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Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

With the rapid and significant cost reduction of next-generation sequencing, low-coverage whole-genome sequencing (lcWGS), followed by genotype imputation, is becoming a cost-effective alternative to single-nucleotide polymorphism (SNP)-array genotyping. The objectives of this study were 2-fold: (1) construct a haplotype reference panel for genotype imputation from lcWGS data in rainbow trout (Oncorhynchus mykiss); and (2) evaluate the concordance between imputed genotypes and SNP-array genotypes in 2 breeding populations. Medium-coverage (12×) whole-genome sequences were obtained from a total of 410 fish representing 5 breeding populations with various spawning dates. The short-read sequences were mapped to the rainbow trout reference genome, and genetic variants were identified using GATK. After data filtering, 20,434,612 biallelic SNPs were retained. The reference panel was phased with SHAPEIT5 and was used as a reference to impute genotypes from lcWGS data employing GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3×, and these fish were also genotyped with the Axiom 57K rainbow trout SNP array. The concordance between array-based genotypes and imputed genotypes was 99.1%. After downsampling the coverage to 0.5×, 0.2×, and 0.1×, the concordance between array-based genotypes and imputed genotypes was 98.7, 97.8, and 96.7%, respectively. In the USDA odd-year breeding population, the concordance between array-based genotypes and imputed genotypes was 97.8% for 109 fish downsampled to 0.5× coverage. Therefore, the reference haplotype panel reported in this study can be used to accurately impute genotypes from lcWGS data in rainbow trout breeding populations. [ABSTRACT FROM AUTHOR]

Genetic maps are fundamental resources for linkage and association studies. A fine‐scale genetic map can be constructed by inferring historical recombination events from the genome‐wide structure of linkage disequilibrium—a non‐random association of alleles among loci—by using population‐scale sequencing data. We constructed a fine‐scale genetic map and identified recombination hotspots from 10 092 551 bi‐allelic high‐quality autosomal markers segregating among 150 unrelated Japanese individuals whose genotypes were determined by high‐coverage (30×) whole‐genome sequencing, and the genotype quality was carefully controlled by using their parents' and offspring's genotypes. The pedigree information was also utilized for haplotype phasing. The resulting genome‐wide recombination rate profiles were concordant with those of the worldwide population on a broad scale, and the resolution was much improved. We identified 9487 recombination hotspots and confirmed the enrichment of previously known motifs in the hotspots. Moreover, we demonstrated that the Japanese genetic map improved the haplotype phasing and genotype imputation accuracy for the Japanese population. The construction of a population‐specific genetic map will help make genetics research more accurate. [ABSTRACT FROM AUTHOR]

Ancient DNA provides a unique frame for directly studying human population genetics in time and space. Still, since most of the ancient genomic data is low coverage, analysis is confronted with a low number of SNPs, genotype uncertainties, and reference-bias. Here, we for the first time benchmark the two distinct versions of Glimpse tools on 120 ancient human genomes from Eurasia including those largely from previously under-evaluated regions and compare the performance of genotype imputation with de facto analysis approaches for low coverage genomic data analysis. We further investigate the impact of two distinct reference panels on imputation accuracy for low coverage genomic data. We compute accuracy statistics and perform PCA and f4-statistics to explore the behaviour of genotype imputation on low coverage data regarding (i)two versions of Glimpse, (ii)two reference panels, (iii)four post-imputation filters and coverages, as well as (iv)data type and geographical origin of the samples on the analyses. Our results reveal that even for 0.1X coverage ancient human genomes, genotype imputation using Glimpse-v2 is suitable. Additionally, using the 1000 Genomes merged with Human Genome Diversity Panel improves the accuracy of imputation for the rare variants with low MAF, which might be important not only for ancient genomics but also for modern human genomic studies based on low coverage data and for haplotype-based analysis. Most importantly, we reveal that genotype imputation of low coverage ancient human genomes reduces the genetic affinity of the samples towards human reference genome. Through solving one of the most challenging biases in data analysis, so-called reference bias, genotype imputation using Glimpse v2 is promising for low coverage ancient human genomic data analysis and for rare-variant-based and haplotype-based analysis. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is the most common cancer in women and has been associated with genetic and environmental factors. New developments have led to the creation of genetic risk scores which seek to better approximate an individual's risk of developing cancer and to be used clinically to improve cancer screening. Previous studies have shown that these risk scores can be used to determine an individual's cancer risk, but replication in independent groups is limited. In addition, certain genotyping methods utilize a process called imputation, which has the potential to make polygenic risk scores less accurate. This work aims to validate two breast cancer polygenic risk scores and to interrogate the impact imputation has on their values to improve their clinical utility. Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2–3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes. [ABSTRACT FROM AUTHOR]

