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Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used wholegenome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50 k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50 k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IAm) and by individual (IAi) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested, in which some scenarios used different family relationships and others added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes against those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software (VanRaden, 2015). There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sire (S), dam (D), and maternal grandsire genomic data were combined in the reference population. In all scenarios including related individuals in the reference population, IAm and IAi were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called SPB+DLD(where PB indicates purebread and LD indicates low density), similar to the genomic evaluations performed on US crossbred dairy, was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population, and the IAm and IAi were 80.87 ± 0.06% and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario SPB+DLD, where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA.

This study aimed at evaluating the quality of imputation accuracy (IA) by marker (IA m) and by individual (IA i) in US crossbred dairy cattle. Holstein × Jersey crossbreds were used to evaluate IA from a low- (7K) to a medium-density (50K) SNP chip. Crossbred animals, as well as their sires (53), dams (77), and maternal grandsires (63), were all genotyped with a 78K SNP chip. Seven different scenarios of reference populations were tested, in which some scenarios used different family relationships and others added random unrelated purebred and crossbred individuals to those different family relationship scenarios. The same scenarios were tested on Holstein and Jersey purebred animals to compare these outcomes against those attained in crossbred animals. The genotype imputation was performed with findhap (version 4) software (VanRaden, 2015). There were no significant differences in IA results depending on whether the sire of imputed individuals was Holstein and the dam was Jersey, or vice versa. The IA increased significantly with the addition of related individuals in the reference population, from 86.70 ± 0.06% when only sires or dams were included in the reference population to 90.09 ± 0.06% when sire (S), dam (D), and maternal grandsire genomic data were combined in the reference population. In all scenarios including related individuals in the reference population, IA m and IA i were significantly superior in purebred Jersey and Holstein animals than in crossbreds, ranging from 90.75 ± 0.06 to 94.02 ± 0.06%, and from 90.88 ± 0.11 to 94.04 ± 0.10%, respectively. Additionally, a scenario called S PB +D LD (where PB indicates purebread and LD indicates low density), similar to the genomic evaluations performed on US crossbred dairy, was tested. In this scenario, the information from the 5 evaluated breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey) genotyped with a 50K SNP chip and genomic information from the dams genotyped with a 7K SNP chip were combined in the reference population, and the IA m and IA i were 80.87 ± 0.06% and 80.85 ± 0.08%, respectively. Adding randomly nonrelated genotyped individuals in the reference population reduced IA for both purebred and crossbred cows, except for scenario S PB +D LD , where adding crossbreds to the reference population increased IA values. Our findings demonstrate that IA for US Holstein × Jersey crossbred ranged from 85 to 90%, and emphasize the significance of designing and defining the reference population for improved IA. [ABSTRACT FROM AUTHOR]

Sea louse (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge in aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have been proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequence (WGS) data to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. Mowi Genetics AS provided the genotype (50 k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50 k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequence data. The WGS and 50 k SNPs of the challenged population were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based on WGS data.

Genotype imputation using a reference panel that combines high-density array data and publicly available whole genome sequence consortium variant data is potentially a cost-effective method to increase the density of extant lower-density array datasets. In this study, three datasets (two Border Collie; one Italian Spinone) generated using a legacy array (Illumina CanineHD, 173 662 SNPs) were utilised to assess the feasibility and accuracy of this approach and to gather additional evidence for the efficacy of canine genotype imputation. The cosmopolitan reference panels used to impute genotypes comprised dogs of 158 breeds, mixed breed dogs, wolves and Chinese indigenous dogs, as well as breed-specific individuals genotyped using the Axiom Canine HD array. The two Border Collie reference panels comprised 808 individuals including 79 Border Collies and 426 326 or 426 332 SNPs; and the Italian Spinone reference panel comprised 807 individuals including 38 Italian Spinoni and 476 313 SNPs. A high accuracy for imputation was observed, with the lowest accuracy observed for one of the Border Collie datasets (mean R2  = 0.94) and the highest for the Italian Spinone dataset (mean R2  = 0.97). This study's findings demonstrate that imputation of a legacy array study set using a reference panel comprising both breed-specific array data and multi-breed variant data derived from whole genomes is effective and accurate. The process of canine genotype imputation, using the valuable growing resource of publicly available canine genome variant datasets alongside breed-specific data, is described in detail to facilitate and encourage use of this technique in canine genetics.

Genotype imputation is a standard approach used in the field of genetics. It can be used to fill in missing genotypes or to increase genotype density. Accurate imputed genotypes are required for downstream analyses. In this study, the accuracy of whole-genome sequence imputation for Angus beef cattle was examined using two different ways to form the reference panel, a within-breed reference population and a multi breed reference population. A stepwise imputation was conducted by imputing medium-density (50k) genotypes to high-density, and then to the whole genome sequence (WGS). The reference population consisted of animals with WGS information from the 1 000 Bull Genomes project. The within-breed reference panel comprised 396 Angus cattle, while an additional 2 380 Taurine cattle were added to the reference population for the multi breed reference scenario. Imputation accuracies were variant-wise average accuracies from a 10-fold cross-validation and expressed as concordance rates (CR) and Pearson’s correlations (PR). The two imputation scenarios achieved moderate to high imputation accuracies ranging from 0.896 to 0.966 for CR and from 0.779 to 0.834 for PR. The accuracies from two different scenarios were similar, except for PR from WGS imputation, where the within-breed scenario outperformed the multi breed scenario. The result indicated that including a large number of animals from other breeds in the reference panel to impute purebred Angus did not improve the accuracy and may negatively impact the results. In conclusion, the imputed WGS in Angus cattle can be obtained with high accuracy using a within-breed reference panel.

Abstract Background To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line. Results Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent. Conclusions RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.

