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Genetic diversity analysis of highly incomplete SNP genotype data with imputations: an empirical assessment.

ABSTRACT: Genotyping by sequencing (GBS) recently has emerged as a promising genomic approach for assessing genetic diversity on a genome-wide scale. However, concerns are not lacking about the uniquely large unbalance in GBS genotype data. Although some genotype imputation has been proposed to infer missing observations, little is known about the reliability of a genetic diversity analysis of GBS data, with up to 90% of observations missing. Here we performed an empirical assessment of accuracy in genetic diversity analysis of highly incomplete single nucleotide polymorphism genotypes with imputations. Three large single-nucleotide polymorphism genotype data sets for corn, wheat, and rice were acquired, and missing data with up to 90% of missing observations were randomly generated and then imputed for missing genotypes with three map-independent imputation methods. Estimating heterozygosity and inbreeding coefficient from original, missing, and imputed data revealed variable patterns of bias from assessed levels of missingness and genotype imputation, but the estimation biases were smaller for missing data without genotype imputation. The estimates of genetic differentiation were rather robust up to 90% of missing observations but became substantially biased when missing genotypes were imputed. The estimates of topology accuracy for four representative samples of interested groups generally were reduced with increased levels of missing genotypes. Probabilistic principal component analysis based imputation performed better in terms of topology accuracy than those analyses of missing data without genotype imputation. These findings are not only significant for understanding the reliability of the genetic diversity analysis with respect to large missing data and genotype imputation but also are instructive for performing a proper genetic diversity analysis of highly incomplete GBS or other genotype data.

SUBMITTER: Fu YB

PROVIDER: S-EPMC4025488 | biostudies-other | 2014 May

REPOSITORIES: biostudies-other

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Genetic diversity analysis of highly incomplete SNP genotype data with imputations: an empirical assessment.

Fu Yong-Bi YB

G3 (Bethesda, Md.) 20140313 5

Genotyping by sequencing (GBS) recently has emerged as a promising genomic approach for assessing genetic diversity on a genome-wide scale. However, concerns are not lacking about the uniquely large unbalance in GBS genotype data. Although some genotype imputation has been proposed to infer missing observations, little is known about the reliability of a genetic diversity analysis of GBS data, with up to 90% of observations missing. Here we performed an empirical assessment of accuracy in geneti ...[more]

PMID: 24626289

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Project description:BACKGROUND: In families segregating a monogenic genetic disorder with a single disease gene introduction, patients share a mutation-carrying chromosomal interval with identity-by-descent (IBD). Such a shared chromosomal interval or haplotype, surrounding the actual pathogenic mutation, is typically detected and defined by multipoint linkage and phased haplotype analysis using microsatellite or SNP genotype data. High-density SNP genotype data presents a computational challenge for conventional genetic analyses. A novel non-parametric method termed Homozygosity Haplotype (HH) was recently proposed for the genome-wide search of the autosomal segments shared among patients using high density SNP genotype data. METHODOLOGY/PRINCIPAL FINDINGS: The applicability and the effectiveness of HH in identifying the potential linkage of disease causative gene with high-density SNP genotype data were studied with a series of monogenic disorders ascertained in eastern Canadian populations. The HH approach was validated using the genotypes of patients from a family affected with a rare autosomal dominant disease Schnyder crystalline corneal dystrophy. HH accurately detected the approximately 1 Mb genomic interval encompassing the causative gene UBIAD1 using the genotypes of only four affected subjects. The successful application of HH to identify the potential linkage for a family with pericentral retinal disorder indicates that HH can be applied to perform family-based association analysis by treating affected and unaffected family members as cases and controls respectively. A new strategy for the genome-wide screening of known causative genes or loci with HH was proposed, as shown the applications to a myoclonus dystonia and a renal failure cohort. CONCLUSIONS/SIGNIFICANCE: Our study of the HH approach demonstrates that HH is very efficient and effective in identifying potential disease linked region. HH has the potential to be used as an efficient alternative approach to sequencing or microsatellite-based fine mapping for screening the known causative genes in genetic disease study.

