• Genomics & Informatics
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• v.8 no.3
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• pp.142-149
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• 2010

In recent years, genome-wide association (GWA) studies have successfully led to many discoveries of genetic variants affecting common complex traits, including height, blood pressure, and diabetes. Although GWA studies have made much progress in finding single nucleotide polymorphisms (SNPs) associated with many complex traits, such SNPs have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. This is partly due to that fact that most current GWA studies have relied on single-marker approaches that identify single genetic factors individually and have limitations in considering the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and provide a better prediction of complex traits, since it utilizes combined information across variants. Recently, a new statistical method for joint identification of genetic variants for common complex traits via the elastic-net regularization method was proposed. In this study, we applied this joint identification approach to a large-scale GWA dataset (i.e., 8842 samples and 327,872 SNPs) in order to identify genetic variants of obesity for the Korean population. In addition, in order to test for the biological significance of the jointly identified SNPs, gene ontology and pathway enrichment analyses were further conducted.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.2
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• pp.303-308
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• 2002

Genetic markers engendered by genome projects drew enormous interest in quantitative genetics, but knowledge on genetic architecture of complex traits is limited. Complexities in genetics will not allow us to easily clarify relationship between genotypes and phenotypes for quantitative traits. Quantitative genetics guides an important way in facing such challenges. It is our exciting task to find genes that affect complex traits. In this paper, landmark research and future prospects are discussed on genetic parameter estimation and quantitative trait locus (QTL) mapping as major subjects of interest.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.3
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• pp.303-309
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• 2014

The increasing importance of meat quality has implications for animal breeding programs. Research has revealed much about the genetic background of pigs, and many studies have revealed the importance of various genetic factors. Since meat quality is a complex trait which is affected by many factors, consideration of the overall phenotype is very useful to study meat quality. For integrating the phenotypes, we used principle component analysis (PCA). The significant SNPs refer to results of the GRAMMAR method against PC1, PC2 and PC3 of 14 meat quality traits of 181 Duroc pigs. The Genome-wide association study (GWAS) found 26 potential SNPs affecting various meat quality traits. The loci identified are located in or near 23 genes. The SNPs associated with meat quality are in or near five genes (ANK1, BMP6, SHH, PIP4K2A, and FOXN2) and have been reported previously. Twenty-five of the significant SNPs also located in meat quality-related QTL regions, these result supported the QTL effect indirectly. Each single gene typically affects multiple traits. Therefore, it is a useful approach to use integrated traits for the various traits at the same time. This innovative approach using integrated traits could be applied on other GWAS of complex-traits including meat-quality, and the results will contribute to improving meat-quality of pork.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.5
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• pp.628-634
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• 2014

Data on age and body weight at breeding, parity, previous litter size, days open and some descriptive body linear traits from 389 meat-type, prolific Black Bengal goats in Tripura State of India, were collected for 3 and 1/2 years (2007 to 2010) and analyzed using logistic regression model. The objectives of the study were i) to evaluate the effect of age and body weight at breeding, parity, previous litter size and days open on litter size of does; and ii) to investigate if body linear type traits influenced litter size in meat-type, prolific goats. The incidence of 68.39% multiple births with a prolificacy rate of 175.07% was recorded. Higher age (>2.69 year), higher parity order (>2.31), more body weight at breeding (>20.5 kg) and larger previous litter size (>1.65) showed an increase likelihood of multiple litter size when compared to single litter size. There was a strong, positive relationship between litter size and various body linear type traits like neck length (>22.78 cm), body length (>54.86 cm), withers height (>48.85 cm), croup height (>50.67 cm), distance between tuber coxae bones (>11.38 cm) and distance between tuber ischii bones (>4.56 cm) for discriminating the goats bearing multiple fetuses from those bearing a single fetus.