Context. In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims. This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods. The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10-5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (I) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and Flmpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson's correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results. Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion. The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications. The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle. [ABSTRACT FROM AUTHOR]

Background: To reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line. Results: Genome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent. Conclusions: RADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered. [ABSTRACT FROM AUTHOR]

Genotype imputation is an attractive approach to obtain whole genome sequencing (WGS) data at a low cost. However, the availability of imputed WGS data mainly depended on imputation accuracy. Balancing influencing factors to enhance imputation accuracy is crucial, particularly in aquaculture. In the present study, we downloaded 361 whole genome re-sequencing data of Nile tilapia to construct different reference panels for determining the influence of several key factors on imputation accuracy systematically, including the reference panel type, the haplotype phasing and imputation software, the reference panel size, the key individual selection strategies, and the composition of the combined reference panel. Results showed that the imputation accuracy has no significant difference (P = 0.3) using pre-phasing data obtained from Beagle5, Eagle2, and Shapeit4, but Beagle5 has the highest computational efficiency. But for imputation software, both Beagle5 and Impute5 were more suitable for combined and external reference panels with a large reference size, and Minimac4 was suitable for internal reference panels, especially for a small reference size. Furthermore, it would always improve the imputation accuracy by increasing the size of the reference panel, nevertheless, a larger combined reference size does not necessarily result in a higher imputation accuracy. When the number of external individuals increased from 5 to 250, the average imputation accuracy of the combined reference panel descended from 0.942 to 0.899 for Minimac4, which is always higher than the internal reference panel (0.866). Compared with minimizing the average distance to the closest leaf (ADCL) and randomly selecting individuals (RAN), it always had slightly higher accuracy using maximizing the expected genetic relationship (REL) method to select key individuals to construct an internal reference panel for imputation. However, it has zero or negative growth on imputation accuracy when using selection strategies to select internal or external individuals to construct a combined reference panel for imputation. In conclusion, using a combined reference panel provided greater imputation accuracy, but the optimal genotype imputation strategy needs to balance the actual situation carefully and comprehensively. Our work sheds light on how to design and execute the genotype imputation in aquaculture. • Beagle5 has high computational efficiency without decreasing phasing accuracy. • Minimac4 has the highest imputation accuracy with the combined reference panel. • A larger combined reference panel does not necessarily mean a higher imputation accuracy. • Key individual selection strategy improves the accuracy of the internal reference panel. • The optimal genotype imputation strategy needs to balance the actual situation comprehensively. [ABSTRACT FROM AUTHOR]

Genotype imputation has become an indispensable part of genomic data analysis. In recent years, imputation based on a multi-breed reference population has received more attention, but the relevant studies are scarce in pigs. In this study, we used the Illumina PorcineSNP50 Bead Chip to investigate the variations of imputation accuracy with various influencing factors and compared the imputation performance of four commonly used imputation software programs. The results indicated that imputation accuracy increased as either the validation population marker density, reference population sample size, or minor allele frequency (MAF) increased. However, the imputation accuracy would have a certain extent of decrease when the pig reference population was a mixed group of multiple breeds or lines. Considering both imputation accuracy and running time, Beagle 4.1 and FImpute are excellent choices among the four software packages tested. This work visually presents the impacts of these influencing factors on imputation and provides a reference for formulating reasonable imputation strategies in actual pig breeding.

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. However, despite the favorable points, KNN still imposes undesirable circumstances. KNN suffers from high time complexity, choosing the right k, and different functions. Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. Our GOA is designed to find the best value of k and optimize the imputed value from KNN that maximizes the imputation accuracy. Experimental evaluation for different types of datasets collected from UCI, with various rates of missing values ranging from 10%, 30%, and 50%. Our proposed algorithm has achieved promising results from the experiment conducted, which outperformed other methods, especially in terms of accuracy.

Machine learning methods have been used in genetic studies to build models capable of predicting missing genotypes for both human and animal genetic variations. Genotype imputation is an important process of predicting unknown genotypes. The objective of this study was to investigate the idea of using machine learning as imputation to compare the family-based methods and tried to offer improving the imputation performance in different scenarios. Also, the accuracies of different methods i.e. Support vector Machine; SVM, Random forest; RF are compared. The final population were simulated in the form of different family structures. Therefore, 100 families including one sire with different number of genotyped progenies (2, 3, 4, 5 or 7) were simulated. The number of markers was set to 5000 for whole genome. The sires in families and other scenarios such as, BothParents, sire/dam and one progeny, sire and maternal grandsire were defined to investigate the ability of learning machine algorithm for imputation. The imputation accuracy ranged from 0.78 to 0.99 in different scenarios. Also, least amount of imputation accuracy were achieved for sire and maternal grand sire scenario with both methods. Increasing in number of progenies from 2 to 3 was considerably increased in imputation accuracy (SVM and RF). The imputation of non-genotyped individuals based on parent-offspring trios and close relatives paired is possible. But, the use of child- one parent genotyped, BothParents genotyped and sire and maternal grandsire genotyped, average imputation accuracy would not exceed 85%. While genotyped progenies are the best source of predicted genotypes for ungenotyped individuals and if the number of progeny is more than 4, the imputation accuracy is increased more than 95%. These results confirmed, that the performance of machine learning methods in family of trios has a good accuracy and computational speed, which can be used in estimated breeding value.