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31–1.04% compared to the chip data, and by 0.7–1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r 2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ  value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r 2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r 2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value. [ABSTRACT FROM AUTHOR]

Abstract Background Genotype imputation is an essential step in genetic studies to improve data quality and statistical power. Public imputation servers are widely used by researchers to impute their data using otherwise access-controlled reference panels of high-fidelity genomes held by these servers. Results We report evidence against the prevailing assumption that providing access to panels only indirectly via imputation servers poses a negligible privacy risk to individuals in the panels. To this end, we present algorithmic strategies for adaptively constructing artificial input samples and interpreting their imputation results that lead to the accurate reconstruction of reference panel haplotypes. We illustrate this possibility on three reference panels of real genomes for a range of imputation tools and output settings. Moreover, we demonstrate that reconstructed haplotypes from the same individual could be linked via their genetic relatives using our Bayesian linking algorithm, which allows a substantial portion of the individual’s diploid genome to be reassembled. We also provide population genetic estimates of the proportion of a panel that could be linked when an adversary holds a varying number of genomes from the same population. Conclusions Our results show that genomes in imputation server reference panels can be vulnerable to reconstruction, implying that additional safeguards may need to be considered. We suggest possible mitigation measures based on our findings. Our work illustrates the value of adversarial algorithms in uncovering new privacy risks to help inform the genomics community towards secure data sharing practices.

Abstract Background Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies’ (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance. Results SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays. Conclusions Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.

Numbers of vertebrae is an important economic trait associated with body size and meat productivity in animals. However, the genetic basis of vertebrae number in donkey remains to be well understood. The aim of this study was to identify candidate genes affecting the number of thoracic (TVn) and the number of lumbar vertebrae (LVn) in Dezhou donkey. A genome-wide association study was conducted using whole genome sequence data imputed from low-coverage genome sequencing. For TVn, we identified 38 genome-wide significant and 64 suggestive SNPs, which relate to 7 genes (NLGN1, DCC, SLC26A7, TOX, WNT7A, LOC123286078, and LOC123280142). For LVn, we identified 9 genome-wide significant and 38 suggestive SNPs, which relate to 8 genes (GABBR2, FBXO4, LOC123277146, LOC123277359, BMP7, B3GAT1, EML2, and LRP5). The genes involve in the Wnt and TGF-β signaling pathways and may play an important role in embryonic development or bone formation and could be good candidate genes for TVn and LVn.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01x sequence coverage, which was slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (p-value < 2 x 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2x GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Abstract Background As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts. Results We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs. Conclusions We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.

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.

Background: Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies' (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance. Results: SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays. Conclusions: Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays. [ABSTRACT FROM AUTHOR]

Background: As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts. Results: We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs. Conclusions: We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs. [ABSTRACT FROM AUTHOR]

A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.

Objective Pigs, an ideal biomedical model for human diseases, suffer from about 50% early embryonic and fetal death, a major cause of fertility loss worldwide. However, identifying the causal variant remains a huge challenge. This study aimed to detect single nucleotide polymorphisms (SNPs) and candidate genes for the number of mummified (NM) piglets using the imputed whole-genome sequence (WGS) and validate the potential candidate genes. Methods The imputed WGS was introduced from genotyping-by-sequencing (GBS) using a multi-breed reference population. We performed genome-wide association studies (GWAS) for NM piglets at birth from a Landrace pig populatiGWAS peak located on SSC11: 0.10 to 7.11 Mbp (Top SNP, SSC11:1,889,658 bp; p = 9.98E-13) was identified in cyclin dependent kinase on. A total of 300 Landrace pigs were genotyped by GBS. The whole-genome variants were imputed, and 4,252,858 SNPs were obtained. Various molecular experiments were conducted to determine how the genes affected NM in pigs. Results A strong GWAS peak located on SSC11: 0.10 to 7.11 Mbp (Top SNP, SSC11:1,889,658 bp; p = 9.98E-13) was identified in cyclin dependent kinase 8 (CDK8) gene, which plays a crucial role in embryonic retardation and lethality. Based on the molecular experiments, we found that Y-box binding protein 1 (YBX1) was a crucial transcription factor for CDK8, which mediated the effect of CDK8 in the proliferation of porcine ovarian granulosa cells via transforming growth factor beta/small mother against decapentaplegic signaling pathway, and, as a consequence, affected embryo quality, indicating that this pathway may be contributing to mummified fetal in pigs. Conclusion A powerful imputation-based association study was performed to identify genes associated with NM in pigs. CDK8 was suggested as a functional gene for the proliferation of porcine ovarian granulosa cells, but further studies are required to determine causative mutations and the effect of loci on NM in pigs.

