A new approach for efficient genotype imputation using information from relatives

Abstract

BackgroundGenotype imputation can help reduce genotyping costs particularly for implementation of genomic selection. In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging. Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process. A fast, deterministic approach, which makes use of both family and population information, is presented here. All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships. The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows. The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships.ResultsThe proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used. When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used. Rare variants were also imputed with higher accuracy. Finally, computing requirements were considerably lower than those of Beagle and Impute2. The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals.ConclusionsThe proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation. In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical.

Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle

“…FImpute [32] is an efficient, rule-based, and deterministic method for phasing and genotype imputation inspired by ''long range phasing'' [33]. Kong et al [33] reasoned that the length of shared haplotypes reflects the degree of relatedness between two individuals.…”

“…In this review, we attempt to survey and categorize various historical and more recent population-based genotype imputation methods that accept unphased reference panels as input and then evaluate effects of imputed data on feed efficiency genomic predictions for beef cattle. We focus on the most important population-based imputation methods that have been widely adopted and relevant to both human and bovine genomics and their underlying computational schemes for parameter estimations, including Beagle [15], the ''PAC'' model of Li and Stephens [31] and its variants [17], and a simple rule-based method called FImpute [32] inspired by ''long range phasing'' [33]. We also evaluate the impact of genotype imputation accuracy on genomic predictions based on real beef cattle data.…”

Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey

“…Danish Jersey cows were genotyped either with the standard BovineLD BeadChip (6909 SNP, Illumina, Inc.) or with a customized Illumina BovineLD which included the SNP in the standard BovineLD BeadChip and near 5000 user-selected SNP, and a few cows were genotyped with Illumina Bovine SNP50 chip. The marker data of different chips were imputed to Bovine SNP50 chip using FIMPUTE (Sargolzaei et al, 2014). The markers which are not in the Bovine SNP50 chip were excluded.…”

Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection

“…Accuracy of genotype imputation is influenced by several factors; including the number and distribution of markers on the low-density panel, the number of individuals genotyped using the high-density chip (reference population) and their genetic relationships with the animals to be imputed, allele frequencies at the SNP markers and the local LD between each low-density genotype and its surrounding high-density genotypes (Zhang and Druet, 2010;Huang et al, 2011;Hickey et al, 2012). Several studies (Badke et al, 2014;Chen et al, 2014;Sargolzaei et al, 2014) have already been conducted to evaluate the efficiency of SNP genotype imputation under different conditions with special emphasis on the accuracy of imputed genotypes and their impact on the quality of the genomic predictions. However, little attention has been paid to the imputation accuracy and its potential impact on GS after several cycles of selection.…”

“…Several imputation software are already available including fastPHASE (Scheet and Stephens, 2006), MaCH (Li et al, 2010), Beagle (Browning and Browning, 2007) and IMPUTE2 (Howie et al, 2009), which were designed specifically for human populations, using only LD information. AlphaImpute (Hickey et al, 2012), FImpute (Sargolzaei et al, 2011) and findhap (VanRaden et al, 2011), which were developed for animal and plant applications, use pedigree and linkage information. It is worth mentioning that FImpute has an option to impute missing genotypes based on population and/or pedigree information.…”