Project description:Coxsackievirus A8 (CV-A8) is one of the pathogens associated with hand, foot and mouth disease (HFMD) and herpangina (HA), occasionally leading to severe neurological disorders such as acute flaccid paralysis (AFP). Only one study aimed at CV-A8 has been published to date, and only 12 whole-genome sequences are publicly available. In this study, complete genome sequences from 11 CV-A8 strains isolated from HFMD patients in extensive regions from China between 2013 and 2018 were determined, and all sequences from GenBank were retrieved. A phylogenetic analysis based on a total of 34 complete VP1 sequences of CV-A8 revealed five genotypes: A, B, C, D and E. The newly emerging genotype E presented a highly phylogenetic divergence compared with the other genotypes and was composed of the majority of the strains sequenced in this study. Markov chain Monte Carlo (MCMC) analysis revealed that genotype E has been evolving for nearly a century and somehow arose in approximately 2010. The Bayesian skyline plot showed that the population size of CV-A8 has experienced three dynamic fluctuations since 2001. Amino acid residues of VP1100N, 103Y, 240T and 241V, which were embedded in the potential capsid loops of genotype E, might enhance genotype E adaption to the human hosts. The CV-A8 whole genomes displayed significant intra-genotypic genetic diversity in the non-capsid region, and a total of six recombinant lineages were detected. The Chinese viruses from genotype E might have emerged recently from recombining with European CV-A6 strains. CV-A8 is a less important HFMD pathogen, and the capsid gene diversity and non-capsid recombination variety observed in CV-A8 strains indicated that the constant generation of deleterious genomes and a constant selection pressure against these deleterious mutations is still ongoing within CV-A8 quasispecies. It is possible that CV-A8 could become an important pathogen in the HFMD spectrum in the future. Further surveillance of CV-A8 is greatly needed.

Project description:BackgroundHigh-throughput genotyping of single nucleotide polymorphisms (SNPs) generates large amounts of data. In many SNP genotyping assays, the genotype assignment is based on scatter plots of signals corresponding to the two SNP alleles. In a robust assay the three clusters that define the genotypes are well separated and the distances between the data points within a cluster are short. "Silhouettes" is a graphical aid for interpretation and validation of data clusters that provides a measure of how well a data point was classified when it was assigned to a cluster. Thus "Silhouettes" can potentially be used as a quality measure for SNP genotyping results and for objective comparison of the performance of SNP assays at different circumstances.ResultsWe created a program (ClusterA) for calculating "Silhouette scores", and applied it to assess the quality of SNP genotype clusters obtained by single nucleotide primer extension ("minisequencing") in the Tag-microarray format. A Silhouette score condenses the quality of the genotype assignment for each SNP assay into a single numeric value, which ranges from 1.0, when the genotype assignment is unequivocal, down to -1.0, when the genotype assignment has been arbitrary. In the present study we applied Silhouette scores to compare the performance of four DNA polymerases in our minisequencing system by analyzing 26 SNPs in both DNA polarities in 16 DNA samples. We found Silhouettes to provide a relevant measure for the quality of SNP assays at different reaction conditions, illustrated by the four DNA polymerases here. According to our result, the genotypes can be unequivocally assigned without manual inspection when the Silhouette score for a SNP assay is > 0.65. All four DNA polymerases performed satisfactorily in our Tag-array minisequencing system.Conclusion"Silhouette scores" for assessing the quality of SNP genotyping clusters is convenient for evaluating the quality of SNP genotype assignment, and provides an objective, numeric measure for comparing the performance of SNP assays. The program we created for calculating Silhouette scores is freely available, and can be used for quality assessment of the results from all genotyping systems, where the genotypes are assigned by cluster analysis using scatter plots.

Dataset Information

Genetic diversity analysis of highly incomplete SNP genotype data with imputations: an empirical assessment.

Publications

Genetic diversity analysis of highly incomplete SNP genotype data with imputations: an empirical assessment.

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