Reproductive traits have a key impact on production efficiency in the pig industry. It is necessary to identify the genetic structure of potential genes that influence reproductive traits. In this study, a genome-wide association study (GWAS) based on chip and imputed data of five reproductive traits, namely, total number born (TNB), number born alive (NBA), litter birth weight (LBW), gestation length (GL), and number of weaned (NW), was performed in Yorkshire pigs. In total, 272 of 2844 pigs with reproductive records were genotyped using KPS Porcine Breeding SNP Chips, and then chip data were imputed to sequencing data using two online software programs: the Pig Haplotype Reference Panel (PHARP v2) and Swine Imputation Server (SWIM 1.0). After quality control, we performed GWAS based on chip data and the two different imputation databases by using fixed and random model circulating probability unification (FarmCPU) models. We discovered 71 genome-wide significant SNPs and 25 potential candidate genes (e.g., SMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5). Functional enrichment analysis revealed that these genes are mainly enriched in the calcium signaling pathway, ovarian steroidogenesis, and GnRH signaling pathways. In conclusion, our results help to clarify the genetic basis of porcine reproductive traits and provide molecular markers for genomic selection in pig breeding. [ABSTRACT FROM AUTHOR]

A new study published in the Plant Biotechnology Journal presents a high-quality soybean genetic variation panel called 4kSoyGVP, which can be used for genotype imputation and improving the power of genome-wide association studies (GWAS) in soybean research. The panel was constructed using population-level resequencing data from 4414 soybean accessions from major soybean-production regions around the world. The study also introduces a soybean genetic variation platform called SoyGVD, which provides important data resources and tools for future soybean genetic research and breeding. The research was supported by various funding sources and the authors declare no competing financial interests. [Extracted from the article]

Abstract Background The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype–phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li–Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. Results Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants. Conclusions Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer .

Reading difficulties are prevalent worldwide, including in economically developed countries, and are associated with low academic achievement and unemployment. Longitudinal studies have identified several early childhood predictors of reading ability, but studies frequently lack genotype data that would enable testing of predictors with heritable influences. The National Child Development Study (NCDS) is a UK birth cohort study containing direct reading skill variables at every data collection wave from age 7 years through to adulthood with a subsample (final n = 6431) for whom modern genotype data are available. It is one of the longest running UK cohort studies for which genotyped data are currently available and is a rich dataset with excellent potential for future phenotypic and gene-by-environment interaction studies in reading. Here, we carry out imputation of the genotype data to the Haplotype Reference Panel, an updated reference panel that offers greater imputation quality. Guiding phenotype choice, we report a principal components analysis of nine reading variables, yielding a composite measure of reading ability in the genotyped sample. We include recommendations for use of composite scores and the most reliable variables for use during childhood when conducting longitudinal, genetically sensitive analyses of reading ability. [ABSTRACT FROM AUTHOR]

Genotype imputation is widely used to enrich genetic datasets. The operation relies on panels of known reference haplotypes, typically with whole-genome sequencing data. How to choose a reference panel has been widely studied and it is essential to have a panel that is well matched to the individuals who require missing genotype imputation. However, it is broadly accepted that such an imputation panel will have an enhanced performance with the inclusion of diversity (haplotypes from many different populations). We investigate this observation by examining, in fine detail, exactly which reference haplotypes are contributing at different regions of the genome. This is achieved using a novel method of inserting synthetic genetic variation into the reference panel in order to track the performance of leading imputation algorithms. We show that while diversity may globally improve imputation accuracy, there can be occasions where incorrect genotypes are imputed following the inclusion of more diverse haplotypes in the reference panel. We, however, demonstrate a technique for retaining and benefitting from the diversity in the reference panel whilst avoiding the occasional adverse effects on imputation accuracy. What is more, our results more clearly elucidate the role of diversity in a reference panel than has been shown in previous studies. [ABSTRACT FROM AUTHOR]

Single-step genomic best linear unbiased prediction (ssGBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence (WGS) data at the population level, more attention is being paid to the usage of WGS data in ssGBLUP. The predictive ability of ssGBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ssGBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms (SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ssGBLUP, haplotype-based ssGHBLUP, and four extended ssGBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation (GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ssGBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ssGBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ssGBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family (SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ssGBLUP, and validated the idea that properly handling the genomic annotation information and WGS data increased the predictive ability of ssGBLUP. Moreover, while using WGS data, the genotyping strategy of maximizing the expected genetic relationship between the reference and candidate population could further improve the predictive ability of ssGBLUP. The results from this study shed light on the comprehensive usage of genomic annotation information in WGS-based single-step genomic prediction.

