jointgenotyping

Joint genotyping refers to the process of inferring the genotypes of multiple individuals simultaneously. This contrasts with single-sample genotyping, where genotypes are determined for each individual independently. Joint genotyping leverages the relatedness and shared genetic variation among individuals in a cohort to improve genotype accuracy. By considering the entire dataset, statistical power is increased, leading to more robust genotype calls, especially for low-quality or missing data points in individual samples.

The fundamental principle behind joint genotyping is that shared variants within a population or family can inform genotype calls for all individuals. For example, if a specific variant is rare and observed in a few individuals, it is more likely to be a true variant than if it were detected in only one individual with uncertain data quality. Algorithms used for joint genotyping often employ models that account for population structure, relatedness, and linkage disequilibrium. This allows them to impute missing genotypes with higher confidence and correct erroneous calls.

Joint genotyping is particularly beneficial in studies involving related individuals, such as family-based association studies, or in large population cohorts where subtle genotyping errors can accumulate and obscure true genetic signals. It is a common practice in human genetics, animal breeding, and plant genetics research to enhance the reliability of downstream analyses like genome-wide association studies (GWAS) and genetic risk prediction. The computational cost of joint genotyping can be higher than single-sample methods, but the gains in accuracy often outweigh this drawback.