• Genomics & Informatics
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• v.8 no.3
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• pp.131-137
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• 2010

Genome-wide association studies (GWASs) have greatly contributed to the identification of common variants responsible for numerous complex traits. There are, however, unavoidable limitations in detecting causal and/or rare variants for traits in this approach, which depends on an LD-based tagging SNP microarray chip. In an effort to detect potential casual and/or rare variants for complex traits, such as type 2 diabetes (T2D) and triglycerides (TGs), we conducted a targeted resequencing of loci identified by the Korea Association REsource (KARE) GWAS. The target regions for resequencing comprised whole exons, exon-intron boundaries, and regulatory regions of genes that appeared within 1 Mb of the GWA signal boundary. From 124 individuals selected in population-based cohorts, a total of 0.7 Mb target regions were captured by the NimbleGen sequence capture 385K array. Subsequent sequencing, carried out by the Roche 454 Genome Sequencer FLX, generated about 110,000 sequence reads per individual. Mapping of sequence reads to the human reference genome was performed using the SSAHA2 program. An average of 62.2% of total reads was mapped to targets with an average 22X-fold coverage. A total of 5,983 SNPs (average 846 SNPs per individual) were called and annotated by GATK software, with 96.5% accuracy that was estimated by comparison with Affymetrix 5.0 genotyped data in identical individuals. About 51% of total SNPs were singletons that can be considered possible rare variants in the population. Among SNPs that appeared in exons, which occupies about 20% of total SNPs, 304 nonsynonymous singletons were tested with Polyphen to predict the protein damage caused by mutation. In total, we were able to detect 9 and 6 potentially functional rare SNPs for T2D and triglycerides, respectively, evoking a further step of replication genotyping in independent populations to prove their bona fide relevance to traits.

• Genomics & Informatics
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• v.9 no.2
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• pp.45-51
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• 2011

How personality forms and whether personality genes exist are long-studied questions. Various concepts and theories have been presented for centuries. Personality is a complex trait and is developed through the interaction of genes and the environment. Twin and family studies have found that there are critical genetic and environmental components in the inheritance of personality traits, and modern advances in genetics are making it possible to identify specific variants for personality traits. Although genes that were found in studies on personality have not provided replicable association between genetic and personality variability, more and more genetic variants associated with personality traits are being discovered. Here, we present the current state of the art on genetic research in the personality field and finally list several of the recently published research highlights. First, we briefly describe the commonly used self-reported measures that define personality traits. Then, we summarize the characteristics of the candidate genes for personality traits and investigate gene variants that have been suggested to be associated with personality traits.

• Genomics & Informatics
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• v.19 no.4
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• pp.36.1-36.11
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• 2021

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.3
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• pp.301-307
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• 2005

Genetic variants of Hanwoo mtDNA in the region of cytochrome oxidase subunit I, II and III complex were detected using restriction enzymes. PCR primers were designed based on the bovine mtDNA sequence, and 6 primer sets (Mt4, Mt5, Mt6, Mt7, Mt8 and Mt9) were used. A total of 20 restriction enzymes were used, and 6 restriction enzymes, which were Hinf I, Pvu II, Rsa I, Eco RI, Bgl II, and Msp I, showed genetic polymorphisms. Significant associations between genetic variants and weight traits were observed at WT15 (p<0.05) and WT18 (p<0.01) with Pvu II for Mt9, Bgl II for Mt6 and Rsa I for Mt8 segments in the region of cytochrome oxidase subunit complex. Significant associations were also observed at Mt9-Pvu II and Mt6-Bgl II segments for WT9 (p=0.01), WT12 (p=0.02), respectively. These results suggest that genetic variants of mtDNA in the region of cytochrome oxidase subunit complex may be candidate segments for improvement of animal growth as weight traits.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.7
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• pp.1129-1134
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• 1999

The rapid development of the sheep genetic linkage map over the last five years has given us the ability to follow the inheritance of chromosomal regions. Initially this powerful resource was used to find markers linked to monogenic traits but there is now increasing interest in using the genetic linkage map to define the complex of genes that control multigenic production traits. Of particular interest are those production traits that are difficult to measure and select for using classical quantitative genetic approaches. These include resistance to disease where a disease challenge (necessary for selection) poses too much risk to valuable stud animals and meat and carcass qualities which can be measured only after the animal has been slaughtered. The goal for the new millennium will be to fully characterise the genetic basis of multigenic production traits. The genetic linkage map is a vital tool required to achieve this.