Simple Summary: In this study, we evaluated various imputation strategies for the Korean Hanwoo cattle. We observed that a large reference panel consisting of many cattle breeds did not improve the imputation accuracy when compared to a proportionally small purebred Hanwoo reference. This was because the multi-breed reference did not contain animals sufficiently related to the Hanwoo to improve the accuracies and, although not detrimental, in effect, only added to the computational burden of the imputation. Despite the large multi-breed reference, when the Hanwoo were removed from the reference, the imputation accuracies were low. These results suggest additional sequencing efforts are needed for underrepresented breeds, particularly those less genetically related to the main European breeds. This study evaluated the accuracy of sequence imputation in Hanwoo beef cattle using different reference panels: a large multi-breed reference with no Hanwoo (n = 6269), a much smaller Hanwoo purebred reference (n = 88), and both datasets combined (n = 6357). The target animals were 136 cattle both sequenced and genotyped with the Illumina BovineSNP50 v2 (50K). The average imputation accuracy measured by the Pearson correlation (R) was 0.695 with the multi-breed reference, 0.876 with the purebred Hanwoo, and 0.887 with the combined data; the average concordance rates (CR) were 88.16%, 94.49%, and 94.84%, respectively. The accuracy gains from adding a large multi-breed reference of 6269 samples to only 88 Hanwoo was marginal; however, the concordance rate for the heterozygotes decreased from 85% to 82%, and the concordance rate for fixed SNPs in Hanwoo also decreased from 99.98% to 98.73%. Although the multi-breed panel was large, it was not sufficiently representative of the breed for accurate imputation without the Hanwoo animals. Additionally, we evaluated the value of high-density 700K genotypes (n = 991) as an intermediary step in the imputation process. The imputation accuracy differences were negligible between a single-step imputation strategy from 50K directly to sequence and a two-step imputation approach (50K-700K-sequence). We also observed that imputed sequence data can be used as a reference panel for imputation (mean R = 0.9650, mean CR = 98.35%). Finally, we identified 31 poorly imputed genomic regions in the Hanwoo genome and demonstrated that imputation accuracies were particularly lower at the chromosomal ends. [ABSTRACT FROM AUTHOR]

This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain for economically relevant traits. Simulations were used to mimic a 3-breed crossbreeding system that resembled a modern swine breeding scheme. The simulation consisted of three purebred (PB) breeds A, B, and C each with 25 and 425 mating males and females, respectively. Males from A and females from B were crossed to produce AB females (n = 1,000), which were crossed with males from C to produce crossbreds (CB; n = 10,000). The genome consisted of three chromosomes with 300 quantitative trait loci and ~9,000 markers. Lowly heritable reproductive traits were simulated for A, B, and AB (h 2 = 0.2, 0.2, and 0.15, respectively), whereas a moderately heritable carcass trait was simulated for C (h 2 = 0.4). Genetic correlations between reproductive traits in A, B, and AB were moderate (r g = 0.65). The goal trait of the breeding program was AB performance. Selection was practiced for four generations where AB and CB animals were first produced in generations 1 and 2, respectively. Non-genotyped AB dams were imputed using FImpute beginning in generation 2. Genotypes of PB and CB were used for imputation. Imputation strategies differed by three factors: 1) AB progeny genotyped per generation (2, 3, 4, or 6), 2) known or unknown mates of AB dams, and 3) genotyping rate of females from breeds A and B (0% or 100%). PB selection candidates from A and B were selected using estimated breeding values for AB performance, whereas candidates from C were selected by phenotype. Response to selection using imputed genotypes of non-genotyped animals was then compared to the scenarios where true AB genotypes (trueGeno) or no AB genotypes/phenotypes (noGeno) were used in genetic evaluations. The simulation was replicated 20 times. The average increase in genotype concordance between unknown and known sire imputation strategies was 0.22. Genotype concordance increased as the number of genotyped CB increased with little additional gain beyond 9 progeny. When mates of AB were known and more than 4 progeny were genotyped per generation, the phenotypic response in AB did not differ (P > 0.05) from trueGeno yet was greater (P < 0.05) than noGeno. Imputed genotypes of non-genotyped animals can be used to increase performance when 4 or more progeny are genotyped and sire pedigrees of CB animals are known. [ABSTRACT FROM AUTHOR]

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Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the use of modern Machine-Learning algorithms for imputation. This originates from their capability of showing favorable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine-Learning-based methods for both imputation and prediction are used. We see that even a slight decrease in imputation accuracy can seriously affect the prediction accuracy. In addition, we explore imputation performance when using statistical inference procedures in prediction settings, such as the coverage rates of (valid) prediction intervals. Our analysis is based on empirical datasets provided by the UCI Machine Learning repository and an extensive simulation study. [ABSTRACT FROM AUTHOR]

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals. [ABSTRACT FROM AUTHOR]

Abstract Background Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research. Unlike standard imputation approaches, RF-based imputation methods do not assume normality or require specification of parametric models. However, it is still inconclusive how they perform for non-normally distributed data or when there are non-linear relationships or interactions. Methods To examine the effects of these three factors, a variety of datasets were simulated with outcome-dependent missing at random (MAR) covariates, and the performances of the RF-based imputation methods missForest and CALIBERrfimpute were evaluated in comparison with predictive mean matching (PMM). Results Both missForest and CALIBERrfimpute have high predictive accuracy but missForest can produce severely biased regression coefficient estimates and downward biased confidence interval coverages, especially for highly skewed variables in nonlinear models. CALIBERrfimpute typically outperforms missForest when estimating regression coefficients, although its biases are still substantial and can be worse than PMM for logistic regression relationships with interaction. Conclusions RF-based imputation, in particular missForest, should not be indiscriminately recommended as a panacea for imputing missing data, especially when data are highly skewed and/or outcome-dependent MAR. A correct analysis requires a careful critique of the missing data mechanism and the inter-relationships between the variables in the data.

With the availability of the 600K Affymetrix Axiom high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop a low-density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low-density SNP chips in genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line. Two low-density SNP chip designs were compared: an equidistant methodology and a methodology based on linkage disequilibrium. Egg weight, egg shell color, egg shell strength, and albumen height were evaluated with single-step genomic best linear unbiased prediction methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV) obtained with imputed HD genotypes or low-density genotypes were compared with GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on the offspring. A limited reordering of the breeders, selected on a multitrait index, was observed. Spearman correlations between GEBV on HD genotypes and GEBV on low-density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNP. For the genetically closer, top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNP. Without imputation, the correlations remained lower than 0.85 with less than 3K and 16K SNP for equidistant and linkage disequilibrium methodology, respectively. The differences in GEBV correlations between both methodologies were never significant. The conclusions were the same for all studied traits.

