Breeder's Equation

Genomic Selection Method Under Development

We are currently developing advance yet simple methods for complex traits to increase GENOMIC PREDICTION predictive ability significantly.

Proof of concept

Below plot represent correlation between original phenotypic data and various predicted values. For polygenic complex disease traits, we are able to achieve cross validation correlation coefficient of ~ 0.95 and validation correlation coefficient of 0.58. 

Pedigree-Based (PBA) and GWAS Analysis 

Pedigree-based and GWAS analysis are two powerful methods for identifying genetic variants associated with complex diseases. Pedigree-based analysis uses information about the inheritance of traits within families to identify genes that are likely to be involved in the disease. GWAS analysis uses information about the genetic variation of a large group of individuals to identify genes that are associated with the disease. 

Pedigree-based analysis is more powerful than GWAS analysis when the disease is caused by a single gene. However, GWAS analysis is more powerful when the disease is caused by multiple genes. GWAS analysis is also more efficient than pedigree-based analysis, as it does not require information about the family relationships of the study participants. 

Pedigree-based and GWAS analysis are complementary methods that can be used together to identify genetic variants associated with complex diseases. By combining the strengths of both methods, researchers can identify genes that are involved in a wide range of diseases.

Causal Haplotype Variant

A causal haplotype variant is a genetic variant that is associated with a phenotype or a disease. To determine the causal haplotype variant, one can use statistical methods such as linkage disequilibrium mapping or haplotype association analysis. These methods compare the frequencies of different haplotypes in cases and controls, and identify the haplotype that has the strongest association with the phenotype or the disease. The causal haplotype variant can then be located within the associated haplotype region by further genotyping or sequencing.

Finding needle in a haystack - our unique and automated approach efficiently determines causal variant and identifies the genomic associated sequences. We can efficiently determine individuals carrying variants for good as well as bad traits.

Epistasis Analysis

Epistasis is a genetic phenomenon in which the effect of one gene is dependent on the presence or absence of mutations in one or more other genes. In other words, the effect of the mutation is dependent on the genetic background in which it appears. Epistatic mutations therefore have different effects on their own than when they occur together. 

There are many different types of epistasis, and they can be classified in a number of ways. One common way to classify epistasis is by the way in which the genes interact. For example, additive epistasis occurs when the effects of two genes add together to produce a phenotype. In contrast, dominant epistasis occurs when the effect of one gene masks the effect of another gene. 

Our proprietary methods have automated the epistasis analysis pipeline without manual Excel work

Marker-Assisted Parent Selection

Marker-assisted parent selection (MAPS) is a breeding strategy that uses genetic markers to identify individuals with desirable traits for use as parents in a breeding program. This can be used to accelerate the breeding process by reducing the number of generations required to achieve the desired trait. MAPS can be used for a variety of traits, including disease resistance, yield, and quality. It is particularly useful for traits that are difficult or time-consuming to measure phenotypically, such as drought tolerance or insect resistance. To use MAPS, breeders first identify genetic markers that are linked to the desired trait. These markers can be used to identify individuals with the desired trait even before the trait is expressed phenotypically. Once these individuals have been identified, they can be used as parents in a breeding program. MAPS has been shown to be an effective way to accelerate the breeding process and improve the efficiency of plant breeding programs. It is a valuable tool for breeders who are looking to develop new varieties with improved traits.

Here are some of the advantages of using MAPS:

• It can accelerate the breeding process by reducing the number of generations required to achieve the desired trait.

• It can increase the efficiency of plant breeding programs by allowing breeders to focus their efforts on individuals with the desired traits.

• It can be used to improve the accuracy of selection by reducing the number of false positives and false negatives.

• It can be used to select for traits that are difficult or time-consuming to measure phenotypically.

Overall, MAPS is a valuable tool for breeders who are looking to develop new varieties with improved traits. It is a powerful technique that can accelerate the breeding process and improve the efficiency of plant breeding programs.

Genomic Selection

Genomic selection (GS) is a breeding method that uses information from an individual's genome to predict their breeding value. This is done by genotyping individuals for a large number of genetic markers, and then using statistical methods to build a model that predicts the phenotype of an individual based on their genotype. The model can then be used to estimate the breeding value of individuals without having to phenotype them.

GS has the potential to significantly improve the efficiency of breeding programs. By using GS, breeders can select individuals for breeding based on their genetic potential, rather than their phenotype. This can lead to faster genetic gains, as well as the ability to select for traits that are difficult or impossible to phenotype.

GS is still a relatively new technology, but it has already been used to improve the breeding of a variety of crops and livestock. In the future, GS is likely to become even more widely used, as the cost of genotyping decreases and the accuracy of GS models improves.

Here are some of the benefits of genomic selection:

• Increased genetic gain: GS can lead to faster genetic gains than traditional breeding methods. This is because GS allows breeders to select individuals for breeding based on their genetic potential, rather than their phenotype.

• Improved efficiency: GS can improve the efficiency of breeding programs by reducing the need to phenotype individuals. This can save time and money.

• Increased precision: GS can improve the precision of breeding programs by allowing breeders to select for traits that are difficult or impossible to phenotype.

• Increased flexibility: GS can increase the flexibility of breeding programs by allowing breeders to select for multiple traits simultaneously.

Overall, genomic selection is a promising new technology that has the potential to significantly improve the efficiency and precision of breeding programs.

Imputation

SNP marker imputation technology is a powerful tool that can be used to fill in missing genotype data and increase low-density genotype data to genome-wide high-density data. This can be done by using statistical approaches to infer the genotypes of individuals at SNPs that were not genotyped directly. Imputation has been shown to increase the power of the detection of marker-trait associations in GWAS and genomic selection, and it can also be used to accelerate fine-mapping studies and facilitate meta-analysis that combines multiple studies based on different types of marker sets.

Some of the specific applications of SNP marker imputation technology include:

• Genome-wide association studies (GWAS): GWAS are used to identify genetic variants that are associated with complex traits, such as disease. Imputation can be used to increase the power of GWAS by filling in missing genotype data and increasing the number of SNPs that can be analyzed.

• Genomic selection: Genomic selection is a breeding method that uses genetic information to select individuals with desirable traits. Imputation can be used to increase the accuracy of genomic selection by filling in missing genotype data and increasing the number of SNPs that can be used to predict trait values.

• Fine-mapping: Fine-mapping is a process of identifying the specific genetic variants that are responsible for a trait. Imputation can be used to accelerate fine-mapping by identifying candidate variants that are likely to be causal.

• Meta-analysis: Meta-analysis is a statistical method that combines the results of multiple studies to get a more precise estimate of the effect of a genetic variant on a trait. Imputation can be used to facilitate meta-analysis by allowing studies that use different genotyping platforms to be combined.

SNP marker imputation technology is a powerful tool that has a wide range of applications in genetics and breeding. It is a valuable tool for researchers who are studying the genetic basis of complex traits and for breeders who are developing new varieties of crops.