LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms

Abstract

Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

“…(). As bslmm does not accept missing genotypes, these were imputed using LinkImpute (Money et al., ). qtcat analyses also utilized the imputed data set alongside phenotypic data adjusted to May in 2CY.…”

Genomic‐Wide Association Study of Popping Expansion in Tropical Popcorn and Field Corn Germplasm

“…The original dataset was filtered to require a minimum count of 80 called genotypes out of 183 genotyped lines (Supplemental File S1, 43.17% of count) and a 5% minimum allele frequency, resulting in the genotypic dataset used in the GWAS analysis, with 165,089 high‐quality SNPs (working dataset, Supplemental File S2). To impute the missing data in the resulting dataset, LD k ‐nearest‐neighbor imputation (LD‐ k nni) (Money et al, 2015) was used with default parameters in TASSEL 5.2 (Bradbury et al, 2007).…”

Genome-Wide Association Study of Genetic Control of Seed Fatty Acid Biosynthesis in Brassica napus

“…SNPs of the population were imputed using LD-kNNi (Money et al, 2015) as implemented in TASSEL v5.2.30 (Glaubitz et al, 2014). MSTMap (Wu et al, 2008) was used to create a genetic map with the options: Cut_off_ p _value 2, no_map_size 2, no_map_dist 15, missing_threshold 0.25).…”