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. However, despite the favorable points, KNN still imposes undesirable circumstances. KNN suffers from high time complexity, choosing the right k, and different functions. Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. Our GOA is designed to find the best value of k and optimize the imputed value from KNN that maximizes the imputation accuracy. Experimental evaluation for different types of datasets collected from UCI, with various rates of missing values ranging from 10%, 30%, and 50%. Our proposed algorithm has achieved promising results from the experiment conducted, which outperformed other methods, especially in terms of accuracy. [ABSTRACT FROM AUTHOR]

Context. Genotype imputation is an effective method to increase the number of SNP markers available for an animal and thereby increase the overall power of genome-wide associations and accuracy of genomic predictions. It is also the key to achieve a common set of markers for all individuals when the original genotypes are obtained using multiple genotyping platforms. High accuracy of imputed genotypes is crucial to their utility. Aims. In this study, we propose a method for the construction of a common set of medium density markers for imputation, which relies on keeping as much information as possible. We also investigated the impact of changing marker coordinates on the basis of the new bovine genome assembly, ARS-UCD 1.2, on imputation accuracy. Methods. In total, 49 754 animals with 45 364 single nucleotide polymorphism markers were used in a 10-fold crossvalidation to compare four different imputation scenarios. The four scenarios were based on two alternative designs for the reference datasets. (1) A traditional reference panel that was created using the overlapping SNP from five medium density arrays and (2) a composite reference panel created by combining SNPs across the five arrays. Each of the reference datasets was used to test imputation accuracy when the SNPs were aligned on the basis of two genome assemblies (UMD 3.1 and ARS-UCD 1.2). Key results. Our results showed that a composite reference panel can achieve higher imputation accuracies than does a traditional overlap reference. Incorporating mapping information on the basis of the recent genome build slightly improved the imputation accuracies, especially for lower density chips. Conclusions. Markers with unreliable mapping information and animals with low connectedness to the imputation reference dataset benefited the most from the ARS-UCD 1.2 assembly and composite reference respectively. Implications. The presented method is straightforward and can be used to setup an optimal imputation for accurate inference of genotypes in Australian Angus cattle. [ABSTRACT FROM AUTHOR]

Although several studies have investigated the factors affecting imputation accuracy, most of these studies involved a large number of genotyped animals. Thus, results from these studies cannot be directly applied to small populations, since the population structure affects imputation accuracy. In addition, factors affecting imputation accuracy may also be intensified in small populations. Therefore, we aimed to compare different imputation strategies for the Portuguese Holstein cattle population considering several commercially available single nucleotide polymorphism (SNP) panels in a relatively small number of genotyped animals. Data from 1359 genotyped animals were used to evaluate imputation in 7 different scenarios. In the S1 to S6 scenarios, imputations were performed from LDv1, 50Kv1, 57K, 77K, HDv3 and Ax58K panels to 50Kv2 panel. In these scenarios, the bulls in 50Kv2 were divided into reference (352) and validation (101) populations based on the year of birth. In the S7 scenario, the validation population consisted of 566 cows genotyped with the Ax58K panel with their genotypes masked to LDv1. In general, all sample imputation accuracies were high with correlations ranging from 0.94 to 0.99 and concordance rate ranging from 92.59 to 98.18%. SNP-specific accuracy was consistent with that of sample imputation. S4 (40.32% of SNPs imputed) had higher accuracy than S2 and S3, both with less than 7.59% of SNPs imputed. Most probably, this was due to the high number of imputed SNPs with minor allele frequency (MAF) < 0.05 in S2 and S3 (by 18.43% and 16.06% higher than in S4, respectively). Therefore, for these two scenarios, MAF was more relevant than the panel density. These results suggest that genotype imputation using several commercially available SNP panels is feasible for the Portuguese national genomic evaluation.

This study evaluated the accuracy of sequence imputation in Hanwoo beef cattle using different reference panels: a large multi-breed reference with no Hanwoo (n = 6269), a much smaller Hanwoo purebred reference (n = 88), and both datasets combined (n = 6357). The target animals were 136 cattle both sequenced and genotyped with the Illumina BovineSNP50 v2 (50K). The average imputation accuracy measured by the Pearson correlation (R) was 0.695 with the multi-breed reference, 0.876 with the purebred Hanwoo, and 0.887 with the combined data; the average concordance rates (CR) were 88.16%, 94.49%, and 94.84%, respectively. The accuracy gains from adding a large multi-breed reference of 6269 samples to only 88 Hanwoo was marginal; however, the concordance rate for the heterozygotes decreased from 85% to 82%, and the concordance rate for fixed SNPs in Hanwoo also decreased from 99.98% to 98.73%. Although the multi-breed panel was large, it was not sufficiently representative of the breed for accurate imputation without the Hanwoo animals. Additionally, we evaluated the value of high-density 700K genotypes (n = 991) as an intermediary step in the imputation process. The imputation accuracy differences were negligible between a single-step imputation strategy from 50K directly to sequence and a two-step imputation approach (50K-700K-sequence). We also observed that imputed sequence data can be used as a reference panel for imputation (mean R = 0.9650, mean CR = 98.35%). Finally, we identified 31 poorly imputed genomic regions in the Hanwoo genome and demonstrated that imputation accuracies were particularly lower at the chromosomal ends.

Existing genotype imputation reference panels are mainly derived from European populations, limiting their accuracy in non-European populations. To improve imputation accuracy for Indonesians, the world's fourth most populous country, we combined Whole Genome Sequencing (WGS) data from 227 West Javanese individuals with East Asian data from the 1000 Genomes Project. This created three reference panels: EAS 1KGP3 (EASp), Indonesian (INDp), and a combined panel (EASp+INDp). We also used ten West-Javanese samples with WGS and SNP-typing data for benchmarking. We identified 1.8 million novel single nucleotide variants (SNVs) in the West Javanese population, which, while similar to the East Asians, are distinct from the Central Indonesian Flores population. Adding INDp to the EASp reference panel improved imputation accuracy (R2) from 0.85 to 0.90, and concordance from 87.88% to 91.13%. These findings underscore the importance of including Indonesian genetic data in reference panels, advocating for broader WGS of diverse Indonesian populations to enhance genomic studies., Competing Interests: Declaration of interests The authors declare no competing interests.

Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the use of modern Machine-Learning algorithms for imputation. This originates from their capability of showing favorable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine-Learning-based methods for both imputation and prediction are used. We see that even a slight decrease in imputation accuracy can seriously affect the prediction accuracy. In addition, we explore imputation performance when using statistical inference procedures in prediction settings, such as the coverage rates of (valid) prediction intervals. Our analysis is based on empirical datasets provided by the UCI Machine Learning repository and an extensive simulation study.

Abstract Background The main goal of selection is to achieve genetic gain for a population by choosing the best breeders among a set of selection candidates. Since 2013, the use of a high density genotyping chip (600K Affymetrix® Axiom® HD genotyping array) for chicken has enabled the implementation of genomic selection in layer and broiler breeding, but the genotyping costs remain high for a routine use on a large number of selection candidates. It has thus been deemed interesting to develop a low density genotyping chip that would induce lower costs. In this perspective, various simulation studies have been conducted to find the best way to select a set of SNPs for low density genotyping of two laying hen lines. Results To design low density SNP chips, two methodologies, based on equidistance (EQ) or on linkage disequilibrium (LD) were compared. Imputation accuracy was assessed as the mean correlation between true and imputed genotypes. The results showed correlations more sensitive to false imputation of SNPs having low Minor Allele Frequency (MAF) when the EQ methodology was used. An increase in imputation accuracy was obtained when SNP density was increased, either through an increase in the number of selected windows on a chromosome or through the rise of the LD threshold. Moreover, the results varied depending on the type of chromosome (macro or micro-chromosome). The LD methodology enabled to optimize the number of SNPs, by reducing the SNP density on macro-chromosomes and by increasing it on micro-chromosomes. Imputation accuracy also increased when the size of the reference population was increased. Conversely, imputation accuracy decreased when the degree of kinship between reference and candidate populations was reduced. Finally, adding selection candidates’ dams in the reference population, in addition to their sire, enabled to get better imputation results. Conclusions Whichever the SNP chip, the methodology, and the scenario studied, highly accurate imputations were obtained, with mean correlations higher than 0.83. The key point to achieve good imputation results is to take into account chicken lines’ LD when designing a low density SNP chip, and to include the candidates’ direct parents in the reference population.

Context: Genomic selection has been of increasing interest in the genetic improvement of Zebu cattle, particularly for quantitative traits that are difficult or expensive to measure, such as carcass traits and meat tenderness. The success of genomic selection depends on several factors, and at its core is the availability of single-nucleotide polymorphism (SNP) chips that are appropriately designed for Bos indicus cattle. However, the currently available commercial bovine SNP chips are mostly designed for Bos taurus cattle. There are two commercial Bos indicus SNP chips; namely, GeneSeek genomic profiler high-density Bos indicus (GGP-HD i) SNP chip and a low-density (LD) Bos indicus SNP chip (Z chip), but these two Bos indicus SNP chips were built with mixed contents of SNPs for Bos indicus and Bos taurus cattle, due to limited availability of genotype data from Bos indicus cattle. Aims: To develop a new GGP indicus 35 000 SNP chip specifically for Bos indicus cattle, which has a low cost, but high accuracy of imputation to Illumina BovineHD chips. Methods: The design of the chip consisted of 34 000 optimally selected SNPs, plus 1000 SNPs pre-reserved for those on the Y chromosome, 'causative' mutations for a variety of economically relevant traits, genetic health conditions and International Society for Animal Genetics globally recognised parentage markers for those breeds of cattle. Key results: The present results showed that this new indicus LD SNP chip had considerably increased minor allele frequencies in indicus breeds than the previous Z-chip. It demonstrated with high imputation accuracy to HD SNP genotypes in five indicus breeds, and with considerable predictability on 14 growth and reproduction traits in Nellore cattle. Conclusions: This new indicus LD chip represented a successful effort to leverage existing knowledge and genotype resources towards the public release of a cost-effective LD SNP chip specifically for Bos indicus cattle, which is expected to replace the previous GGP indicus LD chip and to supplement the existing GGP-HD i 80 000 SNP chip. Implications: A new SNP chip specifically designed for Bos indicus , with high power of imputation to Illumina BovineHD technology and with excellent coverage of the whole genome, is now available on the market for Bos indicus cattle, and Bos indicus and Bos taurus crosses. This work reports the development of a low-density DNA genetic markers (single-nucleotide polymorphism) panel specifically for Bos indicus cattle and their crosses, applied to identify the association of these markers with economically relevant traits and to the genomic selection, with high accuracy of genotype imputation to high-density single-nucleotide polymorphism chips. [ABSTRACT FROM AUTHOR]

Background: Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research. Unlike standard imputation approaches, RF-based imputation methods do not assume normality or require specification of parametric models. However, it is still inconclusive how they perform for non-normally distributed data or when there are non-linear relationships or interactions.Methods: To examine the effects of these three factors, a variety of datasets were simulated with outcome-dependent missing at random (MAR) covariates, and the performances of the RF-based imputation methods missForest and CALIBERrfimpute were evaluated in comparison with predictive mean matching (PMM).Results: Both missForest and CALIBERrfimpute have high predictive accuracy but missForest can produce severely biased regression coefficient estimates and downward biased confidence interval coverages, especially for highly skewed variables in nonlinear models. CALIBERrfimpute typically outperforms missForest when estimating regression coefficients, although its biases are still substantial and can be worse than PMM for logistic regression relationships with interaction.Conclusions: RF-based imputation, in particular missForest, should not be indiscriminately recommended as a panacea for imputing missing data, especially when data are highly skewed and/or outcome-dependent MAR. A correct analysis requires a careful critique of the missing data mechanism and the inter-relationships between the variables in the data. [ABSTRACT FROM AUTHOR]

