Dataset Information

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State.

ABSTRACT: The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree.

SUBMITTER: Graffelman J

PROVIDER: S-EPMC6491861 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State.

Graffelman Jan J Galván Femenía Iván I de Cid Rafael R Barceló Vidal Carles C

Frontiers in genetics 20190424

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In th ...[more]

PMID: 31068965

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Project description:BACKGROUND:Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2. RESULTS:To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations. CONCLUSIONS:Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.

Project description:Background and aimLumpy skin disease (LSD) and sheep pox are economically important Capripoxvirus-induced diseases of cattle and sheep, respectively. Despite the extensive vaccination program adopted by Egyptian veterinary authorities, LSD and sheep pox are still prevalent and spread throughout the whole country. The current study was designed for molecular characterization and phylogenetic analysis of LSD virus (LSDV) and Sheep pox virus (SPPV) recovered from field cases in Egypt along with vaccinal strains to assess their genetic relatedness.Materials and methodsSkin biopsies were collected from naturally infected cases of LSD in Ismailia (n=3 farms) and Beni-Suef (n=2 farms) Governorates and sheep pox in Beni-Suef (n=1 flock). Virus isolation was carried out on primary ovine fetal kidney and heart cell cultures. DNA was extracted from infected materials (skin lesions, infected cell cultures) as well as LSDV Neethling vaccine strain and Romanian SPPV vaccine strain. Polymerase chain reaction was performed using oligonucleotide primers targeting the entire open reading frame of G protein-coupled receptors (GPCR) gene and gene sequences were analyzed.ResultsVirus isolation on primary ovine fetal kidney and heart cell culture revealed a cytopathic effect at the third passage characterized by rounding of infected cells and margination of nuclear chromatin. Comparative sequence analysis of GPCR gene revealed that Egyptian LSDV isolated from Ismailia and Beni-Suef shared 99:100% nucleotide and amino acid (AA) identities with each other. In comparison to the vaccinal strains, Egyptian LSDV isolates shared 98:99 nucleotide and AA identities with LSDV Neethling vaccine strain and 93:94% with SPPV Romanian vaccine strain. No differences at the nucleotide or AAs were observed between the SPPV vaccine and virulent strains (100% identity). Phylogenetic analyses revealed that LSDV Neethling vaccine strain is more related to field Egyptian LSDV and clustered within the LSDV group while Romanian SPPV vaccine strain clustered in a separate clade with SPPV field isolates.ConclusionComparative sequencing and phylogenetic analyses of the GPCR gene reveal a minimal genetic variation between LSDV field isolates from different locations and a close relationship between virulent field strains and homologous vaccines.

Dataset Information

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State.

Publications

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State.

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