Orthogonal control of expression mean and variance by epigenetic features at different genomic loci by Siddharth S Dey, Jonathan E Foley, Prajit Limsirichai, David V Schaffer, Adam P Arkin

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Identifying the genetic determinants of transcription factor activity by Eunjee Lee, Harmen J Bussemaker

“…Currently, only a tiny fraction of this genetic variance can be mechanistically accounted for. The influence of trans‐acting polymorphisms on gene expression traits is often mediated by transcription factors (TFs). …”
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Transcription Analysis of the Myometrium of Labouring and Non-Labouring Women. by Gemma C Sharp, James L Hutchinson, Nanette Hibbert, Tom C Freeman, Philippa T K Saunders, Jane E Norman

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A telomere-to-telomere Eucalyptus regnans genome: unveiling haplotype variance in structure and genes within one of the world’s tallest trees by Scott Ferguson, Yoav D Bar-Ness, Justin Borevitz, Ashley Jones

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Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli by Karl Kochanowski, Luca Gerosa, Simon F Brunner, Dimitris Christodoulou, Yaroslav V Nikolaev, Uwe Sauer

“…Using an approximate mathematical description of promoter activity, we dissect the contribution of global and specific transcriptional regulation. About 70% of the total variance in promoter activity across conditions was explained by global transcriptional regulation. …”
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The transcription factor TaNF-YB4 overexpression in wheat increases plant vigor and yield by Arooj Azhar, Sidra Ijaz, Ayesha Jabeen, Attiya Kamal, Aftab Bashir, Kauser Abdulla Malik

“…This study aimed to improve wheat yield by overexpressing the TaNF-YB4 transcription factor, which is involved in carbon assimilation and stress tolerance. …”
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Genetic Variance in Uncoupling Protein 2 in Relation to Obesity, Type 2 Diabetes, and Related Metabolic Traits: Focus on the Functional −866G>A Promoter Variant (rs659366) by Louise T. Dalgaard

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Cell-free DNA profiles of dermatomyositis and its potential role in discriminating phenotypes by Zhuang-li Tang, Peng-yu Chen, Heng Zhang, Hua-li Cao, Ru Dai, Yu-chen Lou, Yan-hong Sun, Yuan Zhou, Xue-yan Chen, Mei-jie Zhang, Ya-qi Wang, Xiao-yong Man

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Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. by Anubha Mahajan, Xueling Sim, Hui Jin Ng, Alisa Manning, Manuel A Rivas, Heather M Highland, Adam E Locke, Niels Grarup, Hae Kyung Im, Pablo Cingolani, Jason Flannick, Pierre Fontanillas, Christian Fuchsberger, Kyle J Gaulton, Tanya M Teslovich, N William Rayner, Neil R Robertson, Nicola L Beer, Jana K Rundle, Jette Bork-Jensen, Claes Ladenvall, Christine Blancher, David Buck, Gemma Buck, Noël P Burtt, Stacey Gabriel, Anette P Gjesing, Christopher J Groves, Mette Hollensted, Jeroen R Huyghe, Anne U Jackson, Goo Jun, Johanne Marie Justesen, Massimo Mangino, Jacquelyn Murphy, Matt Neville, Robert Onofrio, Kerrin S Small, Heather M Stringham, Ann-Christine Syvänen, Joseph Trakalo, Goncalo Abecasis, Graeme I Bell, John Blangero, Nancy J Cox, Ravindranath Duggirala, Craig L Hanis, Mark Seielstad, James G Wilson, Cramer Christensen, Ivan Brandslund, Rainer Rauramaa, Gabriela L Surdulescu, Alex S F Doney, Lars Lannfelt, Allan Linneberg, Bo Isomaa, Tiinamaija Tuomi, Marit E Jørgensen, Torben Jørgensen, Johanna Kuusisto, Matti Uusitupa, Veikko Salomaa, Timothy D Spector, Andrew D Morris, Colin N A Palmer, Francis S Collins, Karen L Mohlke, Richard N Bergman, Erik Ingelsson, Lars Lind, Jaakko Tuomilehto, Torben Hansen, Richard M Watanabe, Inga Prokopenko, Josee Dupuis, Fredrik Karpe, Leif Groop, Markku Laakso, Oluf Pedersen, Jose C Florez, Andrew P Morris, David Altshuler, James B Meigs, Michael Boehnke, Mark I McCarthy, Cecilia M Lindgren, Anna L Gloyn, T2D-GENES consortium and GoT2D consortium

