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Re-evaluating data quality of dog mitochondrial, Y chromosomal, and autosomal SNPs genotyped by SNP array.

ABSTRACT: Quality deficiencies in single nucleotide polymorphism (SNP) analyses have important implications. We used missingness rates to investigate the quality of a recently published dataset containing 424 mitochondrial, 211 Y chromosomal, and 160 432 autosomal SNPs generated by a semicustom Illumina SNP array from 5 392 dogs and 14 grey wolves. Overall, the individual missingness rate for mitochondrial SNPs was ~43.8%, with 980 (18.1%) individuals completely missing mitochondrial SNP genotyping (missingness rate=1). In males, the genotype missingness rate was ~28.8% for Y chromosomal SNPs, with 374 males recording rates above 0.96. These 374 males also exhibited completely failed mitochondrial SNPs genotyping, indicative of a batch effect. Individual missingness rates for autosomal markers were greater than zero, but less than 0.5. Neither mitochondrial nor Y chromosomal SNPs achieved complete genotyping (locus missingness rate=0), whereas 5.9% of autosomal SNPs had a locus missingness rate=1. The high missingness rates and possible batch effect show that caution and rigorous measures are vital when genotyping and analyzing SNP array data for domestic animals. Further improvements of these arrays will be helpful to future studies.

SUBMITTER: O Otecko N

PROVIDER: S-EPMC5359323 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Re-evaluating data quality of dog mitochondrial, Y chromosomal, and autosomal SNPs genotyped by SNP array.

O Otecko Newton N Peng Min-Sheng MS Yang He-Chuan HC Zhang Ya-Ping YP Wang Guo-Dong GD

Zoological research 20161101 6

Quality deficiencies in single nucleotide polymorphism (SNP) analyses have important implications. We used missingness rates to investigate the quality of a recently published dataset containing 424 mitochondrial, 211 Y chromosomal, and 160 432 autosomal SNPs generated by a semicustom Illumina SNP array from 5 392 dogs and 14 grey wolves. Overall, the individual missingness rate for mitochondrial SNPs was ~43.8%, with 980 (18.1%) individuals completely missing mitochondrial SNP genotyping (missi ...[more]

PMID: 28105800

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Project description:BackgroundPopulation genetic studies of dogs have so far mainly been based on analysis of mitochondrial DNA, describing only the history of female dogs. To get a picture of the male history, as well as a second independent marker, there is a need for studies of biallelic Y-chromosome polymorphisms. However, there are no biallelic polymorphisms reported, and only 3200 bp of non-repetitive dog Y-chromosome sequence deposited in GenBank, necessitating the identification of dog Y chromosome sequence and the search for polymorphisms therein. The genome has been only partially sequenced for one male dog, disallowing mapping of the sequence into specific chromosomes. However, by comparing the male genome sequence to the complete female dog genome sequence, candidate Y-chromosome sequence may be identified by exclusion.ResultsThe male dog genome sequence was analysed by Blast search against the human genome to identify sequences with a best match to the human Y chromosome and to the female dog genome to identify those absent in the female genome. Candidate sequences were then tested for male specificity by PCR of five male and five female dogs. 32 sequences from the male genome, with a total length of 24 kbp, were identified as male specific, based on a match to the human Y chromosome, absence in the female dog genome and male specific PCR results. 14437 bp were then sequenced for 10 male dogs originating from Europe, Southwest Asia, Siberia, East Asia, Africa and America. Nine haplotypes were found, which were defined by 14 substitutions. The genetic distance between the haplotypes indicates that they originate from at least five wolf haplotypes. There was no obvious trend in the geographic distribution of the haplotypes.ConclusionWe have identified 24159 bp of dog Y-chromosome sequence to be used for population genetic studies. We sequenced 14437 bp in a worldwide collection of dogs, identifying 14 SNPs for future SNP analyses, and giving a first description of the dog Y-chromosome phylogeny.

Project description:BackgroundCopy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for Affymetrix SNP chips to estimate copy number: Affymetrix Power Tools (APT), Aroma.Affymetrix, PennCNV and CRLMM. Our evaluation used 1,418 GENOA samples that were genotyped on the Affymetrix Genome-Wide Human SNP Array 6.0. We compared bias and variance in the locus-level copy number data, the concordance amongst regions of copy number gains/deletions and the false-positive rate amongst deleted segments.ResultsAPT had median locus-level copy numbers closest to a value of two, whereas PennCNV and Aroma.Affymetrix had the smallest variability associated with the median copy number. Of those evaluated, only PennCNV provides copy number specific quality-control metrics and identified 136 poor CNV samples. Regions of copy number variation (CNV) were detected using the hidden Markov models provided within PennCNV and CRLMM/VanillaIce. PennCNV detected more CNVs than CRLMM/VanillaIce; the median number of CNVs detected per sample was 39 and 30, respectively. PennCNV detected most of the regions that CRLMM/VanillaIce did as well as additional CNV regions. The median concordance between PennCNV and CRLMM/VanillaIce was 47.9% for duplications and 51.5% for deletions. The estimated false-positive rate associated with deletions was similar for PennCNV and CRLMM/VanillaIce.ConclusionsIf the objective is to perform statistical tests on the locus-level copy number data, our empirical results suggest that PennCNV or Aroma.Affymetrix is optimal. If the objective is to perform statistical tests on the summarized segmented data then PennCNV would be preferred over CRLMM/VanillaIce. Specifically, PennCNV allows the analyst to estimate locus-level copy number, perform segmentation and evaluate CNV-specific quality-control metrics within a single software package. PennCNV has relatively small bias, small variability and detects more regions while maintaining a similar estimated false-positive rate as CRLMM/VanillaIce. More generally, we advocate that software developers need to provide guidance with respect to evaluating and choosing optimal settings in order to obtain optimal results for an individual dataset. Until such guidance exists, we recommend trying multiple algorithms, evaluating concordance/discordance and subsequently consider the union of regions for downstream association tests.

Dataset Information

Re-evaluating data quality of dog mitochondrial, Y chromosomal, and autosomal SNPs genotyped by SNP array.

Publications

Re-evaluating data quality of dog mitochondrial, Y chromosomal, and autosomal SNPs genotyped by SNP array.

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