Biology subjects

Meng, W.

Publications and source records attributed to Meng, W..

3 recordsLinked to original sources

bioRxiv2018

Genetic correlations between pain phenotypes and depression and neuroticism

Correlations between pain phenotypes and psychiatric traits such as depression and the personality trait of neuroticism are not fully understood. The purpose of this study was to identify whether eight pain phenotypes, depressive symptoms, major depressive disorders, and neuroticism are correlated for genetic reasons. Eight pain phenotypes were defined by a specific pain-related question in the UK Biobank questionnaire. First we generated genome-wide association summary statistics on each pain phenotype, and estimated the common SNP-based heritability of each trait using GCTA. We then estimated the genetic correlation of each pain phenotype with depressive symptoms, major depressive disorders and neuroticism using the the cross-trait linkage disequilibrium score regression (LDSC) method integrated in the LD Hub. Third, we used the LDSC software to calculate genetic correlations among pain phenotypes. All pain phenotypes were heritable, with pain all over the body showing the highest heritability (h2=0.31, standard error=0.072). All pain phenotypes, except hip pain and knee pain, had significant and positive genetic correlations with depressive symptoms, major depressive disorders and neuroticism. The largest genetic correlations occurred between neuroticism and stomach or abdominal pain (rg=0.70, P=2.4 x 10-9). In contrast, hip pain and knee pain showed weaker evidence of shared genetic architecture with these negative emotional traits. In addition, many pain phenotypes had positive and significant genetic correlations with each other indicating shared genetic mechanisms. Pain at a variety of body sites is heritable and genetically correlated with depression and neuroticism. This suggests that pain, neuroticism and depression share partially overlapping genetic risk factors.

genetics↗

bioRxiv2018

Deciphering the rules which mRNA structures differs from vivo and vitro in Saccharomyces cerevisiae by deep neural networks

The structure of mRNA in vivo is influenced by various factors involved in the translation process, resulting in significant differentiation of mRNA structure from that in vitro. Because multiple factors cause the differentiation of in vivo and in vitro mRNA structures, it was difficult to perform a more accurate analysis of mRNA structures in previous studies. In this study, we have proposed a novel application of a deep neural network (DNN) model to predict the structural stability of mRNA in vivo by fitting six quantifiable features that may affect mRNA folding: ribosome density, minimum folding free energy, GC content, mRNA abundance, ribosomal initial density and position of mRNA structure. Simulated mutations of the mRNA structure were designed and then fed into the trained DNN model to compute their structural stability. We found unique effects of these six features on mRNA structural stability in vivo. Strikingly, the ribosome density of the structural region is the most important factor affecting the structural stability of mRNA in vivo, and the strength of the mRNA structure in vitro should have a relatively small effect on its structural stability in vivo. The recruitment of DNNs provides a new paradigm to decipher the differentiation of mRNA structure in vivo and in vitro. This improved knowledge on the mechanisms of factors influencing mRNA structural stability will facilitate the design and functional analysis of mRNA structure in vivo.

bioinformatics↗

bioRxiv2017

A genome-wide association study finds novel genetic associations with broadly-defined headache in UK Biobank (N = 223,773)

Headache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. We identified 3,343 SNPs which reached the genome-wide significance level of P < 5 x 10-8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 x 10-47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 x 10-15 in the LINC02210-CRHR1 gene was the top SNP.\n\nPositive relationships (P < 0.001) between multiple brain tissues and genetic associations were identified through tissue expression analysis, whereas no vascular related tissues showed significant relationships. We identified several significant positive genetic correlations between headache and other psychological traits including neuroticism, depressive symptoms, insomnia, and major depressive disorder.\n\nOur results suggest that brain function is closely related to broadly-defined headache. In addition, we also found that many psychological traits have genetic correlations with headache.

genetics↗