Similarly, gene expression studies in postmortem brain tissue have found that expression changes in mental illnesses tend to be small and correlated across diagnoses. Genome-wide association studies (GWAS) have revealed that major psychiatric disorders share many common genetic variants that exert small effects on risk.
Genetic risk factors that shape the brain transcriptome may contribute to diagnostic differences between broad classes of mental illness. Common genetic variants previously associated with disease risk were especially enriched for sQTLs, which together accounted for disproportionate fractions of diagnosis-specific heritability. Common genetic variants were associated with transcript expression (eQTL) or relative abundance of alternatively spliced transcripts (sQTL). The ~250 rare transcripts that were differentially-expressed in one or more disorder groups were enriched for genes involved in synapse formation, cell junctions, and heterotrimeric G-protein complexes.
Case–case comparisons revealed greater expression differences, with some transcripts showing opposing patterns of expression between diagnostic groups, relative to controls. Case–control comparisons detected modest expression differences that were correlated across disorders. RNA obtained postmortem from 200 donors diagnosed with bipolar disorder, schizophrenia, major depression, or no psychiatric disorder was deeply sequenced to quantify expression of over 85,000 gene transcripts, many of which were rare.
Here we investigate expressed genes and gene transcripts in postmortem subgenual anterior cingulate cortex (sgACC), a key component of limbic circuits linked to mental illness. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.Despite strong evidence of heritability and growing discovery of genetic markers for major mental illness, little is known about how gene expression in the brain differs across psychiatric diagnoses, or how known genetic risk factors shape these differences. Finally, a stepby-step reproducible analysis guide is introduced. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and betadiversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices.
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Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Here, we systematically summarize the advantages and limitations of microbiome methods. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field.
A practical guide to amplicon and metagenomic analysis of microbiome data doi: 10.1007/s13234-8ġ State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China Ģ CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China ģ CAS-JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China Ĥ College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China ĥ National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China Ħ Department of Rheumatology Immunology&Allergy, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310053, ChinaĪdvances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated.
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