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]

In order to improve the application of genomic resources in genomic selection breeding of aquaculture species, it is imperative to develop high throughput genotyping methods that are suitable for aquatic species with highly heterozygous and complex genomes. This study aimed to address the issue of accurate genotype imputation for lcWGS (low coverage whole genome sequencing) without reference panel in aquatic species. We performed lcWGS with an average depth of 2.8× on 360 individuals of Crassostrea gigas , then performed genotype imputation through reference-free method to achieve genome-wide genotyping. The results demonstrated that STITCH showed outstanding performance in conducting genotype imputation without reference panel, with R2 of 0.860 ± 0.055 and GC of 0.936 ± 0.022. The comparison of accuracy among different populations indicated that the selected breeding population with higher level of linkage disequilibrium within the population showed significant improvement in imputation accuracy compared to the wild population (P < 0.01). The diminishing marginal utility in accuracy beyond 300 samples suggested that hundreds of individuals were adequate for accurate genotyping by lcWGS. The imputed genotypes showed high accuracy in identifying genetic relationships and potential application in selection breeding. This study provides valuable information toward application of high-throughput and cost-effective genotyping in accelerating genetic analysis and genomic selection of economic traits in aquaculture species. • STITCH showed high imputation accuracy for lcWGS (low coverage whole genome sequencing) data without reference panels. • High linkage disequilibrium strength within the selected population genome was beneficial for performing genotype imputation. • The genotype imputation accuracy tended to stabilize after the sample size used for imputation reached 300. • The imputed genotype showed high accuracy in identifying genetic relationships and for genetic breeding of aquatic species. [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.

Abstract Background Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce Iam hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. Iam (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results Applying both measures to a large case–control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for Iam and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of Iam and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2). Conclusion We recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data.

Owing to massive jump in DNA technology, large-scale genomic datasets, including valuable information, have become available. While this is a prodigious opportunity, and it can also be a big challenge because analysing these large datasets with current computers and software tools is very difficult and may take days or even weeks to complete. Novel approaches such as parallel computing have been suggested to deal with these large datasets. Here, the effect of parallel computing on the performance of random forest (RF) algorithm for imputation of missing genotypes was studied. To this end, the genotypic matrices were simulated for, respectively, 500, 1000, 2000, and 3000 single-nucleotide polymorphism (SNP) for 500, 1000 and 2000 individuals, respectively. Then, 50% of genotypic information was masked and imputed by RF. The per cent of genotypes correctly imputed was used to measure accuracy of genotype imputation. Serial and parallel computing were applied to the data. In comparison to serial computing, parallel computing did not affect the accuracy of imputation, and the accuracy was the same in both scenarios. However, regarding computational time, parallel computing accelerated the analyses significantly in a way that it reduced the running time up to 63%. This was due to the fact that in the serial computing, only 10% of the processing power of the central processing unit (CPU) of the machine was used by the RF, while in the parallel computing, 55% of the processing power of the CPU was utilized. Therefore, as parallel computing significantly reduced the computing time and does not affect the accuracy of the results, this approach should be exploited by researchers to analyse large genomic datasets. [ABSTRACT FROM AUTHOR]

Genotype imputation is essential for enhancing the power of associationmapping and discovering rare and indels that are missed by most genotyping arrays. Imputation analysis can be more accurate with a population-specific reference panel or a multi-ethnic reference panel with numerous samples. The National Institute of Health, Republic of Korea, initiated the Korean Reference Genome (KRG) project to identify variants in wholegenome sequences of ~20,000 Korean participants. In the pilot phase, we analyzed the data from 1,490 participants. The genetic characteristics and imputation performance of the KRG were compared with those of the 1,000 Genomes Project Phase 3, GenomeAsia 100K Project, ChinaMAP, NARD, and TOPMed reference panels. For comparison analysis, genotype panels were artificially generated using whole-genome sequencing data from combinations of four different ancestries (Korean, Japanese, Chinese, and European) and two population-specific optimized microarrays (Korea Biobank Array and UK Biobank Array). The KRG reference panel performed best for the Korean population (R2 = 0.78-0.84, percentage of well-imputed is 91.9% for allele frequency >5%), although the other reference panels comprised a larger number of samples with genetically different background. By comparing multiple reference panels and multi-ethnic genotype panels, optimal imputation was obtained using reference panels from genetically related populations and a population-optimized microarray. Indeed, the reference panels of KRG and TOPMed showed the best performance when applied to the genotype panels of KBA (R2 = 0.84) and UKB (R2 = 0.87), respectively. Using a meta-imputation approach to merge imputation results from different reference panels increased the imputation accuracy for rare variants (~7%) and provided additional well-imputed variants (~20%) with comparable imputation accuracy to that of the KRG. Our results demonstrate the importance of using a population-specific reference panel and meta-imputation to assess a substantial number of accurately imputed rare variants. [ABSTRACT FROM AUTHOR]

