Centre for High-Throughput Biology, Department of Microbiology & Immunology, University of British Columbia, Vancouver, British Columbia, Canada.
Published: October 2014
AI Article Synopsis
Article Abstract
Transcriptional regulation involves complex and interdependent interactions of noncoding and coding regions of the genome with proteins that interact and modify them. Genetic variation/mutation in coding and noncoding regions of the genome can drive aberrant transcription and disease. In spite of accounting for nearly 98% of the genome comparatively little is known about the contribution of noncoding DNA elements to disease. Genome-wide association studies of complex human diseases including cancer have revealed enrichment for variants in the noncoding genome. A striking finding of recent cancer genome re-sequencing efforts has been the previously underappreciated frequency of mutations in epigenetic modifiers across a wide range of cancer types. Taken together these results point to the importance of dysregulation in transcriptional regulatory control in genesis of cancer. Powered by recent technological advancements in functional genomic profiling, exploration of normal and transformed regulatory networks will provide novel insight into the initiation and progression of cancer and open new windows to future prognostic and diagnostic tools.
Laboratory Medicine Center, Department of Genetic and Genomic Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Background: GTPBP3 catalyzes τm(s) U biosynthesis at the 34th wobble position of mitochondrial tRNAs, the hypomodification of τmU leads to mitochondrial disease. While twenty-three variants of GTPBP3 have been reported worldwide, the genetic landscape in China remains uncertain.
Methods: By using whole-exome sequencing, the candidate individuals carrying GTPBP3 variants were screened and identified.
Background: Fat traits in cattle are considered important due to their contribution to beef eating quality and carcass economic value. Discovering the genes controlling fat traits in cattle will enable better selection of these traits, but identifying these genes in individual experiments has proven difficult. Compared to individual experiments, meta-analyses allow greater statistical power for detecting quantitative trait loci and identifying genes that influence single and multiple economically important fat traits.
Background: Accurate prediction of copy number variations (CNVs) from targeted capture next-generation sequencing (NGS) data relies on effective normalization of read coverage profiles. The normalization process is particularly challenging due to hidden systemic biases such as GC bias, which can significantly affect the sensitivity and specificity of CNV detection. In many cases, the kit manifests provide only the genome coordinates of the targeted regions, and the exact bait design of the oligo capture baits is not available.
Sweetpotato (Ipomoea batatas L.), an important food and industrial crop in the world, has a highly heterozygous hexaploid genome, making the development of single nucleotide polymorphism (SNP) markers challenging. Identifying SNP loci and developing practical SNP markers are crucial for genomic and genetic research on sweetpotato.
Comorbidity among atopic diseases (ADs) and gastrointestinal diseases (GIDs) has been repeatedly demonstrated by epidemiological studies, whereas the shared genetic liability remains largely unknown. Here we establish an atlas of the shared genetic architecture between 10 ADs or related traits and 11 GIDs, comprehensively investigating the comorbidity-associated genomic regions, cell types, genes and genetically predicted causality. Although distinct genetic correlations between AD-GID are observed, including 14 genome-wide and 28 regional correlations, genetic factors of Crohn's disease (CD), ulcerative colitis (UC), celiac disease and asthma subtypes are converged on CD4 T cells consistently across relevant tissues.