“…Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.…”
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Luminal epithelial cells integrate variable responses to aging into stereotypical changes that underlie breast cancer susceptibility by Rosalyn W Sayaman, Masaru Miyano, Eric G Carlson, Parijat Senapati, Arrianna Zirbes, Sundus F Shalabi, Michael E Todhunter, Victoria E Seewaldt, Susan L Neuhausen, Martha R Stampfer, Dustin E Schones, Mark A LaBarge

“…In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. …”
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Forensic saliva stains aging, using reverse transcription real-time PCR combined with a multidimensional prediction model of specific mRNA markers by Emad Ahmad Mostafa, Eman Abd Elhakim Attia, Nashwa El-Khazragy, Hanan E. Salem

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Chromatin measurements reveal contributions of synthesis and decay to steady‐state mRNA levels by Sylvia C Tippmann, Robert Ivanek, Dimos Gaidatzis, Anne Schöler, Leslie Hoerner, Erik van Nimwegen, Peter F Stadler, Michael B Stadler, Dirk Schübeler

“…In both cases, chromatin‐derived transcription rates explain over 80% of the observed variance in measured RNA levels. …”
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Mammalian gene expression variability is explained by underlying cell state by Robert Foreman, Roy Wollman

“…This variability can come from differential regulation related to cell state (extrinsic) and allele‐specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. …”
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Molecular insights into postharvest seed coat darkening in common beans: a look beyond the P gene by Nishat S. Islam, Nishat S. Islam, Sangeeta Dhaubhadel, Sangeeta Dhaubhadel

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The effect of running and starvation interventions on atherogenic index and Xbp1 gene expression in the endoplasmic reticulum of liver in non-alcoholic fatty liver rats by Mahshad Paziraee, Habib Asgharpour, Asra Askari, Reza Rezaei Shirazi, Neda Aghaei Bahman Beglo

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Dynamics of protein noise can distinguish between alternate sources of gene‐expression variability by Abhyudai Singh, Brandon S Razooky, Roy D Dar, Leor S Weinberger

“…Abstract Within individual cells, two molecular processes have been implicated as sources of noise in gene expression: (i) Poisson fluctuations in mRNA abundance arising from random birth and death of individual mRNA transcripts or (ii) promoter fluctuations arising from stochastic promoter transitions between different transcriptional states. …”
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Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite. by Isaac W Vock, Justin W Mabin, Martin Machyna, Alexandra Zhang, J Robert Hogg, Matthew D Simon

“…EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. …”
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EdSurvey: an R package to analyze large-scale educational assessments data from NCES by Ting Zhang, Paul Bailey, Yuqi Liao, Emmanuel Sikali

“…The analysis functions in EdSurvey account for the use of plausible values for test scores, survey sampling weights, and their associated variance estimator. We describe the capabilities of the package in the context of the 2019 National Assessment of Educational Progress (NAEP) High School Transcript Study.…”
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Finding spatially variable ligand-receptor interactions with functional support from downstream genes by Shiying Li, Ruohan Wang, Sitong Liu, Shuai Cheng Li

“…To identify spatially variable LRIs with activation evidence, we present SPIDER, which constructs cell-cell interaction interfaces constrained by cellular interaction capacity, and profiles and identifies spatially variable interaction (SVI) signals with support from downstream transcript factors via multiple probabilistic models. …”
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Long-range regulatory polymorphisms affecting a GABA receptor constitute a quantitative trait locus (QTL) for social behavior in Caenorhabditis elegans. by Andres Bendesky, Jason Pitts, Matthew V Rockman, William C Chen, Man-Wah Tan, Leonid Kruglyak, Cornelia I Bargmann

“…Fine-mapping with near-isogenic lines localized one QTL, accounting for 5%-8% of the behavioral variance between N2 and CB4856, 3' to the transcript of the GABA neurotransmitter receptor gene exp-1. …”
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