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub. [Display omitted] Ever-growing reference panels allow imputation of huge number (∼108) of lower-frequency variants. However, not all variants can be well imputed and standard imputation quality metric poorly reflects true imputation quality, particularly for uncommon variants. We present MagicalRsq, a machine-learning-based and better calibrated post-imputation quality metric, that can rescue 105–106 variants. [ABSTRACT FROM AUTHOR]

Genetic analysis of porcine growth and fatness traits is beneficial to the swine industry and provides a reference to understand human obesity. Here, we obtained 29 growth and fatness traits for 473 individuals from a White Duroc × Erhualian F3 intercross population. Basic statistical analyses showed that: (1) Positive correlations between different-stage body weights were detected, the shorter the time interval the stronger the correlation. (2) Strong correlations existed in the paired fatness traits. (3) With the growth of age, the correlation between fatness and body weight was increasing. All pigs were genotyped by Illumina 50 K SNP chips and their whole-genome genotypes were imputed referred to 109 re-sequencing data. We performed common and imputation-based GWASs for these traits. Two genome-wide significant loci on swine chromosome (SSC) 4 and 7 were repeatedly detected. The strongest association (P = 3.24 × 10−19) was detected at 31.96 Mb on SSC7 for leaf fat weight. On this locus, seven major haplotypes were identified, of which two were novel and had an increasing-fatness effect. In the imputation-based GWAS, three new loci were identified. Our findings provide further insights into and enhance our understanding of genetic mechanism of porcine growth and fat deposition. [ABSTRACT FROM AUTHOR]

Simple Summary: Backfat thickness (BF) and loin muscle area (LMA) are important characteristics in pig breeding. The identification of the quantitative trait loci (QTLs) and genes closely associated with these carcass traits may be useful for pig molecular breeding. In this study, genome-wide association studies (GWAS) using imputation-based whole genome sequencing data of four selected phenotypic traits (adjusted 100 kg BF and LMA, adjusted 100 kg BF estimate breeding values and LMA estimate breeding values) were conducted in a total of 1131 pigs from three pure breeds (French Yorkshire, Landrace, and Duroc). As a result, we highlighted CCND2 and SHANK2 genes as strong candidates affecting BF traits. The results of this study may provide additional insight into the genetic contribution of these genes to carcass meat production capabilities in pig breeding and enhancing lean meat percentages in pigs. Improvement of carcass features is an essential goal in pig genetic breeding programs. Backfat (BF) and loin muscle area (LMA) are important carcass production metrics and useful indicators of pig production performance and lean meat rate. However, the genetic architecture of BF and LMA traits remains elusive. To identify quantitative trait loci (QTLs) and genes associated with these traits, we performed a genome-wide association study (GWAS) using imputation-based whole genome sequencing data for four phenotypes (adjusted 100 kg BF and LMA, adjusted 100 kg BF EBV and LMA EBV) in 1131 pigs from 3 breeds (French Yorkshire, Landrace, and Duroc). After genotype imputation and quality control, 14,163,315 single nucleotide polymorphisms (SNPs) were retained for further analysis. For the adjusted 100 kg BF trait, using the 2-LOD drop method, a QTL with a 13.4 Kb interval (2.91 to 2.93 Mb on SSC2) and containing a SHANK2 gene was defined. In addition, two QTLs with 135.40 Kb (from 66.10 to 66.23 Mb) and 3.12 Kb (from 66.886 to 66.889 Mb) intervals containing CCND2 and TSPAN11 genes, respectively, were found on SSC5. For the BF-EBV trait, two QTLs (128.77 Kb from 66.10 to 66.23 Mb on SSC5 and 42.10 Kb from 2.89 to 2.93 Mb on SSC2) were identified. Notably, CCND2 and SHANK2 were the only candidate genes in their respective QTL interval. Furthermore, we detected a 3.33 Kb (66.106 to 66.110 Mb on SSC2) haplotype block which was detected as affecting the BF_EBV trait, which only contained the CCND2 gene. Thus, we suggested CCND2 and SHANK2 as strong candidate genes for regulating the BF trait for pigs. The empirical confidence intervals of the QTLs were 1.14 Mb (165.65 to 166.79 Mb on SSC6) for adjusted 100 kg LMA and 1.49 Mb (165.26–166.74 Mb on SSC6) for LMA-EBV. These two confidence intervals contained 13 and 28 annotated genes, respectively. Our results provide a deeper understanding of the genetic basis of pig carcass traits. The identified molecular markers will be useful for selecting breeding lines for breeding pigs with superior carcass traits. [ABSTRACT FROM AUTHOR]

