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Whole-genome definitive haplotypes of SNPs and CNVs determined by genotyping duplicated haploid genomes

ABSTRACT: Copy number variations (CNVs) constitute the largest portion of the human genome variation. We determined a genome-wide high resolution SNP/CNV haplotype structure of Asians, by analyzing a collection of complete hydatidiform moles (CHMs) of Japanese, using high-density DNA arrays. CHMs are tissues carrying duplicated haploid genomes derived from single sperms, and are suitable material for the detection of CNVs, because they are expected to reveal greater signal to noise ratio in hybridization experiments. Also, the absence of heterozygosity ensures straightforward CNV interpretation without being bothered by overlapping CNV segments. We genotyped 100 CHM genomes using Affymetrix SNP 6.0 and Illumina 1M-duo, created a definitive haplotype map including 1.7 million SNPs and 2339 CNV region (CNVR) that is presented as D-HaploDB Phase 4.1.

ORGANISM(S): Homo sapiens

PROVIDER: GSE54948 | GEO | 2014/02/13

SECONDARY ACCESSION(S): PRJNA238130

REPOSITORIES: GEO

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Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken. In total, 475 birds (203 and 272 individuals from the lean and fat lines, respectively) from the 11th generation population of Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) were used. These 475 birds were genotyped by the chicken 60k SNP chip and PennCNV method were used to perform genome-wide CNV detection.

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Whole-genome definitive haplotypes of SNPs and CNVs determined by genotyping duplicated haploid genomes

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