HisCoM-PCA: software for hierarchical structural component analysis for pathway analysis based using principal component analysis

Jiang, Nan,Lee, Sungyoung,Park, Taesung Korea Genome Organization 2020 Genomics & informatics Vol.18 No.1

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

On the Meta-Analysis of Genome-Wide Association Studies: A Robust and Efficient Approach to Combine Population and Family-Based Studies

Won, Sungho,Lu, Qing,Bertram, Lars,Tanzi, Rudolph E.,Lange, Christoph S. Karger AG 2012 Human heredity Vol.73 No.1

<P>For the meta-analysis of genome-wide association studies, we propose a new method to adjust for the population stratification and a linear mixed approach that combines family-based and unrelated samples. The proposed approach achieves similar power levels as a standard meta-analysis which combines the different test statistics or p values across studies. However, by virtue of its design, the proposed approach is robust against population admixture and stratification, and no adjustments for population admixture and stratification, even in unrelated samples, are required. Using simulation studies, we examine the power of the proposed method and compare it to standard approaches in the meta-analysis of genome-wide association studies. The practical features of the approach are illustrated with a meta-analysis of three genome-wide association studies for Alzheimer’s disease. We identify three single nucleotide polymorphisms showing significant genome-wide association with affection status. Two single nucleotide polymorphisms are novel and will be verified in other populations in our follow-up study.</P><P>Copyright © 2012 S. Karger AG, Basel</P>

Genomic Tools and Their Implications for Vegetable Breeding

Ngan Thi Phan,Sung-Chur Sim 한국원예학회 2017 원예과학기술지 Vol.35 No.2

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 genomewide 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 markertrait association analysis to facilitate genome-assisted breeding in vegetable species in the Solanaceae and Cucurbitaceae families.

Genome-wide association analysis reveals the genetic locus for high reproduction trait in Chinese Arbas Cashmere goat

Rabiul Islam,Xuexue Liu,Gebremedhin Gebreselassie,Adam Abied,Qing Ma,Yuehui Ma 한국유전학회 2020 Genes & Genomics Vol.42 No.8

Background Litter size is the most important reproductive trait which plays a crucial role in goat production. Therefore, improvement of litter size trait has been of increasing interest in goat industry as small improvement in litter size may lead to large profit. The recent Cashmere goat breeding program produced a high-reproductive genetic line of Arbas Cashmere goat. But the genetic mechanism of high reproduction rate remains largely unknown in this Chinese native goat breed. To address this question, we performed a genome-wide association studies (GWAS) using two groups of goats varying in fecundity. Objectives Our study was aimed to investigate the significant SNPs and genes associated with high reproduction trait in Inner Mongolia Arbas Cashmere Goat. Methods We used logistic model association to perform GWAS using 47 goats from high fecundity group (~ 190%) and 314 goats from low fecundity group (~ 130%) of the Arbas Cashmere goat breed. Results We identified 66 genomic regions associated with genome wide significant level wherein six loci were found to be associated with reproduction traits. Further analysis showed that five key candidate genes including KISS1, KHDRBS2, WNT10B, SETDB2 and PPP3CA genes are involved in goat fecundity trait. Gene ontology enrichment analysis revealed that several biological pathways could be involved in the variation of fecundity in female goats. Conclusions The identified significant SNPs or genes provide useful information about the underlying genetic control of fecundity trait which will be helpful to use them in goat breeding programs for improving the reproductive efficiency of goats.

Genome-wide identification, characterization and expression analysis of the BRI1 gene family in Triticum aestivum L.

Sharma Nikita,Khurana Paramjit 한국식물생명공학회 2022 Plant biotechnology reports Vol.16 No.6

Brassinosteroids (BRs) are important plant growth regulators afecting the growth and development of plants. Brassinosteroids are perceived by Brassinosteroid insensitive1 (BRI1) at the cell surface and a plethora of events occurs through brassinosteroid signalling, leading to changes in growth and developmental processes. In this study, a whole-genome investigation of the BRI1 gene family was carried out in wheat, Triticum aestivum, by using the currently available wheat reference genome and 106 members were identifed. These were divided into nine paralogue groups and a representative member from each group was studied in detail. Their structure, function and evolution were analysed. Their promoter analysis unveiled the presence of several developmental and hormone response elements. TaBRI1 was found to interact with SOMATIC EMBRYOGENESIS RECEPTOR KINASES (SERKs), which made us curious to explore its function during embryogenesis. In this study, we endeavoured to understand the role of BRI1 in wheat embryogenesis along with genome-wide identifcation of TaBRI1 gene family members. These members were divided into three homeolog groups and their qRT-PCR analysis revealed that they were following similar expression patterns in diferent tissues and somatic embryogenesis-inductive treatment, suggesting their possible involvement in the process.

CDH13 and HCRTR2 May Be Associated with Hypersomnia Symptom of Bipolar Depression: A Genome-Wide Functional Enrichment Pathway Analysis

조철현,이헌정,우현구,최지혜,Tiffany A. Greenwood,John R. Kelsoe 대한신경정신의학회 2015 PSYCHIATRY INVESTIGATION Vol.12 No.3

Although bipolar disorder is highly heritable, the identification of specific genetic variations is limited because of the complex traits underlying the disorder. We performed a genome-wide association study of bipolar disorder using a subphenotype that shows hypersomnia symptom during a major depressive episode. We investigated a total of 2,191 cases, 1,434 controls, and 703,012 single nucleotide polymorphisms (SNPs) in the merged samples obtained from the Translational Genomics Institute and the Genetic Association Information Network. The gene emerging as the most significant by statistical analysis was rs1553441 (odds ratio=0.4093; p=1.20×10-5; Permuted p=6.0×10-6). However, the 5×10-8 threshold for statistical significance required in a genome-wide association study was not achieved. The functional enrichment pathway analysis showed significant enrichments in the adhesion, development-related, synaptic transmission- related, and cell recognition-related pathways. For further evaluation, each gene of the enriched pathways was reviewed and matched with genes that were suggested to be associated with psychiatric disorders by previous genetic studies. We found that the cadherin 13 and hypocretin (orexin) receptor 2 genes may be involved in the hypersomnia symptom during a major depressive episode of bipolar disorder.

Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

이성영,박태성,권민석 한국유전체학회 2012 Genomics & informatics Vol.10 No.4

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.

Application of Structural Equation Models to Genome-wide Association Analysis

Kim, Ji-Young,Namkung, Jung-Hyun,Lee, Seung-Mook,Park, Tae-Sung Korea Genome Organization 2010 Genomics & informatics Vol.8 No.3

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

Application of Structural Equation Models to Genome-wide Association Analysis

김지영,남궁정현,이승묵,박태성 한국유전체학회 2010 Genomics & informatics Vol.8 No.3

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher’s combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher’s exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

Network Graph Analysis of Gene-Gene Interactions in Genome-Wide Association Study Data

Lee, Sungyoung,Kwon, Min-Seok,Park, Taesung Korea Genome Organization 2012 Genomics & informatics Vol.10 No.4

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.