Total number born (TNB), number of stillborn (NSB), and gestation length (GL) are economically important traits in pig production, and disentangling the molecular mechanisms associated with traits can provide valuable insights into their genetic structure. Genotype imputation can be used as a practical tool to improve the marker density of single-nucleotide polymorphism (SNP) chips based on sequence data, thereby dramatically improving the power of genome-wide association studies (GWAS). In this study, we applied Beagle software to impute the 50 K chip data to the whole-genome sequencing (WGS) data with average imputation accuracy (R2) of 0.876. The target pigs, 2655 Large White pigs introduced from Canadian and French lines, were genotyped by a GeneSeek Porcine 50K chip. The 30 Large White reference pigs were the key ancestral individuals sequenced by whole-genome resequencing. To avoid population stratification, we identified genetic variants associated with reproductive traits by performing within-population GWAS and cross-population meta-analyses with data before and after imputation. Finally, several genes were detected and regarded as potential candidate genes for each of the traits: for the TNB trait: NOTCH2, KLF3, PLXDC2, NDUFV1, TLR10, CDC14A, EPC2, ORC4, ACVR2A, and GSC; for the NSB trait: NUB1, TGFBR3, ZDHHC14, FGF14, BAIAP2L1, EVI5, TAF1B, and BCAR3; for the GL trait: PPP2R2B, AMBP, MALRD1, HOXA11, and BICC1. In conclusion, expanding the size of the reference population and finding an optimal imputation strategy to ensure that more loci are obtained for GWAS under high imputation accuracy will contribute to the identification of causal mutations in pig breeding. [ABSTRACT FROM AUTHOR]

Non-invasive prenatal testing (NIPT) employs ultra-low-pass sequencing of maternal plasma cell-free DNA to detect fetal trisomy. Its global adoption has established NIPT as a large human genetic resource for exploring genetic variations and their associations with phenotypes. Here, we present methods for analyzing large-scale, low-depth NIPT data, including customized algorithms and software for genetic variant detection, genotype imputation, family relatedness, population structure inference, and genome-wide association analysis of maternal genomes. Our results demonstrate accurate allele frequency estimation and high genotype imputation accuracy (R 2 >0.84) for NIPT sequencing depths from 0.1× to 0.3×. We also achieve effective classification of duplicates and first-degree relatives, along with robust principal-component analysis. Additionally, we obtain an R 2 >0.81 for estimating genetic effect sizes across genotyping and sequencing platforms with adequate sample sizes. These methods offer a robust theoretical and practical foundation for utilizing NIPT data in medical genetic research., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Genetic factors significantly influence the concentration of metabolites in adults. Nevertheless, the genetic influence on neonatal metabolites remains uncertain. To bridge this gap, we employed genotype imputation techniques on large-scale low-pass genome data obtained from non-invasive prenatal testing. Subsequently, we conducted association studies on a total of 75 metabolic components in neonates. The study identified 19 previously reported associations and 11 novel associations between single-nucleotide polymorphisms and metabolic components. These associations were initially found in the discovery cohort (8,744 participants) and subsequently confirmed in a replication cohort (19,041 participants). The average heritability of metabolic components was estimated to be 76.2%, with a range of 69%-78.8%. These findings offer valuable insights into the genetic architecture of neonatal metabolism., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research. [ABSTRACT FROM AUTHOR]

Sequence data can be used in genomic selection (GS), genome-wide association studies (GWAS) and the analysis of recessive lethal defects instead of, or complementary to, SNP array genotypes and increase the power of those applications in various ways. Because sequencing large study cohorts is still expensive, genotype imputation - the prediction of missing genotypes - is a cost-effective alternative to generate sequence-level genotypes for a large number of animals. These genotypes can then be used in subsequent studies and, for example, facilitate the identification of causative mutations. In this context, it is important to consider the accuracy of imputation and to know the factors affecting it. As the availability of sequence data from (warmblood) horses is limited, for this species, imputation is a good possibility to generate sequence level genotypes, which can boost the power of GWAS and the identification of recessive lethal defects. [ABSTRACT FROM AUTHOR]

