• Genomics & Informatics
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• v.6 no.1
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• pp.8-13
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• 2008

High-density lipoprotein (HDL) whose primary role is to transport cholesterol from peripheral tissues to the liver, is associated with the incidence of coronary heart disease. We analyzed HDL cholesterol levels in a genetically isolated population of extended Mongolian families. A total of 1002 individuals (54.5% women) from 95 families were enrolled. After genotyping by use of 1000 microsatellite markers, we performed a genome-wide linkage search with variance component analysis. The estimated heritability of HDL cholesterol was 0.45, revealing that HDL cholesterol was under significant genetic influence. We found peak evidence of linkage (LOD score=1.88) for HDL cholesterol level on chromosome 6 (nearest marker D6S1660) and potential evidences for linkage on chromosomes 1, 12 and 19 with the LOD scores of 1.32, 1.44 and 1.14, respectively. These results should pave the way for the discovery of the relevant genes by fine mapping and association analysis.

• Genomics & Informatics
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• v.21 no.3
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

• Horticultural Science & Technology
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• v.35 no.2
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• pp.149-164
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• 2017

Next generation sequencing (NGS) technologies have led to the rapid accumulation of genome sequences through whole-genome sequencing and re-sequencing of crop species. Genomic resources provide the opportunity for a new revolution in plant breeding by facilitating the dissection of complex traits. Among vegetable crops, reference genomes have been sequenced and assembled for several species in the Solanaceae and Cucurbitaceae families, including tomato, pepper, cucumber, watermelon, and melon. These reference genomes have been leveraged for re-sequencing of diverse germplasm collections to explore genome-wide sequence variations, especially single nucleotide polymorphisms (SNPs). The use of genome-wide SNPs and high-throughput genotyping methods has led to the development of new strategies for dissecting complex quantitative traits, such as genome-wide association study (GWAS). In addition, the use of multi-parent populations, including nested association mapping (NAM) and multiparent advanced generation intercross (MAGIC) populations, has helped increase the accuracy of quantitative trait loci (QTL) detection. Consequently, a number of QTL have been discovered for agronomically important traits, such as disease resistance and fruit traits, with high mapping resolution. The molecular markers for these QTL represent a useful resource for enhancing selection efficiency via marker-assisted selection (MAS) in vegetable breeding programs. In this review, we discuss current genomic resources and marker-trait association analysis to facilitate genome-assisted breeding in vegetable species in the Solanaceae and Cucurbitaceae families.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.233-233
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• 2022

As the world's population grows and food needs diversify, the demand for horticultural crops for beneficial traits is increasing. In order to meet this demand, it is necessary to develop suitable cultivars and breeding methods accordingly. Breeding methods have changed over time. With the recent development of sequencing technology, the concept of genomic selection (GS) has emerged as large-scale genome information can be used. GS shows good predictive ability even for quantitative traits by using various markers, breaking away from the limitations of Marker Assisted Selection (MAS). Moreover, GS using machine learning (ML) and deep learning (DL) has been studied recently. In this study, we aim to build a system that selects phenotype-related markers using the genomic information of the pepper population and trains a genomic selection model to select individuals from the validation population. We plan to establish an optimal genome wide association analysis model by comparing and analyzing five models. Validation of molecular markers by applying linkage markers discovered through genome wide association analysis to breeding populations. Finally, we plan to establish an optimal genome selection model by comparing and analyzing 12 genome selection models. Then We will use the genome selection model of the learning group in the breeding group to verify the prediction accuracy and discover a prediction model.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.58-58
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• 2017

The advent of next generation sequencing technology has elicited plenty of sequencing data available in agriculturally relevant plant species. For most crop species, it is too expensive to obtain the whole genome sequence data with sufficient coverage. Thus, many approaches have been developed to bring down the cost of NGS. Genotyping-by-sequencing (GBS) is a cost-effective genotyping method for complex genetic populations. GBS can be used for the analysis of genomic selection (GS), genome-wide association study (GWAS) and constructing haplotype and genetic linkage maps in a variety of plant species. For efficiently dealing with plant GBS data, the TASSEL-GBS pipeline is one of the most popular choices for many researchers. TASSEL-GBS is JAVA based a software package to obtain genotyping data from raw GBS sequences. Here, we describe application of GBS and bioinformatics pipeline of TASSEL-GBS for analyzing plant genetics data.

• Animal Bioscience
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• v.36 no.10
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• pp.1499-1507
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• 2023

Objective: The study investigated the origin of the Akhal-Teke horse using genome-wide single-nucleotide polymorphism (SNP) data and mitochondrial hypervariable region 1 (HVR-1) nucleotide sequences Methods: Genome-wide SNP data from 22 breeds (481 horses) and mitochondrial HVR-1 sequences from 24 breeds (544 sequences) worldwide to examine the origin of the Akhal-Teke horse. The data were analyzed using principal component analysis, linkage disequilibrium analysis, neighbor-joining dendrograms, and ancestry inference to determine the population relationships, ancestral source, genetic structure, and relationships with other varieties. Results: A close genetic relationship between the Akhal-Teke horse and horses from the Middle East was found. Analysis of mitochondrial HVR-1 sequences showed that there were no shared haplotypes between the Akhal-Teke and Tarpan horses, and the mitochondrial data indicated that the Akhal-Teke horse has not historically expanded its group. Ancestral inference suggested that Arabian and Caspian horses were the likely ancestors of the Akhal-Teke horse. Conclusion: The Akhal-Teke horse originated in the Middle East.

