• Genomics & Informatics
• /
• 제9권2호
• /
• pp.45-51
• /
• 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.

• Journal of Genetic Medicine
• /
• 제12권1호
• /
• pp.1-5
• /
• 2015

De novo variants (DNVs) can arise during parental germ cell formation, fertilization, and the processes of embryogenesis. It is estimated that each individual carries 60-100 such spontaneous variants in the genome, most of them benign. However, a number of recent studies suggested that DNVs contribute to the pathogenesis of a variety of human diseases. Applications of DNVs include aiding in clinical diagnosis and identifying disease-causing genetic factors in patients with atypical symptoms. Therefore, understanding the roles of DNVs in a trio, with healthy parents and an affected offspring, would be crucial in elucidating the genetic mechanism of disease pathogenesis in a personalized manner.

• Genomics & Informatics
• /
• 제19권4호
• /
• pp.36.1-36.11
• /
• 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.

• Genomics & Informatics
• /
• 제21권3호
• /
• pp.37.1-37.11
• /
• 2023

Systemic lupus erythematosus (SLE) is an inflammatory-autoimmune disease with a complex multi-organ pathogenesis, and it is known to be associated with significant morbidity and mortality. Various genetic, immunological, endocrine, and environmental factors contribute to SLE. Genomic variants have been identified as potential contributors to SLE susceptibility across multiple continents. However, the specific pathogenic variants that drive SLE remain largely undefined. In this study, we sought to identify these pathogenic variants across various continents using genomic and bioinformatic-based methodologies. We found that the variants rs35677470, rs34536443, rs17849502, and rs13306575 are likely damaging in SLE. Furthermore, these four variants appear to affect the gene expression of NCF2, TYK2, and DNASE1L3 in whole blood tissue. Our findings suggest that these genomic variants warrant further research for validation in functional studies and clinical trials involving SLE patients. We conclude that the integration of genomic and bioinformatic-based databases could enhance our understanding of disease susceptibility, including that of SLE.

• Childhood Kidney Diseases
• /
• 제28권1호
• /
• pp.8-15
• /
• 2024

With the rapid evolution of diagnostic tools, particularly next-generation sequencing, the identification of genetic diseases, predominantly those with pediatric-onset, has significantly advanced. However, this progress presents challenges that span from selecting appropriate tests to the final interpretation of results. This review examines various genetic testing methodologies, each with specific indications and characteristics, emphasizing the importance of selecting the appropriate genetic test in clinical practice, taking into account factors like detection range, cost, turnaround time, and specificity of the clinical diagnosis. Interpretation of variants has become more challenging, often requiring further validation and significant resource allocation. Laboratories primarily classify variants based on the American College of Medical Genetics and Genomics and the Association for Clinical Genomic Science guidelines, however, this process has limitations. This review underscores the critical role of clinicians in matching patient phenotypes with reported genes/variants and considering additional factors such as variable expressivity, disease pleiotropy, and incomplete penetrance. These considerations should be aligned with specific gene-disease characteristics and segregation results based on an extended pedigree. In conclusion, this review aims to enhance understanding of the complexities of clinical genetic testing, advocating for a multidisciplinary approach to ensure accurate diagnosis and effective management of rare genetic diseases.

• 한국작물학회지
• /
• 제49권1호
• /
• pp.69-72
• /
• 2004

Somaclonal variation, defined as phenotypic and genetic variations among regenerated plants from a parental plant, could be caused by changes in chromosome structure, single gene mutation, cytoplasm genetic mutation, insertion of transposable elements, and DNA methylation during plant regeneration. The objective of this study was to evaluate DNA variations among somaclonal variants from the cotyledonary node culture in soybean. A total of 61 soybean somaclones including seven $\textrm{R}_1$ lines and seven $\textrm{R}_2$ lines from Iksannamulkong as well as 27 $\textrm{R}_1$ lines and 20 $\textrm{R}_2$ lines from Jinju 1 were regenerated by organogenesis from the soybean cotyledonary node culture system. Field evaluation revealed no phenotypic difference in major agronomic traits between somaclonal variants and their wild types. AFLP and SSR analyses were performed to detect variations at the DNA level among somaclonal variants of two varieties. Based on AFLP analysis using 36 primer sets, 17 of 892 bands were polymorphic between Iksannamulkong and its somaclonal variants and 11 of 887 bands were polymorphic between Jinju 1 and its somaclonal variants, indicating the presence of DNA sequence change during plant regeneration. Using 36 SSR markers, two polymorphic SSR markers were detected between Iksannamulkong and its somaclonal variants. Sequence comparison amplified with the primers flanking Satt545 showed four additional stretches of ATT repeat in the variant. This suggests that variation at the DNA level between somaclonal variants and their wild types could provide basis for inducing mutation via plant regeneration and broadening crop genetic diversity.