Understanding the genetic basis of human diseases and traits is dependent on the identification and accurate genotyping of genetic variants. Deep whole-genome sequencing (WGS), the gold standard technology for SNP and indel identification and genotyping, remains very expensive for most large studies. Here, we quantify the extent to which array genotyping followed by genotype imputation can approximate WGS in studies of individuals of African, Hispanic/Latino, and European ancestry in the US and of Finnish ancestry in Finland (a population isolate). For each study, we performed genotype imputation by using the genetic variants present on the Illumina Core, OmniExpress, MEGA, and Omni 2.5M arrays with the 1000G, HRC, and TOPMed imputation reference panels. Using the Omni 2.5M array and the TOPMed panel, ≥90% of bi-allelic single-nucleotide variants (SNVs) are well imputed (r2 > 0.8) down to minor-allele frequencies (MAFs) of 0.14% in African, 0.11% in Hispanic/Latino, 0.35% in European, and 0.85% in Finnish ancestries. There was little difference in TOPMed-based imputation quality among the arrays with >700k variants. Individual-level imputation quality varied widely between and within the three US studies. Imputation quality also varied across genomic regions, producing regions where even common (MAF > 5%) variants were consistently not well imputed across ancestries. The extent to which array genotyping and imputation can approximate WGS therefore depends on reference panel, genotype array, sample ancestry, and genomic location. Imputation quality by variant or genomic region can be queried with our new tool, RsqBrowser, now deployed on the Michigan Imputation Server. This manuscript presents—across multiple ancestries, genotype arrays, and imputation reference panels—the minor-allele frequency thresholds above which array genotyping and imputation accurately capture genetic variants genotyped with deep whole-genome sequencing. [ABSTRACT FROM AUTHOR]

Background: The decreasing cost of DNA sequencing has led to a great increase in our knowledge about genetic variation. While population-scale projects bring important insight into genotype–phenotype relationships, the cost of performing whole-genome sequencing on large samples is still prohibitive. In-silico genotype imputation coupled with genotyping-by-arrays is a cost-effective and accurate alternative for genotyping of common and uncommon variants. Imputation methods compare the genotypes of the typed variants with the large population-specific reference panels and estimate the genotypes of untyped variants by making use of the linkage disequilibrium patterns. Most accurate imputation methods are based on the Li–Stephens hidden Markov model, HMM, that treats the sequence of each chromosome as a mosaic of the haplotypes from the reference panel. Results: Here we assess the accuracy of vicinity-based HMMs, where each untyped variant is imputed using the typed variants in a small window around itself (as small as 1 centimorgan). Locality-based imputation is used recently by machine learning-based genotype imputation approaches. We assess how the parameters of the vicinity-based HMMs impact the imputation accuracy in a comprehensive set of benchmarks and show that vicinity-based HMMs can accurately impute common and uncommon variants. Conclusions: Our results indicate that locality-based imputation models can be effectively used for genotype imputation. The parameter settings that we identified can be used in future methods and vicinity-based HMMs can be used for re-structuring and parallelizing new imputation methods. The source code for the vicinity-based HMM implementations is publicly available at https://github.com/harmancilab/LoHaMMer. [ABSTRACT FROM AUTHOR]

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Trachinotus ovatus is one of the most commercial species in marine aquaculture, making it crucial to investigate the molecular basis underlying its growth-related traits, such as total length (TL). This study had two main objectives: first, to evaluate the influence of genotype imputation on the detection power of genome-wide association studies (GWAS); and second, to screen potential candidate genes affecting growth-related traits in T. ovatus. In this study, a high-quality haplotype reference panel was developed using high coverage resequencing data from 199 individuals representing different populations. The genomic variant dataset was obtained by imputing the low coverage whole genome sequencing (lcWGS) data of 716 individuals using Beagle, with the reference panel. The average imputation accuracy was 0.80, which was influenced by the minor allele frequency (MAF). GWAS was then performed to detect single nucleotide polymorphisms (SNPs) and genes potentially associated with TL, using both the imputed and lcWGS datasets. The lcWGS and imputation-based GWAS identified 53 and 170 SNPs, annotating 58 and 111 candidate genes, respectively. Of which, 32 genes were shared between both approaches, including genes such as prex1 and ralgps2 , which are related to TL. Additionally, several new genes, including mybph , atp5f1d , and ankrd24 , were uniquely identified in the imputation-based GWAS. These results demonstrate a clear advantage combining genotype imputation with GWAS for lcWGS data. The approach provides an economically efficient method for expanding the available genotypic information in genomic evaluations, and also provided potential markers for selective breeding in T. ovatus. • We constructed a reference haplotype panel for genotyping imputation in golden pompano. • The imputation accuracy was 0.80 and affected by the minor allele frequency. • The imputation-based whole-genome association study identified more markers. • We revealed several potential genes related to total length of the golden pompano. [ABSTRACT FROM AUTHOR]

