Advance Genetic Solutions 

Pedigree-based Analysis (PBA)

Pedigree-based quantitative trait loci (QTL) analysis (PBA) is a method for identifying genetic factors that influence phenotypic variation in complex traits. PBA uses information from pedigrees to estimate the effects of QTLs on trait values and to map their locations on the genome. PBA can be applied to various types of pedigrees, such as full-sib, half-sib, or multigenerational families. PBA has several advantages over other QTL mapping methods, such as increased power, reduced false positives, and improved resolution. PBA also allows for the incorporation of environmental and epistatic effects, as well as prior knowledge from other sources.

Marker Segregation Error (MSE)

Mendelian segregation error (MSE) corrections are a type of quality control procedure that can be applied to genetic data. MEs occur when the observed genotype of an individual is incompatible with the expected genotype based on the parental genotypes. MEs can arise from various sources, such as genotyping errors, sample mix-ups, or non-paternity events. ME corrections aim to identify and remove or correct these errors, which can otherwise affect the accuracy and validity of downstream analyses. There are different methods and software tools available for performing ME corrections, depending on the type and format of the data, the availability of parental information, and the desired outcome.

We apply advance methodologies to predict parentage.

Ancestry and Relationship

Ancestry and relationship determination based on SNP markers is a method of genetic analysis that uses variations in DNA sequences called single nucleotide polymorphisms (SNPs) to infer the geographic origin and biological kinship of individuals. SNPs are common and stable genetic markers that occur throughout the genome and can be detected by high-throughput genotyping platforms. By comparing the SNP profiles of different individuals, it is possible to estimate the degree of relatedness and the probability of sharing a common ancestor.

Genetic Linkage Mapping

Genetic linkage mapping is a technique that allows researchers to determine the relative positions of genes on a chromosome. It is based on the observation that genes that are close together on a chromosome tend to be inherited together, while genes that are far apart tend to be separated by recombination events. By analyzing the patterns of inheritance of different genes in a population, it is possible to estimate the distance between them and construct a map of their order and location on the chromosome. This can help to identify genes that are involved in certain traits or diseases, as well as to compare the genomes of different species.

Population Genetics

Population genetics is the study of the genetic variation and evolution of populations. It combines principles from biology, mathematics, and statistics to investigate how natural selection, mutation, gene flow, and genetic drift affect the frequency and distribution of alleles and genotypes in a population over time. Population genetics can help us understand the origin and diversity of species, the effects of environmental factors on genetic adaptation, and the implications of genetic diseases and conservation strategies. 

Some common parameters that are used to describe and quantify population genetic processes are:

- Allele frequency: the proportion of a specific allele in a population.

- Genotype frequency: the proportion of a specific combination of alleles in a population.

- Hardy-Weinberg equilibrium: a state in which allele and genotype frequencies do not change over generations in the absence of       

  evolutionary forces.

- Genetic diversity: a measure of the amount of genetic variation within or among populations.

- Inbreeding coefficient: a measure of how closely related individuals are in a population.

- Effective population size: the size of an idealized population that would have the same rate of genetic drift as the actual population.

- Selection coefficient: a measure of the relative fitness advantage or disadvantage of a genotype or phenotype.

- Migration rate: the rate at which individuals move between populations and exchange genes.

Application of Machine Learning in GS

Genomic selection (GS) is a method of predicting the genetic potential of individuals based on their genome-wide marker data. GS can accelerate the breeding process and increase the genetic gain by reducing the generation interval and increasing the selection accuracy. Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and find complex patterns. ML can be applied in GS to handle large and high-dimensional genomic data sets, and to model nonlinear and interactive effects among genomic regions. ML can also integrate different types of data, such as phenotypic, environmental, epigenetic, and pangenome data, to improve the prediction performance. However, ML also poses some challenges for plant breeders, such as the interpretability, scalability, and robustness of the models. Therefore, careful evaluation and comparison of different ML methods are needed to select the best one for a specific GS problem.

Genomic Prediction

We apply linear mixed models for complex traits like yield and other disease related traits. We implement GBLUP, Bayes B, RKHS and other genetic models using ASReml-R and BGLR packages. We also implement Linux based programs to interconnect various software outputs and estimate BLUPs and predict phenotypes.