An association between lower educational attainment (EA) and an increased risk for depression has... more An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120 000 subjects) and MDD (using a 10-fold leave-one-ou...
Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphi... more Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphisms (SNPs) for association to a trait, treating each marker equally and ignoring prior evidence of association to specific regions. Typically, promising regions are selected for further investigation based on p-values obtained from simple tests of association. However, loci that exert only a weak, low-penetrant role on the trait, producing modest evidence of association, are not detectable in the context of a GWAS. Implementing prior knowledge of association in GWAS could increase power, help distinguish between false and true positives, and identify better sets of SNPs for follow-up studies.
Genome-wide association studies (GWAS) are a popular approach for identifying common genetic vari... more Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2... more Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterizati...
Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, 2010
Recent studies have revealed that in higher eukaryotes, several ribosomal proteins are involved i... more Recent studies have revealed that in higher eukaryotes, several ribosomal proteins are involved in some pathological events or developmental defects, indicating that ribosomal proteins perform unconventional functions other than protein biosynthesis. To obtain an insight into the novel roles of ribosomal proteins, we aimed to analyze the changes in proteome expression in ribosomal protein mutants by using Saccharomyces cerevisiae as a model system. We introduced the rpl35bΔ mutation into the 4159 green fluorescent protein (GFP)-tagged yeast strains by using the synthetic genetic array (SGA) method, and performed quantitative proteomic analysis by using a multilabel microplate reader and flow cytometer. We identified 22 upregulated and 20 downregulated proteins in the rpl35bΔ mutant. These proteins were primarily classified into the Gene Ontology (GO) categories of cellular biosynthetic process, translation, protein or nucleotide metabolic process, cell wall organization and biogenesis, and hyperosmotic response. We also investigated the correlation between the mRNA and protein levels of the identified proteins. Our results show that a ribosomal protein mutation can lead to perturbation in the expression of several proteins, including some other ribosomal proteins. Furthermore, our approach of combining a library of GFP-tagged yeast strains and the SGA method provides an effective and highly sensitive method for dynamic analysis of the effects of various mutations on proteome expression.
Sequencing and exome-chip technologies have motivated development of novel statistical tests to i... more Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical b...
An association between lower educational attainment (EA) and an increased risk for depression has... more An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120 000 subjects) and MDD (using a 10-fold leave-one-ou...
Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphi... more Genome-wide association studies (GWAS) test hundreds of thousands of single-nucleotide polymorphisms (SNPs) for association to a trait, treating each marker equally and ignoring prior evidence of association to specific regions. Typically, promising regions are selected for further investigation based on p-values obtained from simple tests of association. However, loci that exert only a weak, low-penetrant role on the trait, producing modest evidence of association, are not detectable in the context of a GWAS. Implementing prior knowledge of association in GWAS could increase power, help distinguish between false and true positives, and identify better sets of SNPs for follow-up studies.
Genome-wide association studies (GWAS) are a popular approach for identifying common genetic vari... more Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2... more Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterizati...
Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, 2010
Recent studies have revealed that in higher eukaryotes, several ribosomal proteins are involved i... more Recent studies have revealed that in higher eukaryotes, several ribosomal proteins are involved in some pathological events or developmental defects, indicating that ribosomal proteins perform unconventional functions other than protein biosynthesis. To obtain an insight into the novel roles of ribosomal proteins, we aimed to analyze the changes in proteome expression in ribosomal protein mutants by using Saccharomyces cerevisiae as a model system. We introduced the rpl35bΔ mutation into the 4159 green fluorescent protein (GFP)-tagged yeast strains by using the synthetic genetic array (SGA) method, and performed quantitative proteomic analysis by using a multilabel microplate reader and flow cytometer. We identified 22 upregulated and 20 downregulated proteins in the rpl35bΔ mutant. These proteins were primarily classified into the Gene Ontology (GO) categories of cellular biosynthetic process, translation, protein or nucleotide metabolic process, cell wall organization and biogenesis, and hyperosmotic response. We also investigated the correlation between the mRNA and protein levels of the identified proteins. Our results show that a ribosomal protein mutation can lead to perturbation in the expression of several proteins, including some other ribosomal proteins. Furthermore, our approach of combining a library of GFP-tagged yeast strains and the SGA method provides an effective and highly sensitive method for dynamic analysis of the effects of various mutations on proteome expression.
Sequencing and exome-chip technologies have motivated development of novel statistical tests to i... more Sequencing and exome-chip technologies have motivated development of novel statistical tests to identify rare genetic variation that influences complex diseases. Although many rare-variant association tests exist for case-control or cross-sectional studies, far fewer methods exist for testing association in families. This is unfortunate, because cosegregation of rare variation and disease status in families can amplify association signals for rare variants. Many researchers have begun sequencing (or genotyping via exome chips) familial samples that were either recently collected or previously collected for linkage studies. Because many linkage studies of complex diseases sampled affected sibships, we propose a strategy for association testing of rare variants for use in this study design. The logic behind our approach is that rare susceptibility variants should be found more often on regions shared identical by descent by affected sibling pairs than on regions not shared identical b...
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Papers by Min Jhun