FD) and Filling duration (FDU);

4.Phenotype and genotype file sample ID correspondence table

5.Planting date record in ten environments


1.Plant structure traits


E1 E2 E3 E4 E5 E6 E7 E8 E9 E10

2.Plant yield traits


E1 E2 E3 E4 E5 E6 E7 E8 E9 E10

3.Growth period traits


E1 E2 E3 E4 E5 E7 E8 E9 E10

4.Phenotype and genotype file sample ID correspondence table


Download sample ID correspondence table

5.Planting date record in ten environments


Download planting date record

Below-ground phenotypic data


The seeds of 406 bread wheat accessions germinated and grown on water-soaked filter papers in germination boxes under the conditions of 22/16 Celsius day/night (50% relative air humidity) and 16h light (2000 Lux) / 8h dark. Six biological replicates were carried out to obtain robust results.

The phenotypes of root related traits at 14 days after germination (DAG) were measured, including total root length (TRL), root surface (RS), root volume (RV), root diameter (RD) and root fresh root weight (FRW).

The root traits were normalized with a general linear model and the lm function in R (v 3.6.1) to exclude the effects of kernel weight on root-related traits.


1.Root traits


Download root phenotypic data

2.Normalized root traits


Download normalized root phenotypic data


Manual

The database contains six modules that can search for the genetic effects of a given genomic block and the related genomic blocks for a given trait.

Sample info

This module can query the sample information and phenotypic values of 406 wheat accessions.

Input parameter:

Traits: Select traits. If “All” is selected, all phenotypes are displayed.

Germplasm Type: Select the type of germplasms. If “All” is selected, all germplasms are displayed.

Output result:

Output the phenotypic value corresponding to the germplasms.

Variation

This module can query the SNP information among 406 wheat accessions based on physical intervals.

Input parameter:

Region or Block_id: Input physical intervals or Block ID. eg: Input “chr1A:1144621-1162006” or “b000001”. The physical interval must be consistent with the example (chr:start_pos-end_pos).

MAF: Input minimum allele frequency used to filter SNPs.

Output result:

CHROM: The chromosome in which the SNP is located.

POS: Physical location of SNP.

REF: Reference allele.

ALT: Alternative allele.

MAF: Minimum allele frequency of SNPs.

Ann: Annotation of SNPs.

Region: Region where the SNPs is located.

Expression

This module can query the gene expression levels (TPM) among the 406 wheat accessions with Gene ID.

Input parameter:

Gene ID: Input Gene ID. Multiple genes can be input.

Output result:

Output the gene expression (TPM) of 406 wheat accessions in the seeding roots at 14 days after germination.

Genetic effects

This module can query the genetic effects of the LD blocks located in the provided physical interval.

Input parameter:

Region or Block_id: Input physical intervals or Block ID. eg: Input “chr1A:1144621-1162006” or “b000001”. The physical interval must be consistent with the example (chr:start_pos-end_pos).

Traits: Select traits. If “All” is selected, the genetic effects of LD block on all phenotypes are displayed.

Output result:

Block_id: LD block number.

Chr: The chromosome in which the block is located.

Start_Pos: Start physical location of LD blocks.

End_Pos: End physical location of LD blocks.

Selective_sweeps: Whether a LD block locate in selective sweeps. (“Select” / “None” indicates a LD block located in or not in the selective sweeps.)

Type: Type of LD block. The “Above”, “Below”, and “Both” indicate that the relative linkage block has a genetic effect on above-ground traits, below-ground traits and both above- and below-ground traits, respectively.

Markers: The SNPs located in the LD blocks.

The numbers in the phenotype column represent the P-value in the genetic effect estimation.

Phenotype blocks

This module can query the LD blocks that have genetics effects on the provided traits.

Input parameter:

Traits: Select traits.

Type: Select type of LD block. The “Above”, “Below”, and “Both” indicate that the relative linkage block has a genetic effect on above-ground traits, below-ground traits and both above- and below-ground traits, respectively. If “All” is selected, the genetic effects of all LD block are displayed.

Chromosome: Select chromosome. If “All” is selected, the genetic effects of all LD block are displayed.

Start: The start physical location of the search.

End: The end physical location of the search.

P-value: Input P-value used to filter blocks.

Output result:

Traits: Selected traits.

Block_id: LD block number.

Chrom: The chromosome in which the block is located.

Start_Pos: Start physical location of LD blocks.

End_Pos: End physical location of LD blocks.

Selective_sweeps: Whether a LD block locate in selective sweeps. (“Select” / “None” indicates a LD block located in or not in the selective sweeps.)

Type: Type of LD block. The “Above”, “Below”, and “Both” indicate that the relative linkage block has a genetic effect on above-ground traits, below-ground traits and both above- and below-ground traits, respectively.

P-value: P-value of the genetic effect estimation.

Markers: The SNPs located in the LD blocks.

Download

Click the button to download the data.