Advancements in sequencing technologies have facilitated low-coverage whole-genome sequencing (lcWGS) for detecting millions of single nucleotide polymorphisms (SNPs) in large populations at low cost. The effectiveness of this approach relies on genotype imputation following lcWGS. While several imputation methods have been applied in human and other terrestrial animals, their performance in aquaculture organisms, particularly mollusks, remains unassessed. Mollusks contribute 70% of mariculture production in China and are characterized by high genetic polymorphism. In this study, we aimed to optimize reference panel construction and evaluate imputation strategies for SNP detection in two scallops, Chlamys farreri and Patinopecten yessoensis , both important bivalve species. High-coverage whole-genome sequencing (hcWGS) data were used to construct haplotype reference panels, and three imputation methods were applied: Beagle5.4, GLIMPSE2, and QUILT. QUILT outperformed other methods, achieving imputation quality scores (IQS) exceeding 0.930 in C. farreri and 0.946 in P. yessoensis , and yielded the highest number of imputed loci in both species. Furthermore, panel construction was optimized by adjusting for genotype quality (GQ) and minor allele frequency (MAF); we determined that a GQ threshold > 20 and an MAF threshold > 0.01 were optimal. We observed that imputation quality improved with increased sequencing depth, plateauing at 0.5×, and varied with sample size depending on the imputation method. A negative correlation was observed between imputation quality and the ratio of non-synonymous to synonymous substitution rates (Ka/Ks) across chromosomal segments, highlighting the influence of selection pressure on imputation efficacy. Collectively, our findings provide an optimized framework for SNP genotyping using lcWGS in scallops, with implications for aquaculture breeding and genetic research. • Genotype filtering with GQ > 20 and MAF > 0.01 constructed the best haplotype reference panel in scallops. • Imputation accuracies reached 0.93 in C. farreri and 0.95 in P. yessoensis with 0.5× sequencing depth. • QUILT presented the best genotype imputation quality compared to Beagle5.4 and GLIMPSE2. • A negative correlation was observed between imputation quality and the Ka/Ks ratio across different segments of scallop genomes. [ABSTRACT FROM AUTHOR]

• Uncertainty of missing genotypes in low-coverage sequencing complicates genotype imputation. • Using key progenitors as reference panel yields the highest accuracy of imputation. • The imputed sequencing data improve the accuracy of genomic prediction in pigs. • Novel candidate genes are identified in the imputed sequencing data of pigs. • The low-coverage whole-genome sequencing is a cost-effective alternative to genotyping. The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31–1.04% compared to the chip data, and by 0.7–1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2 , MBD5 , ORC4 and ACVR2A ; number of piglets born healthy - IKBKE ; total litter weight of piglets born alive - HSPA13 and CPA1 ; gestation length - GTF2H5 , ITGAV , NFE2L2 , CALCRL , ITGA4 , STAT1 , HOXD10 , MSTN , COL5A2 and STAT4. With the exception of EPC2 , ORC4 , ACVR2A and MSTN , others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry. [ABSTRACT FROM AUTHOR]

Genotype imputation is an integral tool in genome-wide association studies, in which it facilitates meta-analysis, increases power, and enables fine-mapping. With the increasing availability of whole-genome-sequence datasets, investigators have access to a multitude of reference-panel choices for genotype imputation. In principle, combining all sequenced whole genomes into a single large panel would provide the best imputation performance, but this is often cumbersome or impossible due to privacy restrictions. Here, we describe meta-imputation, a method that allows imputation results generated using different reference panels to be combined into a consensus imputed dataset. Our meta-imputation method requires small changes to the output of existing imputation tools to produce necessary inputs, which are then combined using dynamically estimated weights that are tailored to each individual and genome segment. In the scenarios we examined, the method consistently outperforms imputation using a single reference panel and achieves accuracy comparable to imputation using a combined reference panel. [ABSTRACT FROM AUTHOR]

Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F2 population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future. [ABSTRACT FROM AUTHOR]

Abstract Background Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. Results To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. Conclusions These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.