Single Nucleotide Variants in Transcription Factors Associate More Tightly with Phenotype than with Gene Expression

AbstractMany single nucleotide variants (SNVs) associated with human traits and genetic diseases are thought to alter the activity of existing regulatory elements. Some SNVs may also create entirely new regulatory elements which change gene expression, but the mechanism by which they do so is largely unknown. Here we show that a single base change in an otherwise unremarkable region of the human α-globin cluster creates an entirely new promoter and an associated unidirectional transcript. This SNV downregulates α-globin expression causing α-thalassaemia. Of note, the new promoter lying between the α-globin genes and their associated super-enhancer disrupts their interaction in an orientation-dependent manner. Together these observations show how both the order and orientation of the fundamental elements of the genome determine patterns of gene expression and support the concept that active genes may act to disrupt enhancer-promoter interactions in mammals as in Drosophila. Finally, these findings should prompt others to fully evaluate SNVs lying outside of known regulatory elements as causing changes in gene expression by creating new regulatory elements.

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Landscape of allele-specific transcription factor binding in the human genome

10.1101/2020.10.07.327643 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sergey Abramov ◽

Alexandr Boytsov ◽

Dariia Bykova ◽

Dmitry D. Penzar ◽

Ivan Yevshin ◽

...

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Molecular Mechanisms ◽

Specific Binding ◽

Transcription Factor Binding ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Specific Transcription Factor ◽

Factor Binding ◽

Allele Specific

AbstractSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

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Genomic properties of structural variants and short tandem repeats that impact gene expression and complex traits in humans

10.1101/714477 ◽

2019 ◽

Cited By ~ 3

Author(s):

David Jakubosky ◽

Matteo D’Antonio ◽

Marc Jan Bonder ◽

Craig Smail ◽

Margaret K.R. Donovan ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Short Tandem Repeats ◽

Tandem Repeats ◽

Evolutionary Constraint ◽

Structural Variants ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Genomic Locations ◽

Short Tandem

AbstractStructural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we show that different SV classes and STRs differentially impact gene expression and complex traits. Functional differences between SV classes and STRs include their genomic locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We also identified a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and showed they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that impact gene expression and human traits.

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Finding a suitable library size to call variants in RNA-seq

10.1101/2019.12.18.881870 ◽

2019 ◽

Author(s):

Anna Quaglieri ◽

Christoffer Flensburg ◽

Terence P Speed ◽

Ian J Majewski

Keyword(s):

Gene Expression ◽

Cancer Biology ◽

Expression Patterns ◽

Cancer Type ◽

Rna Seq ◽

Single Nucleotide Variants ◽

Library Size ◽

Single Nucleotide ◽

Fundamental Factor ◽

Comparable Performance

AbstractBackgroundRNA-Seq allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. While several studies analyse the effect that library size has on differential expression analyses, sensitivity analysis for variant detection has received far less attention.ResultsWe simulated shallower sequencing depths by downsampling 45 AML samples that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%). The sensitivity in recovering indels varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort, assessing the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 3% average loss in sensitivity using 40M fragments.ConclusionsBetween 30M and 40M fragments are needed to recover 90%-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.

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Impact of common regulatory single-nucleotide variants on gene expression profiles in whole blood

European Journal of Human Genetics ◽

10.1038/ejhg.2012.106 ◽

2012 ◽

Vol 21 (1) ◽

pp. 48-54 ◽

Cited By ~ 32

Author(s):

Divya Mehta ◽

Katharina Heim ◽

Christian Herder ◽

Maren Carstensen ◽

Gertrud Eckstein ◽

...

Keyword(s):

Gene Expression ◽

Whole Blood ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Single Nucleotide Variants ◽

Single Nucleotide

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Landscape of allele-specific transcription factor binding in the human genome

Nature Communications ◽

10.1038/s41467-021-23007-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Sergey Abramov ◽

Alexandr Boytsov ◽

Daria Bykova ◽

Dmitry D. Penzar ◽

Ivan Yevshin ◽

...

Keyword(s):

Transcription Factor ◽

Transcription Factors ◽

Molecular Mechanisms ◽

Specific Binding ◽

Transcription Factor Binding ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Specific Transcription Factor ◽

Factor Binding ◽

Allele Specific

AbstractSequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

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Determination of novel members in the Drosophila melanogaster anteriorposterior patterning system using natural variation

10.1101/319434 ◽

2018 ◽

Author(s):

Ashley A. Jermusyk ◽

Sarah E. Gharavi ◽

Aslesha S. Tingare ◽

Gregory T. Reeves

Keyword(s):

Gene Expression ◽

Drosophila Melanogaster ◽

Transcription Factors ◽

Natural Variation ◽

Expression Patterns ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Gap Gene ◽

Drosophila Genetic Reference Panel

AbstractThe anterior-posterior axis of the developing Drosophila melanogaster embryo is patterned by a well-studied gene regulatory network called the Gap Gene Network. This network acts to buffer the developing pattern against noise, thereby minimizing errors in gene expression and preventing patterning defects.In this paper, we sought to discover novel regulatory regions and transcription factors acting in a subset of the Gap network using a selection of wild-caught fly lines derived from the Drosophila Genetic Reference Panel (DGRP). The fly lines in the DGRP contain subtle genomic differences due to natural variation; we quantified the differences in positioning of gene expression borders of two anterior-poster patterning genes, Krüppel (Kr) and Even-skipped in 13 of the DGRP lines. The differences in the positions of Krüppel and Even-skipped were then correlated to specific single nucleotide polymorphisms and insertions/deletions within the select fly lines. Putative enhancers containing these genomic differences were validated for their ability to produce expression using reporter constructs and analyzed for possible transcription factor binding sites. The identified transcription factors were then perturbed and the resulting Eve and Kr positioning was determined. In this way, we found medea, ultraspiracle, glial cells missing, and orthopedia effect Kr and Eve positioning in subtle ways, while knock-down of pangolin produces significant shifts in Kr and subsequent Eve expression patterns. Most importantly this study points to the existence of many additional novel members that have subtle effects on this system and the degree of complexity that is present in patterning the developing embryo.

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Defining the Yeast Resistome through in vitro Evolution and Whole Genome Sequencing

10.1101/2021.02.17.430112 ◽

2021 ◽

Author(s):

Sabine Ottilie ◽

Madeline R. Luth ◽

Erich Hellemann ◽

Gregory M. Goldgof ◽

Eddy Vigil ◽

...

Keyword(s):

Drug Resistance ◽

Transcription Factors ◽

Enrichment Analysis ◽

Neutral Evolution ◽

Whole Genome ◽

Genetic Determinants ◽

Single Nucleotide Variants ◽

Whole Genome Analysis ◽

Single Nucleotide

SummaryIn vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in the model microbe, Saccharomyces cerevisiae. Analysis of 355 curated, laboratory-evolved clones, resistant to 80 different compounds, demonstrates differences in the types of mutations that are identified in selected versus neutral evolution and reveals numerous new, compound-target interactions. Through enrichment analysis we further identify a set of 137 genes strongly associated with or conferring drug resistance as indicated by CRISPR-Cas9 engineering. The set of 25 most frequently mutated genes was enriched for transcription factors and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function, single nucleotide variants found in 170-amino-acid domains in two Zn2C6 transcription factors, YRR1 and YRM1 (p < 1x 10 −100). This remarkable enrichment for transcription factors as drug resistance genes may explain why it is challenging to develop effective antifungal killing agents and highlights their important role in evolution.

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