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Combinatorial transcriptional profiling of mouse and human enteric neurons identifies shared and disparate subtypes in situ

ABSTRACT: Combinatorial transcriptional profiling of mouse and human enteric neurons identifies shared and disparate subtypes in situ

PROVIDER: PRJNA641717 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12083600.fastq.gz	Fastqsanger.gz
	SRR12083700.fastq.gz	Fastqsanger.gz
	SRR12083800.fastq.gz	Fastqsanger.gz
	SRR12083900.fastq.gz	Fastqsanger.gz
	SRR12084000.fastq.gz	Fastqsanger.gz

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Project description:300,000 SNPs genotyped (Illumina CytoSNP12v2 array) in a family with unexplained inherited kidney disease. Analysed using parametric and Combinatorial Conflicting Homozygosity analysis, as described in Levine et al. BMC Genomics 2015. Abstract: Background: The ability to identify regions of the genome inherited with a dominant trait in one or more families has become increasingly valuable with the wide availability of next generationhigh throughput sequencing technology. While a number of methods exist for mapping of homozygous variants segregating with recessive traits in consanguineous families, dominant conditions are conventionally analysed by by linkage analysis, which requires computationally demanding haplotype reconstruction from marker genotypes and, even using advanced parallel approximation implementations, can take substantial time. In addition, linkage analysis lacks sensitivity in the presence of phenocopies (individuals sharing the trait but not the genetic variant responsible). Combinatorial Conflicting Homozygosity (CCH) analysis uses bi-allelic single nucleotide polymorphism (SNP) marker genotypes to identify genetic loci within which consecutive markers are not homozygous for different alleles. This allows inference of identical by descent (IBD) inheritance of a haplotype among a set or subsets of related or unrelated individuals. Results: A single genome-wide conflicting homozygosity analysis takes <3 seconds and parallelisation permits multiple combinations of subsets of individuals to be analysed quickly. Analysis of unrelated individuals demonstrated that in the absence of IBD inheritance, runs of no- CH exceeding 4 cM are not observed. At this threshold, CCH is >97% sensitive and specific for IBD regions within a pedigree exceeding this length and was able to identify the locus responsible for a dominantly inherited kidney disease in a Turkish Cypriot family in which 6 six out 17 affected individuals were phenocopies. It also revealed shared ancestry at the disease-linked locus among affected individuals from two different Cypriot populations. Conclusions: CCH does not require computationally demanding haplotype reconstruction and can detect regions of shared inheritance of a haplotype among subsets of related or unrelated individuals directly from SNP genotype data. In contrast to parametric linkage allowing for phenocopies, CCH directly provides the exact number and identity of individuals sharing each locus. CCH can also identify regions of shared ancestry among ostensibly unrelated individuals who share a trait. CCH is implemented in Python and is freely available (as source code) for download at http://sourceforge.net/projects/cchsnp/. Linkage and identity-by-descent studies in a single family with 17 individuals affected with kidney disease

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Combinatorial transcriptional profiling of mouse and human enteric neurons identifies shared and disparate subtypes in situ

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