Genome-wide Association Study (GWAS)

Platform Overview

Genome-wide association study (GWAS) aims at identifying genetic variants (genotype) that associated with specific traits (phenotype). GWA studies investigates genetic markers cross whole genome of large number of individuals and predicts genotype-phenotype associations by statistical analysis at population level.


Materials for GWAS

Analysis Pipeline


Resequencing-GWAS

Whole-genome resequencing can potentially discover all genetic variants. Coupling with phenotypic data, GWAS can be processed to identify phenotype related SNPs, QTLs and candidate genes, which strongly backs up modern animal/plant breeding.
Bioinformatic analysis

Standard analysis:
1. Raw data quality control
2. Alignment against reference genome
3. Genome-wide variation identification
4. Evolutional genetics analysis
5. GWAS
1)Genotype imputation (lower than 3X) 2)SNP-traits association study 3)Indel-traits association study 4)Candidate gene annotation

Advanced analysis:
1. LD-block analysis on significant
2.Genotype imputation(other methods)
3.Association studies by other models
GWAS

SLAF-GWAS

SLAF is a self-developed simplified genome sequencing strategy, which discovers genome-wide distributed markers, SNP. These SNPs, as molecular genetic markers, can be processed for association studies with targeted traits. It is a cost-effective strategy in identifying complex traits associated genetic variations.
Bioinformatic analysis

Standard analysis:
1. Raw data quality control
2. Genome-wide variation identification
3. Evolutional genetics analysis
4. GWAS
1)SNP-traits association study 2)Candidate gene annotation

Advanced analysis:
1. LD-block analysis on significant
2.Association studies by other models
LD-BLOCK

GWAS with Biomarker Technologies


Biomarker Technologies has accumulated massive experience in GWAS projects, covering hundreds of species. Our technical group has contributed to over 40 publications in high impact journals, such as Nature Communication, Molecular Plant, Plant Biotechnology Journal, The Plant Journal, etc., accumulated IF over 120.
50+ skilled experts in analysis
Diverse successful cases
Highly-experienced technical team
3 distributed computer cluster servers
In-depth data interpretation
Optimized reports delivery
Professional after-sale support
Rapid analysis

Results Demo



LD-decay Analysis

Linkage disequilibrium (LD) refers to non-random association of alleles at different loci in a population. Normalized coefficient of LD, D' or R2 is used to indicate correlation between pairs of loci, i.e. loci pairs with D' or R2 closer to 1 indicates a stronger linkage. LD-decay curve is plot by data with R2>0.1. A shorter LD-decay distance indicates higher genetic diversity.




Genome-wide Association Study

By employing compressed MLM model in TASSEL software, each SNP is given an association value to particular trait (y=Xα+Qβ+Kµ+e). Here, sample population structure (Q) is calculated by admixture and SPAGeDi calculates Kinship (K). X stands for genotype and y stands for phenotype. By plotting the association values of SNP along chromosome, SNPs with strong association will be revealed.


LD-Block Analysis

Haplotype block patterns is predicted based on SNPs by Haploview. The size distribution of these blocks revealed genome-wide genomic sequences that display higher linkage disequilibrium. A smaller block indicates higher level of genetic recombination.
















LD-Block Application

Significantly associated SNPs on Chromosome can be identified by GWAS analysis. Combining with LD-block analysis on corresponding chromosome region, SNPs that have strong linkage with those SNPs can be revealed. Functional annotation on corresponding genes can be obtained.

FAQ

1What are the sample requirements for GWAS analysis?
Samples selected should be representative. Samples should not have obvious subspecies differentiation (e.g. reproductive isolation), as group differentiation may lead to higher background noise in genetic analysis. For qualitative traits, we recommend to choose binary traits, i.e. with traits value 0 and 1. Number of samples with two traits should be as close as possible. For quantitative traits, the values should be recorded as accurate as possible (e.g. For disease resistance studies, record disease incidence, mortality rate, survival rate, lesion area, lesion counts, etc.) The numbers should follow normal distribution. For cultivated plants, traits and sampling can be recorded and processed for multiple points and repeatedly for several years. GWAS analysis can be processed respectively to these recording points. Mean values can be taken from repeated records to process GWAS analysis. For studies with very distinct phenotypes and obvious main controller of traits, at least 200 samples are recommended. For studies with less distinct phenotypes and phenotypes controlled by multiple genes, at least 500 samples are required.
2What's the research objects for natural population GWAS?
1. Genobank 2. Half-sib family or mixed family 3. MAGIC/NAM family 4. Multiple F2/RIL full-sib family 5. Highly heterozygous species: F1 population