Summary: Natural variation has become a prime resource to identify genetic variants that contribute to phenotypic variation. The regional mapping (RegMap) population is one of the most important populations for studying natural variation in Arabidopsis thaliana, and has been used in a large number of association studies and in studies on climatic adaptation. However, only 413 RegMap accessions have been completely sequenced, as part of the 1001 Genomes (1001G) Project, while the remaining 894 accessions have only been genotyped with the Affymetrix 250k chip. As a consequence, most association studies involving the RegMap are either restricted to the sequenced accessions, reducing power, or rely on a limited set of SNPs. Here we impute millions of SNPs to the 894 accessions that are exclusive to the RegMap, using the 1135 accessions of the 1001G Project as the reference panel. We assess imputation accuracy using a novel cross‐validation scheme, which we show provides a more reliable measure of accuracy than existing methods. After filtering out low accuracy SNPs, we obtain high‐quality genotypic information for 2029 accessions and 3 million markers. To illustrate the benefits of these imputed data, we reconducted genome‐wide association studies on five stress‐related traits and could identify novel candidate genes. Significance Statement: In this study, we imputed the genotypic information of 849 accessions of Arabidopsis thaliana with high accuracy. This work makes a large and intensive genotypic resource available for the scientific community and for further meta‐analysis. [ABSTRACT FROM AUTHOR]

With the availability of the 600K Affymetrix Axiom high-density (HD) single nucleotide polymorphism (SNP) chip, genomic selection has been implemented in broiler and layer chicken. However, the cost of this SNP chip is too high to genotype all selection candidates. A solution is to develop a low-density SNP chip, at a lower price, and to impute all missing markers. But to routinely implement this solution, the impact of imputation on genomic evaluation accuracy must be studied. It is also interesting to study the consequences of the use of low-density SNP chips in genomic evaluation accuracy. In this perspective, the interest of using imputation in genomic selection was studied in a pure layer line. Two low-density SNP chip designs were compared: an equidistant methodology and a methodology based on linkage disequilibrium. Egg weight, egg shell color, egg shell strength, and albumen height were evaluated with single-step genomic best linear unbiased prediction methodology. The impact of imputation errors or the absence of imputation on the ranking of the male selection candidates was assessed with a genomic evaluation based on ancestry. Thus, genomic estimated breeding values (GEBV) obtained with imputed HD genotypes or low-density genotypes were compared with GEBV obtained with the HD SNP chip. The relative accuracy of GEBV was also investigated by considering as reference GEBV estimated on the offspring. A limited reordering of the breeders, selected on a multitrait index, was observed. Spearman correlations between GEBV on HD genotypes and GEBV on low-density genotypes (with or without imputation) were always higher than 0.94 with more than 3K SNP. For the genetically closer, top 150 individuals for a specific trait, with imputation, the reordering was reduced with correlation higher than 0.94 with more than 3K SNP. Without imputation, the correlations remained lower than 0.85 with less than 3K and 16K SNP for equidistant and linkage disequilibrium methodology, respectively. The differences in GEBV correlations between both methodologies were never significant. The conclusions were the same for all studied traits. [ABSTRACT FROM AUTHOR]

• Reference panel for genotype imputation for purebred. • Impact of multi breed reference panel when imputing purebred genotype. • Similar imputation accuracy between within-breed and multi breed reference panel. • Negative impact on imputation accuracy with a multi breed reference panel. • Accurate imputed genotype for purebred can be obtained from within-breed imputation. Genotype imputation is a standard approach used in the field of genetics. It can be used to fill in missing genotypes or to increase genotype density. Accurate imputed genotypes are required for downstream analyses. In this study, the accuracy of whole-genome sequence imputation for Angus beef cattle was examined using two different ways to form the reference panel, a within-breed reference population and a multi breed reference population. A stepwise imputation was conducted by imputing medium-density (50k) genotypes to high-density, and then to the whole genome sequence (WGS). The reference population consisted of animals with WGS information from the 1 000 Bull Genomes project. The within-breed reference panel comprised 396 Angus cattle, while an additional 2 380 Taurine cattle were added to the reference population for the multi breed reference scenario. Imputation accuracies were variant-wise average accuracies from a 10-fold cross-validation and expressed as concordance rates (CR) and Pearson's correlations (PR). The two imputation scenarios achieved moderate to high imputation accuracies ranging from 0.896 to 0.966 for CR and from 0.779 to 0.834 for PR. The accuracies from two different scenarios were similar, except for PR from WGS imputation, where the within-breed scenario outperformed the multi breed scenario. The result indicated that including a large number of animals from other breeds in the reference panel to impute purebred Angus did not improve the accuracy and may negatively impact the results. In conclusion, the imputed WGS in Angus cattle can be obtained with high accuracy using a within-breed reference panel. [ABSTRACT FROM AUTHOR]

Abstract Background Genotypes not directly measured in genetic studies are often imputed to improve statistical power and to increase mapping resolution. The accuracy of standard imputation techniques strongly depends on the similarity of linkage disequilibrium (LD) patterns in the study and reference populations. Here we develop a novel approach for genotype imputation in low-recombination regions that relies on the coalescent and permits to explicitly account for population demographic factors. To test the new method, study and reference haplotypes were simulated and gene trees were inferred under the basic coalescent and also considering population growth and structure. The reference haplotypes that first coalesced with study haplotypes were used as templates for genotype imputation. Computer simulations were complemented with the analysis of real data. Genotype concordance rates were used to compare the accuracies of coalescent-based and standard (IMPUTE2) imputation. Results Simulations revealed that, in LD-blocks, imputation accuracy relying on the basic coalescent was higher and less variable than with IMPUTE2. Explicit consideration of population growth and structure, even if present, did not practically improve accuracy. The advantage of coalescent-based over standard imputation increased with the minor allele frequency and it decreased with population stratification. Results based on real data indicated that, even in low-recombination regions, further research is needed to incorporate recombination in coalescence inference, in particular for studies with genetically diverse and admixed individuals. Conclusions To exploit the full potential of coalescent-based methods for the imputation of missing genotypes in genetic studies, further methodological research is needed to reduce computer time, to take into account recombination, and to implement these methods in user-friendly computer programs. Here we provide reproducible code which takes advantage of publicly available software to facilitate further developments in the field.

