Dataset Information

Accuracy of Imputation of Microsatellite Markers from BovineSNP50 and BovineHD BeadChip in Hanwoo Population of Korea.

ABSTRACT: Until now microsatellite (MS) have been a popular choice of markers for parentage verification. Recently many countries have moved or are in process of moving from MS markers to single nucleotide polymorphism (SNP) markers for parentage testing. FAO-ISAG has also come up with a panel of 200 SNPs to replace the use of MS markers in parentage verification. However, in many countries most of the animals were genotyped by MS markers till now and the sudden shift to SNP markers will render the data of those animals useless. As National Institute of Animal Science in South Korea plans to move from standard ISAG recommended MS markers to SNPs, it faces the dilemma of exclusion of old animals that were genotyped by MS markers. Thus to facilitate this shift from MS to SNPs, such that the existing animals with MS data could still be used for parentage verification, this study was performed. In the current study we performed imputation of MS markers from the SNPs in the 500-kb region of the MS marker on either side. This method will provide an easy option for the labs to combine the data from the old and the current set of animals. It will be a cost efficient replacement of genotyping with the additional markers. We used 1,480 Hanwoo animals with both the MS data and SNP data to impute in the validation animals. We also compared the imputation accuracy between BovineSNP50 and BovineHD BeadChip. In our study the genotype concordance of 40% and 43% was observed in the BovineSNP50 and BovineHD BeadChip respectively.

SUBMITTER: Sharma A

PROVIDER: S-EPMC5903063 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Accuracy of Imputation of Microsatellite Markers from BovineSNP50 and BovineHD BeadChip in Hanwoo Population of Korea.

Sharma Aditi A Park Jong-Eun JE Park Byungho B Park Mi-Na MN Roh Seung-Hee SH Jung Woo-Young WY Lee Seung-Hwan SH Chai Han-Ha HH Chang Gul-Won GW Cho Yong-Min YM Lim Dajeong D

Genomics & informatics 20180330 1

Until now microsatellite (MS) have been a popular choice of markers for parentage verification. Recently many countries have moved or are in process of moving from MS markers to single nucleotide polymorphism (SNP) markers for parentage testing. FAO-ISAG has also come up with a panel of 200 SNPs to replace the use of MS markers in parentage verification. However, in many countries most of the animals were genotyped by MS markers till now and the sudden shift to SNP markers will render the data o ...[more]

PMID: 29618182

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Project description:BackgroundAlthough high-throughput genotyping arrays have made whole-genome association studies (WGAS) feasible, only a small proportion of SNPs in the human genome are actually surveyed in such studies. In addition, various SNP arrays assay different sets of SNPs, which leads to challenges in comparing results and merging data for meta-analyses. Genome-wide imputation of untyped markers allows us to address these issues in a direct fashion.Methods384 Caucasian American liver donors were genotyped using Illumina 650Y (Ilmn650Y) arrays, from which we also derived genotypes from the Ilmn317K array. On these data, we compared two imputation methods: MACH and BEAGLE. We imputed 2.5 million HapMap Release22 SNPs, and conducted GWAS on approximately 40,000 liver mRNA expression traits (eQTL analysis). In addition, 200 Caucasian American and 200 African American subjects were genotyped using the Affymetrix 500 K array plus a custom 164 K fill-in chip. We then imputed the HapMap SNPs and quantified the accuracy by randomly masking observed SNPs.ResultsMACH and BEAGLE perform similarly with respect to imputation accuracy. The Ilmn650Y results in excellent imputation performance, and it outperforms Affx500K or Ilmn317K sets. For Caucasian Americans, 90% of the HapMap SNPs were imputed at 98% accuracy. As expected, imputation of poorly tagged SNPs (untyped SNPs in weak LD with typed markers) was not as successful. It was more challenging to impute genotypes in the African American population, given (1) shorter LD blocks and (2) admixture with Caucasian populations in this population. To address issue (2), we pooled HapMap CEU and YRI data as an imputation reference set, which greatly improved overall performance. The approximate 40,000 phenotypes scored in these populations provide a path to determine empirically how the power to detect associations is affected by the imputation procedures. That is, at a fixed false discovery rate, the number of cis-eQTL discoveries detected by various methods can be interpreted as their relative statistical power in the GWAS. In this study, we find that imputation offer modest additional power (by 4%) on top of either Ilmn317K or Ilmn650Y, much less than the power gain from Ilmn317K to Ilmn650Y (13%).ConclusionCurrent algorithms can accurately impute genotypes for untyped markers, which enables researchers to pool data between studies conducted using different SNP sets. While genotyping itself results in a small error rate (e.g. 0.5%), imputing genotypes is surprisingly accurate. We found that dense marker sets (e.g. Ilmn650Y) outperform sparser ones (e.g. Ilmn317K) in terms of imputation yield and accuracy. We also noticed it was harder to impute genotypes for African American samples, partially due to population admixture, although using a pooled reference boosts performance. Interestingly, GWAS carried out using imputed genotypes only slightly increased power on top of assayed SNPs. The reason is likely due to adding more markers via imputation only results in modest gain in genetic coverage, but worsens the multiple testing penalties. Furthermore, cis-eQTL mapping using dense SNP set derived from imputation achieves great resolution, and locate associate peak closer to causal variants than conventional approach.

Project description:Genotype imputation is essential for enhancing the power of association-mapping 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 whole-genome 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 (R 2 = 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 (R 2 = 0.84) and UKB (R 2 = 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.

Project description:Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of populations with non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative contains a substantial number of admixed African-ancestry and Hispanic/Latino samples to impute these populations with nearly the same accuracy as European-ancestry cohorts. However, imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we curated genome-wide array data from 23 publications published between 2008 to 2021. In total, we imputed over 43k individuals across 123 populations around the world. We identified a number of populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for 1-5% alleles in Saudi Arabians (N=1061), Vietnamese (N=1264), Thai (N=2435), and Papua New Guineans (N=776) were 0.79, 0.78, 0.76, and 0.62, respectively. In contrast, the mean Rsq ranged from 0.90 to 0.93 for comparable European populations matched in sample size and SNP content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European reference increased, as predicted. Further analysis using sequencing data as ground truth suggested that imputation software may over-estimate imputation accuracy for non-European populations than European populations, suggesting further disparity between populations. Using 1496 whole genome sequenced individuals from Taiwan Biobank as a reference, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, which can combine results from TOPMed with smaller population-specific reference panels. We found that meta-imputation in this design did not improve Rsq genome-wide. Taken together, our analysis suggests that with the current size of alternative reference panels, meta-imputation alone cannot improve imputation efficacy for underrepresented cohorts and we must ultimately strive to increase diversity and size to promote equity within genetics research.

Dataset Information

Accuracy of Imputation of Microsatellite Markers from BovineSNP50 and BovineHD BeadChip in Hanwoo Population of Korea.

Publications

Accuracy of Imputation of Microsatellite Markers from BovineSNP50 and BovineHD BeadChip in Hanwoo Population of Korea.

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