Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type II_A muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data. Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

Motivation: Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course experiments in which gene expression is monitored over time, we are interested in testing gene expression profiles for different experimental groups. However, no sophisticated analytic methods have yet been proposed to handle time-course experiment data. Results: We propose a statistical test procedure based on the ANOVA model to identify genes that have different gene expression profiles among experimental groups in time-course experiments. Especially, we propose a permutation test which does not require the normality assumption. For this test, we use residuals from the ANOVA model only with time-effects. Using this test, we detect genes that have different gene expression profiles among experimental groups. The proposed model is illustrated using cDNA microarrays of 3840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Availability: A set of programs written by R will be electronically sent upon request. Contact: tspark@stats.snu.ac.kr

Background: DNA methylation is one of the most phylogenetically widespread epigenetic modifications of genomic DNA. In particular, DNA methylation of transcription units (‘gene bodies’) is highly conserved across diverse taxa. However, the functional role of gene body methylation is not yet fully understood. A long-standing hypothesis posits that gene body methylation reduces transcriptional noise associated with spurious transcription of genes. Despite the plausibility of this hypothesis, an explicit test of this hypothesis has not been performed until now. Results: Using nucleotide-resolution data on genomic DNA methylation and abundant microarray data, here we investigate the relationship between DNA methylation and transcriptional noise. Transcriptional noise measured from microarrays scales down with expression abundance, confirming findings from single-cell studies. We show that gene body methylation is significantly negatively associated with transcriptional noise when examined in the context of other biological factors. Conclusions: This finding supports the hypothesis that gene body methylation suppresses transcriptional noise. Heavy methylation of vertebrate genomes may have evolved as a global regulatory mechanism to control for transcriptional noise. In contrast, promoter methylation exhibits positive correlations with the level of transcriptional noise. We hypothesize that methylated promoters tend to undergo more frequent transcriptional bursts than those that avoid DNA methylation.

Summary: Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome‐wide association (GWA) studies have been conducted with large‐scale data sets of genetic variants. Most of those studies have relied on single‐marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and would provide better prediction on complex traits since it utilizes combined information across variants. Here we propose a multi‐stage approach for GWA analysis: (1) prescreening, (2) joint identification of putative SNPs based on elastic‐net variable selection, and (3) empirical replication using bootstrap samples. Our approach enables an efficient joint search for genetic associations in GWA analysis. The suggested empirical replication method can be beneficial in GWA studies because one can avoid a costly, independent replication study while eliminating false‐positive associations and focusing on a smaller number of replicable variants. We applied the proposed approach to a GWA analysis, and jointly identified 129 genetic variants having an association with adult height in a Korean population.

Background Worldwide, gastric cancer (GC) is the fourth most common malignancy and the most common cancer in East Asia. Development of targeted therapies for this disease has focused on a few known oncogenes but has had limited effects. Objective To determine oncogenic mechanisms and novel therapeutic targets specific for GC by identifying commonly dysregulated genes from the tumours of both Asian-Pacific and Caucasian patients. Methods We generated transcriptomic profiles of 22 Caucasian GC tumours and their matched non-cancerous samples and performed an integrative analysis across different GC gene expression datasets. We examined the inhibition of commonly overexpressed oncogenes and their constituent signalling pathways by RNAi and/or pharmacological inhibition. Results Hepatocyte nuclear factor-4α (HNF4α) upregulation was a key signalling event in gastric tumours from both Caucasian and Asian patients, and HNF4α antagonism was antineoplastic. Perturbation experiments in GC tumour cell lines and xenograft models further demonstrated that HNF4α is downregulated by AMPKα signalling and the AMPK agonist metformin; blockade of HNF4α activity resulted in cyclin downregulation, cell cycle arrest and tumour growth inhibition. HNF4α also regulated WNT signalling through its target gene WNT5A, a potential prognostic marker of diffuse type gastric tumours. Conclusions Our results indicate that HNF4α is a targetable oncoprotein in GC, is regulated by AMPK signalling through AMPKα and resides upstream of WNT signalling. HNF4α may regulate ‘metabolic switch’ characteristic of a general malignant phenotype and its target WNT5A has potential prognostic values. The AMPKα-HNF4α-WNT5A signalling cascade represents a potentially targetable pathway for drug development.