Abstract Background Haplotype reconstruction (phasing) is an essential step in many applications, including imputation and genomic selection. The best phasing methods rely on both familial and linkage disequilibrium (LD) information. With whole-genome sequence (WGS) data, relatively small samples of reference individuals are generally sequenced due to prohibitive sequencing costs, thus only a limited amount of familial information is available. However, reference individuals have many relatives that have been genotyped (at lower density). The goal of our study was to improve phasing of WGS data by integrating familial information from haplotypes that were obtained from a larger genotyped dataset and to quantify its impact on imputation accuracy. Results Aligning a pre-phased WGS panel [~5 million single nucleotide polymorphisms (SNPs)], which is based on LD information only, to a 50k SNP array that is phased with both LD and familial information (called scaffold) resulted in correctly assigning parental origin for 99.62% of the WGS SNPs, their phase being determined unambiguously based on parental genotypes. Without using the 50k haplotypes as scaffold, that value dropped as expected to 50%. Correctly phased segments were on average longer after alignment to the genotype phase while the number of switches decreased slightly. Most of the incorrectly assigned segments, and subsequent switches, were due to singleton errors. Imputation from 50k SNP array to WGS data with improved phasing had a marginal impact on imputation accuracy (measured as r 2), i.e. on average, 90.47% with traditional techniques versus 90.65% with pre-phasing integrating familial information. Differences were larger for SNPs located in chromosome ends and rare variants. Using a denser WGS panel (~13 millions SNPs) that was obtained with traditional variant filtering rules, we found similar results although performances of both phasing and imputation accuracy were lower. Conclusions We present a phasing strategy for WGS data, which indirectly integrates familial information by aligning WGS haplotypes that are pre-phased with LD information only on haplotypes obtained with genotyping data, with both LD and familial information and on a much larger population. This strategy results in very few mismatches with the phase obtained by Mendelian segregation rules. Finally, we propose a strategy to further improve phasing accuracy based on haplotype clusters obtained with genotyping data.

Abstract Despite thousands of genetic loci identified to date, a large proportion of genetic variation predisposing to complex disease and traits remains unaccounted for. Advances in sequencing technology enable focused explorations on the contribution of low-frequency and rare variants to human traits. Here we review experimental approaches and current knowledge on the contribution of these genetic variants in complex disease and discuss challenges and opportunities for personalised medicine.

Although several studies have investigated the factors affecting imputation accuracy, most of these studies involved a large number of genotyped animals. Thus, results from these studies cannot be directly applied to small populations, since the population structure affects imputation accuracy. In addition, factors affecting imputation accuracy may also be intensified in small populations. Therefore, we aimed to compare different imputation strategies for the Portuguese Holstein cattle population considering several commercially available single nucleotide polymorphism (SNP) panels in a relatively small number of genotyped animals. Data from 1359 genotyped animals were used to evaluate imputation in 7 different scenarios. In the S1 to S6 scenarios, imputations were performed from LDv1, 50Kv1, 57K, 77K, HDv3 and Ax58K panels to 50Kv2 panel. In these scenarios, the bulls in 50Kv2 were divided into reference (352) and validation (101) populations based on the year of birth. In the S7 scenario, the validation population consisted of 566 cows genotyped with the Ax58K panel with their genotypes masked to LDv1. In general, all sample imputation accuracies were high with correlations ranging from 0.94 to 0.99 and concordance rate ranging from 92.59 to 98.18%. SNP-specific accuracy was consistent with that of sample imputation. S4 (40.32% of SNPs imputed) had higher accuracy than S2 and S3, both with less than 7.59% of SNPs imputed. Most probably, this was due to the high number of imputed SNPs with minor allele frequency (MAF) < 0.05 in S2 and S3 (by 18.43% and 16.06% higher than in S4, respectively). Therefore, for these two scenarios, MAF was more relevant than the panel density. These results suggest that genotype imputation using several commercially available SNP panels is feasible for the Portuguese national genomic evaluation. [ABSTRACT FROM AUTHOR]

Highlights • Is it feasible to implement genomic selection for within-litter piglet selection? • Ultra-low density SNP panels are of great value to select within-litter piglets. • A 1.5 K SNPs panel balances the trade-off between SNP density and selection accuracy. • Within-litter selection will contribute to increase selection intensity and genetic progress. Abstract Genotyping costs and the large number of selection candidates are major factors that inhibit the application of genomic selection in the swine industry and other small-sized livestock species. In order to reduce genotyping costs and increase the uptake of genomic selection, a possible strategy is to genotype animals with an affordable low-density (LD) SNP panel and, then accurately impute the LD panel to a high-density (HD) SNP panel. For within-litter piglet selection, genotyping all piglets from all farrows using the commercially available SNP chips is still cost prohibitive. Consequently, genomic evaluation is limited in this stage and genotypic and phenotypic data from all piglets in a litter are rarely available. This study investigates the feasibility of implementing genomic selection for within-litter piglet selection, using a total of nine simulated LD panels: from the "ultra" low (300–3000 SNP markers) to moderately low (6000–10, 000 SNP markers). For each LD panel, the performance of the genomic predictions according to the accuracy of genotype imputation, the accuracy of the genomic estimated breeding values (GEBV) based on the imputed data, and distribution of the correctly selected animals within litter was evaluated and compared to using the simulated HD panel (60,000 SNP) and True Breeding Values (TBVs). In this simulation study, we considered three economically important traits: back fat thickness (BF), growth rate of age to 100 Kg (GR), and litter size (LS). For the LD panel sizes ranging from 300 to 10,000, the accuracy of imputation (measured as concordance rate) ranged from 73.20 to 99.81%; and the mean proportion of the correctly selected top rank animals within litter ranged from 55 to 98%. Based on the trade-off between panel size and genomic selection accuracy, the use of a LD panel containing 1500 SNPs might be recommended, as this panel yielded more than 85% correctly selected animals within-litter based on all three traits. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the accuracy of imputation from low density (LDC) to moderate density SNP chips (MDC) in a Thai Holstein-Other multibreed dairy cattle population. Dairy cattle with complete pedigree information (n = 1,244) from 145 dairy farms were genotyped with GeneSeek GGP20K (n = 570), GGP26K (n = 540) and GGP80K (n = 134) chips. After checking for single nucleotide polymorphism (SNP) quality, 17,779 SNP markers in common between the GGP20K, GGP26K, and GGP80K were used to represent MDC. Animals were divided into two groups, a reference group (n = 912) and a test group (n = 332). The SNP markers chosen for the test group were those located in positions corresponding to GeneSeek GGP9K (n = 7,652). The LDC to MDC genotype imputation was carried out using three different software packages, namely Beagle 3.3 (population-based algorithm), FImpute 2.2 (combined family- and population-based algorithms) and Findhap 4 (combined family- and population-based algorithms). Imputation accuracies within and across chromosomes were calculated as ratios of correctly imputed SNP markers to overall imputed SNP markers. Imputation accuracy for the three software packages ranged from 76.79% to 93.94%. FImpute had higher imputation accuracy (93.94%) than Findhap (84.64%) and Beagle (76.79%). Imputation accuracies were similar and consistent across chromosomes for FImpute, but not for Findhap and Beagle. Most chromosomes that showed either high (73%) or low (80%) imputation accuracies were the same chromosomes that had above and below average linkage disequilibrium (LD; defined here as the correlation between pairs of adjacent SNP within chromosomes less than or equal to 1 Mb apart). Results indicated that FImpute was more suitable than Findhap and Beagle for genotype imputation in this Thai multibreed population. Perhaps additional increments in imputation accuracy could be achieved by increasing the completeness of pedigree information.