• Genomics & Informatics
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• v.12 no.4
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• pp.165-170
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• 2014

Genome-wide association (GWA) studies have found many important genetic variants that affect various traits. Since these studies are useful to investigate untyped but causal variants using linkage disequilibrium (LD), it would be useful to explore the haplotypes of single-nucleotide polymorphisms (SNPs) within the same LD block of significant associations based on high-density variants from population references. Here, we tried to make a haplotype catalog affecting body mass index (BMI) through an integrative analysis of previously published whole-genome next-generation sequencing (NGS) data of 7 representative Korean individuals and previously known Korean GWA signals. We selected 435 SNPs that were significantly associated with BMI from the GWA analysis and searched 53 LD ranges nearby those SNPs. With the NGS data, the haplotypes were phased within the LDs. A total of 44 possible haplotype blocks for Korean BMI were cataloged. Although the current result constitutes little data, this study provides new insights that may help to identify important haplotypes for traits and low variants nearby significant SNPs. Furthermore, we can build a more comprehensive catalog as a larger dataset becomes available.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.10
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• pp.1359-1365
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• 2009

We performed a genome-wide linkage and quantitative trait locus (QTL) analysis to confirm the existence of QTL affecting Rous Sarcoma Virus (RSV) induced tumor regression, and to estimate their effects on phenotypic variance in an F2 resource population. The F2 population comprised 158 chickens obtained by crossing tumor regressive White Leghorn (WL) and tumor progressive Rhode Island Red (RIR) lines was measured for tumor formation after RSV inoculation. Forty-three tumor progressive and 28 tumor regressive chickens were then used for genome-wide linkage and QTL analysis using a total of 186 microsatellite markers. Microsatellite markers were mapped on 20 autosomal chromosomes. A significant QTL was detected with marker LEI0258 located within the MHC B region on chromosome 16. This QTL had the highest F ratio (9.8) and accounted for 20.1% of the phenotypic variation. Suggestive QTL were also detected on chromosomes 4, 7 and 10. The QTL on chromosome 4 were detected at the 1% chromosome-wide level explaining 17.5% of the phenotypic variation, and the QTLs on chromosome 7 and 10 were detected at the 5% chromosome-wide level and explained 11.1% and 10.5% of the phenotypic variation, respectively. These results indicate that the QTLs in the non-MHC regions play a significant role in RSV-induced tumor regression. The present study constitutes one of the first preliminary reports in domestic chickens for QTLs affecting RSV-induced tumor regression outside the MHC region.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.8
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• pp.1061-1065
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• 2017

Objective: This study was conducted to locate quantitative trait loci (QTL) influencing fatty acid (FA) composition in a large $F_2$ intercross between Landrace and Korean native pigs. Methods: Eighteen FA composition traits were measured in more than 960 $F_2$ progeny. All experimental animals were genotyped with 165 microsatellite markers located throughout the pig autosomes. Results: We detected 112 QTLs for the FA composition; Forty seven QTLs reached the genome-wide significant threshold. In particular, we identified a cluster of highly significant QTLs for FA composition on SSC12. QTL for polyunsaturated fatty acid on pig chromosome 12 (F-value = 97.2 under additive and dominance model, nominal p-value $3.6{\times}10^{-39}$) accounted for 16.9% of phenotypic variance. In addition, four more QTLs for C18:1, C18:2, C20:4, and monounsaturated fatty acids on the similar position explained more than 10% of phenotypic variance. Conclusion: Our findings of a major QTL for FA composition presented here could provide helpful information to locate causative variants to improve meat quality traits in pigs.

• Journal of Embryo Transfer
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• v.31 no.4
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• pp.313-318
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• 2016

The aim of this study was to identify quantitative trait loci (QTLs) influencing teat number traits in an $F_2$ intercross between Landrace and Korean native pigs (KNP). Three teat number traits (left;right;and total) were measured in 1105 $F_2$ progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We detect that seven chromosomes harbored QTLs for teat number traits: genome regions on SSC1;3;7;8;10;11;and 13. Six of fourteen identified QTL reached genome-wide significance. In SSC7;we identified a major QTL affecting total teat number that accounted for 5.6 % of the phenotypic variance;which was the highest test statistic (F-ratio = 61.1 under the additive model;nominal $P=1.3{\times}10^{-14}$) observed in this study. In this region;QTL for left and right teat number were also detected with genome-wide significance. With exception of the QTL in SSC10;the allele from KNP in all 6 identified QTLs was associated with decreased phenotypic values. In conclusion;our study identified both previously reported and novel QTL affecting teat number traits. These results can play an important role in determining the genetic structure underlying the variation of teat number in pigs.