Finding differentially expressed sRNA-Seq regions with srnadiff

Small RNAs (sRNAs) encompass a great variety of molecules of different kinds, such as microRNAs, small interfering RNAs, Piwi-associated RNA, among others. These sRNAs have a wide range of activities, which include gene regulation, protection against virus, transposable element silencing, and have been identified as a key actor in determining the development of the cell. Small RNA sequencing is thus routinely used to assess the expression of the diversity of sRNAs, usually in the context of differentially expression, where two conditions are compared. Tools that detect differentially expressed microRNAs are numerous, because microRNAs are well documented, and the associated genes are well defined. However, tools are lacking to detect other types of sRNAs, which are less studied, and whose precursor RNA is not well characterized. We present here a new method, called srnadiff, which finds all kinds of differentially expressed sRNAs. To the extent of our knowledge, srnadiff is the first tool that detects differentially expressed sRNAs without the use of external information, such as genomic annotation or additional sequences of sRNAs.

Transcription Factors in the Fungus Aspergillus nidulans: Markers of Genetic Innovation, Network Rewiring and Conflict between Genomics and Transcriptomics

Gene regulatory networks (GRNs) are shaped by the democratic/hierarchical relationships among transcription factors (TFs) and associated proteins, together with the cis-regulatory sequences (CRSs) bound by these TFs at target promoters. GRNs control all cellular processes, including metabolism, stress response, growth and development. Due to the ability to modify morphogenetic and developmental patterns, there is the consensus view that the reorganization of GRNs is a driving force of species evolution and differentiation. GRNs are rewired through events including the duplication of TF-coding genes, their divergent sequence evolution and the gain/loss/modification of CRSs. Fungi (mainly Saccharomycotina) have served as a reference kingdom for the study of GRN evolution. Here, I studied the genes predicted to encode TFs in the fungus Aspergillus nidulans (Pezizomycotina). The analysis of the expansion of different families of TFs suggests that the duplication of TFs impacts the species level, and that the expansion in Zn2Cys6 TFs is mainly due to dispersed duplication events. Comparison of genomic annotation and transcriptomic data suggest that a significant percentage of genes should be re-annotated, while many others remain silent. Finally, a new regulator of growth and development is identified and characterized. Overall, this study establishes a novel theoretical framework in synthetic biology, as the overexpression of silent TF forms would provide additional tools to assess how GRNs are rewired.

LocusZoom.js: Interactive and embeddable visualization of genetic association study results

LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets.AvailabilityLocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/[email protected]

A genome-wide association study for nonalcoholic fatty liver disease identifies novel genetic loci and trait-relevant candidate genes in the Million Veteran Program

Nonalcoholic fatty liver disease (NAFLD) is a prevalent, heritable trait that can progress to cancer and liver failure. Using our recently developed proxy definition for NAFLD based on chronic liver enzyme elevation without other causes of liver disease or alcohol misuse, we performed a multi-ancestry genome-wide association study in the Million Veteran Program with 90,408 NAFLD cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance of which 70 were novel, with an additional European-American specific and two African-American specific loci. Twelve of these loci were also significantly associated with quantitative hepatic fat on radiological imaging (n=44,289). Gene prioritization based on coding annotations, gene expression from GTEx, and functional genomic annotation identified candidate genes at 97% of loci. At eight loci, the allele associated with lower gene expression in liver was also associated with reduced risk of NAFLD, suggesting potential therapeutic relevance. Functional genomic annotation and gene-set enrichment demonstrated that associated loci were relevant to liver biology. We expand the catalog of genes influencing NAFLD, and provide a novel resource to understand its disease initiation, progression and therapy.

GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci

Background The development of high-throughput sequencing and genotyping methodologies has enabled the identification of thousands of genomic regions associated with several complex traits. The integration of multiple sources of biological information is a crucial step required to better understand patterns regulating the development of these traits. Findings Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package developed for the accurate annotation of genes and quantitative trait loci (QTLs) located in regions identified in common genomic analyses performed in livestock, such as genome-wide association studies and transcriptomics using RNA sequencing. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies), and QTL enrichment in different livestock species such as cattle, pigs, sheep, and chickens. Conclusions Consequently, GALLO is a useful package for annotation, identification of hidden patterns across datasets, and data mining previously reported associations, as well as the efficient examination of the genetic architecture of complex traits in livestock.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

Motivation: Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account. Results: We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

