Traditionally, Quantitative Trait Locus (QTL) mapping has been used as a method to understand the genetic control of polygenic traits and can be used to identify QTL in different species, but QTL mapping has limitations, such as the difficulty in establishing segregation in some species. Populations with only one meiotic generation and reduced genetic diversity from only two parents. Association mapping is becoming an important method for identifying quantitative trait loci and finding diagnostic molecular markers associated with complex traits. This method overcomes some of the obstacles of QTL mapping and is an alternative method for direct application in plant breeding. Association mapping, also known as association genetics, association studies, and linkage disequilibrium mapping, is a method of mapping QTL that takes advantage of historic linkage disequilibrium to link phenotypes (observable characteristics) to genotypes (the genetic constitution of organisms), uncovering genetic associations. Association mapping uses several statistical methods and procedures, ranging from fixed models to mixed model analyses, and considers population characteristics such as population structure, kinship, and adaptive parameters to identify reliable markers-trait correlation.
Fig. 1. Schematic comparison of various methods for identifying nucleotide polymorphism trait association in terms of resolution, research time, and allele number. (Kushwaha et al., 2017)
Our association mapping service
CD Genomics focuses on animal and plant scientific research, helping researchers identify and mine important genes for breeding, and accumulate resources for future agricultural precision breeding. We provide high-quality association mapping services to dissect complex agronomic traits and identify alleles that help improve target traits. Based on novel high-throughput genotyping and NGS sequencing technology platforms, our association mapping services are widely used in crops, such as corn, wheat, barley, sorghum, ryegrass, soybean, and rice. We provide reliable association analysis results for predicting the optimal haplotype for one or more genes to achieve optimal expression of a target trait.
Our laboratory has a rigorous system of control over the factors that influence association studies, including the size of the experimental population, the magnitude of the target allele effect, the density of the markers used, the rate of decay of linkage disequilibrium between the markers and the target allele, the error in the phenotyping and genotyping data, and the desired level of statistical significance.
CD Genomics provides the following two types of association mapping. Based on project needs, we will choose the most cost-effective solution for you.
Candidate Gene Association Mapping
Genome Wide Association Mapping
Features
Variation in a gene of interest is tested and correlated with the phenotypic trait of interest.
Genetic variation is explored within the whole genome, aiming to find signals of association with the complex trait.
Advantages
Allow fine mapping, relatively low costs.
Allows untargeted fine mapping
(blind approach), detection of
common alleles.
Disadvantages
Detailed functional knowledge of trait is
required, no novel trait will be found.
Confounding effect due to population structure, will miss rare and weak effect alleles.
General requirements
Large population size, small number of genetic
markers, the bigger the population size, the less replicates needed, Prior genetic and biochemical knowledge on trait of interest, Prior knowledge on LD, nucleotide polymorphism, breeding system, and population structure.
Large population size, many genetic markers, The bigger the population size, the fewer replicates needed, Prior knowledge on LD, nucleotide polymorphism, breeding system, and population structure.
Technical characteristics
Maps quantitative features at high resolution in a statistically very powerful way.
Requires extensive knowledge of SNPs within the genome of the target organism .
Use linkage disequilibrium between markers and related genes/QTLs to identify marker-trait associations.
Association mapping methods can identify markers closer to the gene of interest than traditional linkage mapping.
The resolution of the mapping depends on the degree of linkage disequilibrium occurring throughout the genome.
Allowing the use of historically measured trait data for association, and finally eliminating the need to develop expensive and cumbersome biparental populations, makes the method time and cost effective.
Our services can be applied to the following research areas
Fig. 2. CD Genomics' association mapping service process.
Our advantages and features
Technical expertise. We use state-of-the-art genotyping and sequencing technologies for accurate and high-throughput data collection.
Precision. Our marker-trait association analyses are known for their precision and reliability.
One-stop shop. We provide end-to-end association mapping solutions, from sample collection to data interpretation.
Method integration. We understand the value of integrating association mapping with other gene identification and validation technologies to provide a holistic approach to genetic research.
CD Genomics has extensive experience in association mapping services. Our association mapping service allows for more precise localization of QTL controlling traits of interest, offering great potential to enhance crop genetic improvement. Our high-throughput and low-cost next-generation sequencing technologies provide technical support for association mapping. If you are interested, please feel free to contact us.
Reference
Kushwaha, Ujjawal Kumar Singh, et al. Association mapping, principles and techniques. J. Biol. Environ. Eng 2.1 (2017): 1-9.
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OUR MISSION
CD Genomics is propelling the future of agriculture by employing cutting-edge sequencing and genotyping technologies to predict and enhance multiple complex polygenic traits within breeding populations.
Fig. 1. Schematic comparison of various methods for identifying nucleotide polymorphism trait association in terms of resolution, research time, and allele number. (Kushwaha et al., 2017)
Fig. 2. CD Genomics' association mapping service process.
