Title Page
Abstract
Contents
Chapter 1. Literature Review 11
1.1. Genetic variation and population genetics 12
1.1.1. Classification of genetic variation 12
1.1.2. Analysis related to population genetics 14
1.2. Use of transcriptome data 16
1.3. Breed used in study 18
Chapter 2. The idiosyncratic genome of Korean long-tailed chicken as a valuable genetic resource for long-tailed trait 19
2.1. Abstract 20
2.2. Introduction 21
2.3. Materials and Methods 25
2.3.1. Sample collection and Sequencing 25
2.3.2. Re-sequencing and Variant calling 25
2.3.3. Population differentiation, structure and genetic diversity 27
2.3.4. Phylogenetic reconstruction and Demographic inference 27
2.3.5. Detection of selective sweep regions in Korean long-tailed chicken 28
2.3.6. Detection of PBS-based selection signatures in Korean long-tailed chicken 29
2.3.7. CNV calling and CNVR detection 30
2.3.8. Population differentiation based on CNVR 30
2.3.9. Annotation of genes and Functional enrichment analysis 31
2.4. Results 32
2.4.1. Whole genome sequencing of Korean long-tailed chickens 32
2.4.2. The genetic profile of Korean long-tailed chicken 34
2.4.3. The genetic relationship of Korean long-tailed chicken with other chicken breeds 38
2.4.4. Signatures of selective sweep associated with the long-tail phenotype 44
2.4.5. CNV calling and Identification of CNVRs 60
2.4.6. Detection of population-differentiated CNVRs and their functional annotation 60
2.4.7. CNVRs in feather keratin 1-like enriched region 66
2.4.8. Candidate genes responsible for feather formation and development 70
2.5. Discussion 73
Chapter 3. The transcriptomic blueprint of molt in rooster using various tissues from Ginkkoridak (Korean long-tailed chicken) 80
3.1. Abstract 81
3.2. Introduction 83
3.3. Materials and Methods 87
3.3.1. Experimental design and sampling process 87
3.3.2. RNA isolation and quality assessment 87
3.3.3. Library preparation and sequencing of mRNA 88
3.3.4. Raw reads pre-processing of mRNA 88
3.3.5. Differential gene expression 89
3.3.6. Differential transcript usage 90
3.3.7. Tissues clustering 90
3.3.8. Small RNA isolation and quality assessment 91
3.3.9. Library preparation and sequencing of miRNA 91
3.3.10. Raw reads pre-processing of miRNA 91
3.3.11. Prediction of target genes and identification of miRNA-mRNA pairs 92
3.3.12. Integration of the results and functional enrichment analysis 92
3.4. Results 94
3.4.1. Overall gene expression changes during molt 94
3.4.2. Genes altered by molt across multiple tissues 104
3.4.3. Clustering of tissues based on the samples DGE and DTU profiles 107
3.4.4. The impact of molt in male and female blood messenger RNA 108
3.4.5. The impact of molt on male tissue clusters 111
3.4.6. Identification of differentially expressed micro RNAs associated with molt in blood 121
3.4.7. Integration of DEM and DGE data in blood 128
3.4.8. Data integration: candidate biomarkers of molt 134
3.5. Discussion 138
General discussion 146
References 148
요약(국문 초록) 166
Table 2.1. Autosomal distribution and number of SNPs. 33
Table 2.2. 4-population test (qpDstat) 42
Table 2.3. Functional enrichment analysis of genes overlapped with candidate selective sweep region. 47
Table 2.4. Summary of feather-associated candidate regions identified from XP-EHH between LT and NLT. Max XP-EHH means a positive maximum XP-EHH score of all SNPs in each window. 48
Table 2.5. Summary of non-synonymous polymorphisms in SEMA5A and ADAM12 genes. 52
Table 2.6. Summary of candidate selection region (top 0.1%) from PBS analysis. 56
Table 2.7. Summary of terms associated with feather formation and composition. 63
Table 3.1. Top 5 DGE (under and over-expressed) and DTU genes across the 23 Ginkkoridak tissues. 97
Table 3.2. Genes which expression pattern (DGE or DTU) has been altered by molt in at least one tissue among the epithelial cluster and whose function was putatively related to appendages. 115
Table 3.3. Overall mapping data of miRNA in Ginkkoridak blood. 123
Table 3.4. Top 5 significant over and under expressed miRNAs in male and fe male on before molt and during molt. 127
Table 3.5. Summary of candidate biomarkers of molt. 136
Figure 2.1. Photographs of each breeds used in this study. 22
Figure 2.2. Population structure, relationship and genetic diversity of Korean long-tailed chicken (KLC) and other breeds. 35
Figure 2.3. (a) Principal Component analysis based on Eigenvector 2 and 3. (b) Principal Component analysis based on Eigenvector 3 and 4. (c) The percentage of... 36
Figure 2.4. Genetic history of chicken breeds. 39
Figure 2.5. The maximum likelihood tree of the 15 chicken breeds and its residual matrix with effective population size of chicken breeds. 40
Figure 2.6. (a) Distribution plot of XP-EHH raw score for LT and NLT comparison. (b) Histogram of SNP density in non-overlapping 50 kb window. 46
Figure 2.7. Signatures of selective sweep on ADAM12. 50
Figure 2.8. Signatures of selective sweep on SEMA5A. 54
Figure 2.9. PBS-based selection signatures in Korean long-tailed chicken (KLC). 59
Figure 2.10. Population differentiation and functional annotation based on CNVR. 61
Figure 2.11. Estimated copy numbers of CNVRs with different copy numbers between populations and depth of coverage in feather keratin 1-like enriched region. 67
Figure 2.12. (a - h) Box plot of copy number difference between breeds in NLT. The centerline and X mark in the box represent median and mean, respectively. 69
Figure 2.13. Estimated copy numbers of CNVRs overlapping to genes with different copy numbers between LT and NLT. 71
Figure 2.14. Box plot of copy number difference between LT and NLT (left, Top) and breeds in NLT (right, bottom). The centerline and X mark in the box represent... 72
Figure 3.1. Ginkkoridak molt expression body map. 95
Figure 3.2. Heatmap of the genes in DGE or DTU in a least four different tissues. 105
Figure 3.3. Effect of molt in male and females blood. 109
Figure 3.4. Functional enrichment analysis for each of the tissue clusters. 113
Figure 3.5. Identification and profile of miRNA in blood. 125
Figure 3.6. Data integration of miRNA and mRNA in blood. 130
Figure 3.7. Categorization of major GO terms and KEGG pathways. 133
Figure 3.8. Candidate biomarkers of molt. 135