1486. Phylogenetic and alpha toxin variant analyses of Staphylococcus aureus strains isolated from patients during the SAATELLITE study

Background Suvratoxumab is a human monoclonal antibody that neutralizes S. aureus (SA) alpha toxin (AT). SAATELLITE, a phase 2 study of the safety and efficacy of suvratoxumab for reducing the incidence of SA pneumonia (NCT02296320), was conducted within the consortium for Combatting Bacterial Resistance in Europe. Methods A total of 304 SA isolates (baseline, onset and last available isolates from suspected serious bacterial infections, SSBIs) collected from the lower respiratory tract samples from 165 subjects during SAATELLITE were subjected to whole genome sequencing. AT gene (hla) sequences were translated and amino acid variation was identified in comparison to the reference SA USA300 FPR3757. Phylogenetic analysis, genomic annotation and ST analysis were performed. AT expression in SA culture supernatants was performed by ELISA. Representative isolates with novel AT subtypes that had not been identified in previous studies were tested for hemolytic activity and suvratoxumab neutralizing activity. Wilcoxon rank sum test and Fisher’s exact test were performed, respectively: a) to compare difference in baseline AT expression in relation to SA pneumonia incidence; b) to evaluate the association between occurrence of AT stop codons and incidence of SA pneumonia at baseline, as well as the association between occurrence of AT stop codons and treatment arms at post baseline. Results We identified a total of 44 sequence types (STs) and 21 unique AT subtypes, 7 of which have not been described previously. No substitutions were located in the suvratoxumab binding region and all novel AT subtypes displaying lytic activity were neutralized by suvratoxumab. We detected stop codons Q113B and W205B in AT sequences in 53 and 2 SA isolates, respectively. We uncovered no significant associations of: 1) baseline AT expression with SA pneumonia incidence [p=0.967]; 2) occurrence of AT gene stop codon with either SA pneumonia incidence [p >0.999] or suvratoxumab treatment [p=0.103; lower frequency of stop codons in suvratoxumab arm versus placebo]. Conclusion Our data indicated that: 1) suvratoxumab target region in (AT) remains conserved; 2) suvratoxumab is active against all AT variants identified to date; 3) suvratoxumab did not exert pressure on SA clinical isolates for selection of escape mutants. Disclosures David E. Tabor, PhD, AstraZeneca (Employee, Shareholder) Andrey Tovchigrechko, PhD, AstraZeneca (Employee, Shareholder)KitePharma, a Gilead company (Employee, Shareholder) Bret R. Sellman, PhD, AstraZeneca (Employee, Shareholder) Michael McCarthy, n/a, AstraZeneca (Employee) Kathryn Shoemaker, MS, AstraZeneca (Employee) Hasan S. Jafri, MD, FAAP, AstraZeneca (Employee) Mark T. Esser, PhD, AstraZeneca (Employee) Alexey Ruzin, PhD, AstraZeneca (Employee, Shareholder)

A response to Yurko et al: H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives

Hi-C coupled multimarker analysis of genomic annotation (H-MAGMA) was initially developed to advance MAGMA by assigning non-coding SNPs to their cognate genes based on threedimensional chromatin architecture. Yurko and colleagues raised concerns that the SNP-wise mean gene-analysis model of MAGMA may allow inflation in type I errors. Accordingly, we updated MAGMA and found that the updated version (MAGMA v.1.08) effectively controls for error rate inflation. Intrigued by this result, H-MAGMA was also updated by implementing MAGMA v.1.08. As expected, H-MAGMA v.1.08 detected a smaller set of risk genes than its original version (v.1.07), but the overall statistical architecture remained largely unchanged between v.1.07 and v.1.08. H-MAGMA v.1.08 was then applied to genome-wide association studies (GWAS) of five psychiatric disorders, from which we recapitulated our previous findings that psychiatric disorder risk genes display neuronal and prenatal enrichment. Therefore, issues raised by Yurko and colleagues can be overcome by using (H-)MAGMA v.1.08.

H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives

The ‘snp-wise mean model’ of Multi-marker Analysis of GenoMic Annotation is often used to perform gene-level testing for association with disease and other phenotypes. This methodology, in turn, forms the foundation for H-MAGMA. Unfortunately, that foundation is unsound, with implications for H-MAGMA results published in Nature Neuroscience regarding genes associated with psychiatric disorders: e.g., only 125 of H-MAGMA’s 275 reported discoveries for autism replicate when the foundation’s flaws are corrected.