Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data

Despite the widespread use of genotype imputation tools and the availability of different approaches, late developments of currently used programs have not been compared comprehensively. We therefore assessed the performance of 35 combinations of phasing and imputation programs, including versions of SHAPEIT, Eagle, Beagle, minimac, PBWT, and IMPUTE, for genetic imputation of completely missing SNPs with a HRC reference panel regarding quality and speed. We used a data set comprising 1,149 fully sequenced individuals from the German population, subsetting the SNPs to approximate the Illumina Infinium-Omni5 array. Five hundred fifty-three thousand two hundred and thirty-four SNPs across two selected chromosomes were utilized for comparison between imputed and sequenced genotypes. We found that all tested programs with the exception of PBWT impute genotypes with very high accuracy (mean error rate < 0.005). PBTW hardly ever imputes the less frequent allele correctly (mean concordance for genotypes including the minor allele <0.0002). For all programs, imputation accuracy drops for rare alleles with a frequency <0.05. Even though overall concordance is high, concordance drops with genotype probability, indicating that low genotype probabilities are rare. The mean concordance of SNPs with a genotype probability <95% drops below 0.9, at which point disregarding imputed genotypes might prove favorable. For fast and accurate imputation, a combination of Eagle2.4.1 using a reference panel for phasing and Beagle5.1 for imputation performs best. Replacing Beagle5.1 with minimac3, minimac4, Beagle4.1, or IMPUTE4 results in a small gain in accuracy at a high cost of speed.

Spatial Areas of Genotype Probability (SPAG): predicting the spatial distribution of adaptive genetic variants under future climatic conditions

In a context of rapid global change, one of the key components for the survival of species is their genetic adaptive potential. Many methods have been developed to identify adaptive genetic variants, but few tools were made available to integrate this knowledge into conservation management. We present here the SPatial Areas of Genotype probability (SPAG), using genotype-environment logistic associations to map the probability of finding beneficial variants in a study area. We define a univariate model predicting the spatial distribution of a single genotype, and three multivariate models allowing the integration of several genotypes, potentially associated with various environmental variables. We then integrate climate change projections to map the corresponding future distribution of genotypes. The analysis of the mismatch between current and future SPAGs makes it possible to identify a) populations that are better adapted to the future climate through the presence of genetic variants able to cope with future conditions, and b) vulnerable populations where genotype(s) of interest are not frequent enough for the individuals to adapt to the future climate. We validate the SPAG approach using simulations and we use it to study the potential adaptation of 161 Moroccan and 382 European goats to the bioclimatic conditions. In Morocco, using whole genome sequence data, we identify seven genomic regions strongly associated with the precipitation seasonality (WorldClim database). The predicted shift in SPAGs under a strong climate change scenario for 2070 highlights goat populations likely to be threatened by the expected increase in precipitation variation in the future. In Europe, we find genomic regions associated with low precipitation, the shift in SPAGs highlighting vulnerable populations not adapted to the very dry conditions expected in 2070. The SPAG methodology is successfully validated using cross-validations and provides an efficient tool to take the adaptive potential into account in general conservation frameworks.

Fitting the Balding-Nichols model to forensic databases

Large forensic databases provide an opportunity to compare observed empirical rates of genotype matching with those expected under forensic genetic models. A number of researchers have taken advantage of this opportunity to validate some forensic genetic approaches, particularly to ensure that estimated rates of genotype matching between unrelated individuals are indeed slight overestimates of those observed. However, these studies have also revealed systematic error trends in genotype probability estimates. In this analysis, we investigate these error trends and show how they result from inappropriate implementation of the Balding-Nichols model in the context of database-wide matching. Specifically, we show that in addition to accounting for increased allelic matching between individuals with recent shared ancestry, studies must account for relatively decreased allelic matching between individuals with more ancient shared ancestry.

Application of Difference Equations Model in Determining Genotype Probability Offspring with Two Different Characteristic

Population genetics is a branch of biology which studies about the gene composition from population and the change of the gene composition is effect from some factors. One of them is lethal gene factor. The change of gene composition will influence the genotype probabilities in the population. In this paper discussed about determining the genotype for the probability of the n-th offspring genotypes in dihybrid mating by observing linkage between the two loci. The mating occurred randomly and without concern ethics in mating. This research was done by making mathematics model to determine allele pair, using difference equation, then from this model will be determined genotypes probability. The result show that the mating happened normally had the same genotype probability of each generation, meanwhile in abnormal mating, the genotype probability whose had lethal gene would decrease and the genotype probability whose did not have lethal gene would increase in each generation.Keywords : Difference equation, dihybrid mating, lethal gene, population genetics, probability