• Interdisciplinary Bio Central
• /
• 제3권2호
• /
• pp.6.1-6.3
• /
• 2011

Diabetes Mellitus (DM), often simply referred to as diabetes, has developed into a major health concern affecting more than 200 million people worldwide with approximately 4 million deaths per year attributed to the presence of the disease. Diabetes mellitus is categorized as Type 1 and Type 2, where Type 1 diabetes represents a lack of insulin production, and Type 2 diabetes is characterized by a relative lack of insulin receptor (i.e., decreased sensitivity to the effect of insulin) and cased by a complex interplay between genetic factors and environmental factors. Up to date, various studies on the pathology and mechanism in terms of genetic experiments have been conducted and approximately hundreds of genes were reported as diabetes mellitus associated genes. At this point, to support studies on the cause and mechanism of diabetes mellitus, an efficient database system to provide genetic variants related to diabetes mellitus is needed. DMBase is an integrated web-based genetic information resource for diabetes mellitus designed to service genomic variants, genes, and secondary information derived for diabetes mellitus genetics researchers. The current version of DMBase documents 754 genes with 3056 genetic variants and 66 pathways. It provides many effective search interfaces for retrieving diabetes mellitus and genetic information. A web interface for the DMBase is freely available at http://sysbio.kribb.re.kr/dmBase.

• BMB Reports
• /
• 제46권8호
• /
• pp.416-421
• /
• 2013

Genome-wide association studies (GWAS) have become popular as an approach for the identification of large numbers of phenotype-associated variants. However, differences in genetic architecture and environmental factors mean that the effect of variants can vary across populations. Understanding population genetic diversity is valuable for the investigation of possible population specific and independent effects of variants. EvoSNP-DB aims to provide information regarding genetic diversity among East Asian populations, including Chinese, Japanese, and Korean. Non-redundant SNPs (1.6 million) were genotyped in 54 Korean trios (162 samples) and were compared with 4 million SNPs from HapMap phase II populations. EvoSNP-DB provides two user interfaces for data query and visualization, and integrates scores of genetic diversity (Fst and VarLD) at the level of SNPs, genes, and chromosome regions. EvoSNP-DB is a web-based application that allows users to navigate and visualize measurements of population genetic differences in an interactive manner, and is available online at [http://biomi.cdc.go.kr/EvoSNP/].

• 한국가금학회지
• /
• 제50권3호
• /
• pp.187-191
• /
• 2023

Korean native ducks (KNDs) continue to have a high preference from consumers due to their excellent meat quality and taste characteristics. However, due to low productivity and fixed plumage color phenotype, it could not secure a large share in the domestic market compared to imported species. In order to improve the market share of KNDs, the genetic characteristics of the breed should be identified and used for improvement and selection. Therefore, this study was conducted to identify the genetic information of colored and white KNDs using next-generation resequencing data and screening for differences between the two groups. As a result of the analysis, the genetic variants that showed significant differences between the colored and white KND groups were mainly identified as mutations related to tyrosine activity. The variants were located in the genes that affect melanin synthesis and regulation, such as EGFR, PDGFRA, and DDR2, and these were reported as the candidate genes related to plumage pigmentation in poultry. Therefore, the results of this study are expected to be useful as a basis for understanding and utilizing the genetic characteristics of KNDs for genetic improvement and selection of white broiler KNDs.

• Communications for Statistical Applications and Methods
• /
• 제27권5호
• /
• pp.535-546
• /
• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.