• Asian-Australasian Journal of Animal Sciences
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• v.24 no.9
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• pp.1184-1191
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• 2011

We attempted to generate a linkage map using Illumina Porcine 60K SNP Beadchip genotypes of the $F_2$ offspring from Korean native pig (KNP) crossed with Yorkshire (YS) pig, and to identify quantitative trait loci (QTL) using the line-cross model. Among the genotype information of the 62,136 SNPs obtained from the high-density SNP analysis, 45,308 SNPs were used to select informative markers with allelic frequencies >0.7 between the KNP (n = 16) and YS (n = 8) F0 animals. Of the selected SNP markers, a final set of 500 SNPs with polymorphic information contents (PIC) values of >0.300 in the $F_2$ groups (n = 252) was used for detection of thirty meat quality-related QTL on chromosomes at the 5% significance level and 10 QTL at the 1% significance level. The QTL for crude protein were detected on SSC2, SSC3, SSC6, SSC9 and SSC12; for intramuscular fat and marbling on SSC2, SSC8, SSC12, SSC14 and SSC18; meat color measurements on SSC1, SSC3, SSC4, SSC5, SSC6, SSC10, SSC11, SSC12, SSC16 and SSC18; water content related measurements in pork were detected on SSC4, SSC6, SSC7, SSC10, SSC12 and SSC14. Additional QTL of pork quality traits such as texture, tenderness and pH were detected on SSC6, SSC12, SSC13 and SSC16. The most important chromosomal region of superior pork quality in KNP compared to YS was identified on SSC12. Our results demonstrated that a QTL linkage map of the $F_2$ design in the pig breed can be generated with a selected data set of high density SNP genotypes. The QTL regions detected in this study will provide useful information for identifying genetic factors related to better pork quality in KNP.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.10
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• pp.1389-1394
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• 2008

Abdominal fat characters are complex and economically important in the poultry industry. Their selection may benefit from the implementation of marker-assisted selection (MAS). The objective of this study was to identify the markers linked to QTL responsible for fatness traits. The Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) were used in the study. A total of 596 individuals from the divergent tails from the 6th to the 10th generations were genotyped at 23 microsatellite markers on chromosome 1. The differences of allele frequencies of all marker alleles between the divergent tails across the five generations were recorded. The allele frequencies of five markers, including LEI0209, LEI0146, MCW0036, ADL328 and MCW0115, had significant differences between the two tails in all five generations. The resulting p-values using Fisher's exact test on eleven markers, containing MCW248, MCW0010, MCW0106, LEI0252, LEI0068, MCW0018, MCW0061, LEI0088, MCW200, MCW283 and ROS0025, had a decreasing tendency from the 6th to the 10th generation. Statistical analysis showed that polymorphisms of the eight markers, including LEI0209, LEI0146, ROS0025, MCW0115, MCW0010, MCW0036, MCW283, ADL328, were significantly (p<0.0011) or suggestively (p<0.05) associated with abdominal fat content (AFW and AFP) across generations. It is concluded that the eight markers could be associated with the QTL affecting the deposition of abdominal fat in broiler chickens.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.6
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• pp.772-779
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• 2013

Linkage disequilibrium (LD) plays an important role in genomic selection and mapping quantitative trait loci (QTL). In this study, the pattern of LD and effective population size ($N_e$) were investigated in Chinese beef Simmental cattle. A total of 640 bulls were genotyped with IlluminaBovinSNP50BeadChip and IlluminaBovinHDBeadChip. We estimated LD for each autosomal chromosome at the distance between two random SNPs of <0 to 25 kb, 25 to 50 kb, 50 to 100 kb, 100 to 500 kb, 0.5 to 1 Mb, 1 to 5 Mb and 5 to 10 Mb. The mean values of $r^2$ were 0.30, 0.16 and 0.08, when the separation between SNPs ranged from 0 to 25 kb to 50 to 100 kb and then to 0.5 to 1 Mb, respectively. The LD estimates decreased as the distance increased in SNP pairs, and increased with the increase of minor allelic frequency (MAF) and with the decrease of sample sizes. Estimates of effective population size for Chinese beef Simmental cattle decreased in the past generations and $N_e$ was 73 at five generations ago.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.6
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• pp.712-721
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• 1997

The objectives of this study were; (1) to identify and localize QTLs for resistance to soybean cyst nematode(SCN) race 5 on RAPD map, (2) to idntify the magnitude and mode of inheritance for each QTL, and (3) to identify the best combinations of QTLs for resistance to SCN race 5. Based on the univariate regression analysis, we detected 26 markers(22 RAPD and 4 RFLP) which showed significant association(P<0.05) with resistance to SCN race 5. From MAPMAKER /QTL analysis, we identified two regions (LGC-20 and Group 2) for resistance to SCN race 5. The QTL that was localized at 8.0 cM from pK418C on LGC-20 showed a recessive mode of inheritance and the QTL that was localized between W03 and E02$^3$ on Group 2 showed a dominant mode of inheritance. Two pairs of flanking markers (E02$^3$ and W03, pK418C and pK418E$_1$) and one unlinked RAPD marker, G10$^1$ were used for multiple regression analysis. Marker combination which was composed of 4 markers, E02$^3$, G10$^1$, W03, and pK418E$_1$, explained the highest amount of phenotypic variation by SCN (35.2％). Further research for the identification of QTLs for resistance to SCN race 5 to explain larger portion of phenotypic variation is needed.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1809-1815
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• 2019

Objective: Copy number variations (CNVs) are a major source of genetic diversity complementary to single nucleotide polymorphism (SNP) in animals. The aim of the study was to perform a comprehensive genomic analysis of CNVs based on high density whole-genome SNP markers in Chinese Dongxiang spotted pigs. Methods: We used customized Affymetrix Axiom Pig1.4M array plates containing 1.4 million SNPs and the PennCNV algorithm to identify porcine CNVs on autosomes in Chinese Dongxiang spotted pigs. Then, the next generation sequence data was used to confirm the detected CNVs. Next, functional analysis was performed for gene contents in copy number variation regions (CNVRs). In addition, we compared the identified CNVRs with those reported ones and quantitative trait loci (QTL) in the pig QTL database. Results: We identified 871 putative CNVs belonging to 2,221 CNVRs on 17 autosomes. We further discarded CNVRs that were detected only in one individual, leaving us 166 CNVRs in total. The 166 CNVRs ranged from 2.89 kb to 617.53 kb with a mean value of 93.65 kb and a genome coverage of 15.55 Mb, corresponding to 0.58% of the pig genome. A total of 119 (71.69%) of the identified CNVRs were confirmed by next generation sequence data. Moreover, functional annotation showed that these CNVRs are involved in a variety of molecular functions. More than half (56.63%) of the CNVRs (n = 94) have been reported in previous studies, while 72 CNVRs are reported for the first time. In addition, 162 (97.59%) CNVRs were found to overlap with 2,765 previously reported QTLs affecting 378 phenotypic traits. Conclusion: The findings improve the catalog of pig CNVs and provide insights and novel molecular markers for further genetic analyses of Chinese indigenous pigs.