Abstract Background Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. Conclusions The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

Abstract In real world data are often plagued by missing values which adversely affects the final outcome of the analysis based on such data. The missing values can be handled using various techniques like deletion or imputation. Of late, R has become one of the most preferred platform for carrying out data analysis, and its popularity is growing further. R provides various packages for handling missing values through imputation. The presence of multiple packages however, calls for an analysis of their comparative performance and examine their suitability for handling a given set of data. The performance of different R packages may differ for different datasets and may depend on the size of the dataset and richness of the missing values in the datasets. In this paper, the authors perform comparative study of the performance of the common R packages, namely VIM, MICE, MissForest, and HMISC, used for missing value imputation. The authors measured the performances of the said packages in terms of their imputation time, imputation efficiency and the effect on the variance. The imputation efficiency was measured in terms of the difference in predictive performance of a model built using original dataset vis-à-vis a dataset with imputed values. Similarly, the variance of the variables in the original dataset was compared that of corresponding variables in the imputed dataset. A missing value imputation package can be considered to be better if it consumes less imputation time and provides high imputation accuracy. Also in terms of variance, one would like to have the imputation package maintain the original variance of the variables. On analysing the four imputation packages on two datasets over three predictive algorithms–Logistic Regression, Support Vector Machines, and Artificial Neural Networks–it was observed that the performances varies depending on the size of the dataset, and the missing values present in them. The study highlights that certain missing value package used in conjunction with a given predictive algorithm provides better performance, which is again a function of the dataset characteristics. [ABSTRACT FROM AUTHOR]

Highlights • Reasonable imputation accuracy for buffalo can be achieved by imputing from a low-density to 90K SNP BeadChip even using a small reference population size. • With a small reference population size, minimac performed better than the two other imputation tools. • Accurate imputations were obtained using FImpute followed by Beagle and minimac when a relatively large reference population size was used. • Poor imputation for rare alleles could be traced when using the correlation coefficient as a measure of imputation accuracy. Abstract The accuracy of genotype imputation from a customized low-density to a moderate-density single-nucleotide polymorphism marker panel was tested in Italian Mediterranean buffalo using different sizes of reference populations (RP) and imputation programs. Masked genotypes were imputed using Beagle, minimac and FImpute. The accuracy of predicted genotypes was assessed by the percentage of correctly imputed genotypes. As expected, overall imputation accuracy was better for larger RP sizes. However, increasing the RP size from small to intermediate (i.e., from 42 to 202) resulted in a greater improvement in imputation accuracy compared to increasing the RP size from intermediate to large (i.e., from 202 to 736). The percentage of correctly imputed genotypes was 97.3%, 97.9% and 98.0% for minimac, Beagle and FImpute, respectively, using 736 reference individuals. The accuracy of imputation tended to decrease for high minor-allele frequency of imputed genotypes especially when the RP size was small. The variation in accuracy with increasing RP size was highest for Beagle and lowest for minimac. The accuracy of imputation was larger when the genomic relationship between the reference and the target populations was higher for all three imputation software tools used in this study. The trend of increasing imputation accuracy with genomic relationship was strongest when the RP size was small. Our results indicate that imputation of genotypes from a low to a moderate density panel (i.e., 10K to 90K) is a valid option to reduce the cost of genotyping for genomic evaluation in Italian Mediterranean buffalo. [ABSTRACT FROM AUTHOR]

Summary: The dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labrador Retriever dogs. The study involved 5826 SNPs on chromosome 1 representing the high density (HighD) array; the low‐density (LowD) array was simulated by masking different proportions of SNPs on the HighD array. The correlations between true and imputed genotypes for a realistic masking level of 87.5% ranged from 0.92 to 0.97, depending on the scenario used. A correlation of 0.92 was found for a likely scenario (10% of dogs genotyped using HighD, 87.5% of HighD SNPs masked in the LowD array), which indicates that genotype imputation in Labrador Retrievers can be a valuable tool to reduce experimental costs while increasing sample size. Furthermore, we show that genotype imputation can be performed successfully even without pedigree information and with low relatedness between dogs in the reference and validation sets. Based on these results, the impact of genotype imputation was evaluated in a genome‐wide association analysis and genomic prediction in Labrador Retrievers. [ABSTRACT FROM AUTHOR]