Publications with Us

Year

Title

Journal

Impact Fator

2019 

Whole-genome resequencing reveals Brassica napus origin and genetic loci involved in its improvement

Nature communications

12.35 

2018 

Whole-genome resequencing of a world-wide collection of rapeseed accessions reveals genetic basis of their ecotype divergence

Molecular Plant

9.33 

2015 

Domestication footprints anchor genomic regions of agronomic importance in soybeans

New Phytologist

7.43 

2017 

Genome-wideassociationstudydiscoveredcandidategenesofVerticilliumwiltresistanceinuplandcotton(GossypiumhirsutumL.)

Plant Biotechnology Journal

6.31 

2015 

Loci and candidate gene identification for resistance to Sclerotinias clerotiorumin soybean(GlycinemaxL.Merr.)viaassociation and linkage maps

The Plant Journal

5.78 

2018 

Genome-Wide Association Study and Transcriptome Analysis Provide New Insights into the White/Red Earlobe Color Formation in Chicken

Cellular Physiology and Biochemistry

5.50 

2019

Identification of candidate genes controlling oil content by combination of genome-wide association and transcriptome analysis in the oilseed crop Brassica napus

Biotechnology for Biofuels

5.45 

2017 

Earliness traits in rapeseed (Brassica napus): SNP loci and candidate genes identified by genome-wide association analysis

DNA Research

5.42 

2017 

Identification of Major Quantitative Trait Loci for Seed Oil Content in Soybeans by Combining Linkage and Genome-Wide Association Mapping

Frontiers in Plant Science

4.50 

2019 

Identification of Loci and Candidate Genes Responsible for Fiber Length in Upland Cotton (Gossypium hirsutum L.) via Association Mapping and Linkage Analyses

Frontiers in Plant Science

4.47 

2018 

Identification of the Genomic Region Underlying Seed Weight per Plant in Soybean (Glycine max L. Merr.) via High-Throughput Single-Nucleotide Polymorphisms and a Genome-Wide Association Study

Frontiers in Plant Science

4.47 

2018 

Multi-Locus Genome-Wide Association Studies of Fiber-Quality Related Traits in Chinese Early-Maturity Upland Cotton

Frontiers in Plant Science

4.47 

2018 

Genome-Wide Association Mapping for Cold Tolerance in a Core Collection of Rice (Oryza sativa L.) Landraces by Using High-Density Single Nucleotide Polymorphism Markers From Specific-Locus Amplified Fragment Sequencing

Frontiers in Plant Science

4.47 

2019 

Analysis of Drought Tolerance and Associated Traits in Upland Cotton at the Seedling Stage

Int. J. Mol. Sci

4.18

2019 

Identifcation of favorable SNP alleles and candidate genes responsible for inflorescence-related traits via GWAS in chrysanthemum

Plant Molecular Biology

3.93 

2015 

Genetic characteristics of soybean resistance to HG type 0 and HG type 1.2.3.5.7 of the cyst nematode analyzed by genome-wide association mapping

BMC Genomics

3.87 

2019 

Combined genome-wide association analysis and transcriptome sequencing to identify candidate genes for flax seed fatty acid metabolism

Plant Science

3.79 

2017 

Loci and candidate genes conferring resistance to soybean cyst nematode HG type 2.5.7

BMC Genomics

3.73 

2017 

Genome-Wide Association Study Identifying Candidate Genes Influencing Important Agronomic Traits of Flax (Linum usitatissimum L.) Using SLAF-seq

Frontiers in Plant Science

3.68

2016 

Detectionof Favorable QTL Alleles and Candidate Genes for Lint Percentage by GWAS in Chinese Upland Cotton

Frontiers in Plant Science

3.68 

2018 

Genome-wide association study discovered favorable single nucleotide polymorphisms and candidate genes associated with ramet number in ramie (Boehmeria nivea L.)

BMC Plant Biology

3.67