a-pathway-association-study-tool-for-gwas-analyses-metabolic-pathway

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

Mississippi State University

Long-distance insular dispersal is associated with divergence and speciation because of founder effects and strong genetic drift. The cotton tribe (Gossypieae) has experienced multiple transoceanic dispersals, generating an aggregate geographic range that encompasses much of the tropics and subtropics worldwide. Two genera in the Gossypieae, Kokia and Gossypioides, exhibit a remarkable geographic disjunction, being restricted to the Hawaiian Islands and Madagascar/East Africa, respectively. We assembled and use de novo genome sequences to address questions regarding the divergence of these two genera from each other and from their sister-group, Gossypium. In addition, we explore processes underlying the genome downsizing that characterizes Kokia and Gossypioides relative to other genera in the tribe. Using 13,000 gene orthologs and synonymous substitution rates, we show that the two disjuncts last shared a common ancestor ∼5 Ma, or half as long ago as their divergence from Gossypium. We report relative stasis in the transposable element fraction. In comparison to Gossypium, there is loss of ∼30% of the gene content in the two disjunct genera and a history of genome-wide accumulation of deletions. In both genera, there is a genome-wide bias toward deletions over insertions, and the number of gene losses exceeds the number of gains by ∼2- to 4-fold. The genomic analyses presented here elucidate genomic consequences of the demographic and biogeographic history of these closest relatives of Gossypium, and enhance their value as phylogenetic outgroups.

Comparative Genomics of an Unusual Biogeographic Disjunction in the Cotton Tribe (Gossypieae) Yields Insights into Genome Downsizing.

The quality of data generated by high-throughput DNA sequencing tools must be rapidly assessed in order to determine how useful the data may be in making biological discoveries; higher quality data leads to more confident results and conclusions. Due to the ever-increasing size of data sets and the importance of rapid quality assessment, tools that analyze sequencing data should quickly produce easily interpretable graphics. Quack addresses these issues by generating information-dense visualizations from FASTQ files at a speed far surpassing other publicly available quality assurance tools in a manner independent of sequencing technology.

Quack: A quality assurance tool for high throughput sequence data.

We employed phylogenomic methods to study molecular evolutionary processes and phylogeny in the geographically widely dispersed New World diploid cottons (Gossypium, subg. Houzingenia). Whole genome resequencing data (average of 33× genomic coverage) were generated to reassess the phylogenetic history of the subgenus and provide a temporal framework for its diversification. Phylogenetic analyses indicate that the subgenus likely originated following transoceanic dispersal from Africa about 6.6 Ma, but that nearly all of the biodiversity evolved following rapid diversification in the mid-Pleistocene (0.5-2.0 Ma), with multiple long-distance dispersals required to account for range expansion to Arizona, the Galapagos Islands, and Peru. Comparative analyses of cpDNAversus nuclear data indicate that this history was accompanied by several clear cases of interspecific introgression. Repetitive DNAs contribute roughly half of the total 880 Mb genome, but most transposable element families are relatively old and stable among species. In the genic fraction, pairwise synonymous mutation rates average 1% per Myr, with nonsynonymous changes being about seven times less frequent. Over 1.1 million indels were detected and phylogenetically polarized, revealing a 2-fold bias toward deletions over small insertions. We suggest that this genome down-sizing bias counteracts genome size growth by TE amplification and insertions, and helps explain the relatively small genomes that are restricted to this subgenus. Compared with the rate of nucleotide substitution, the rate of indel occurrence is much lower averaging about 17 nucleotide substitutions per indel event.

Insights into the Evolution of the New World Diploid Cottons (Gossypium, Subgenus Houzingenia) Based on Genome Sequencing.

Maize (Zea mays mays) oil is a rich source of polyunsaturated fatty acids (FAs) and energy, making it a valuable resource for human food, animal feed, and bio-energy. Although this trait has been studied via conventional genome-wide association study (GWAS), the single nucleotide polymorphism (SNP)-trait associations generated by GWAS may miss the underlying associations when traits are based on many genes, each with small effects that can be overshadowed by genetic background and environmental variation. Detecting these SNPs statistically is also limited by the levels set for false discovery rate. A complementary pathways analysis that emphasizes the cumulative aspects of SNP-trait associations, rather than just the significance of single SNPs, was performed to understand the balance of lipid metabolism, conversion, and catabolism in this study. This pathway analysis indicated that acyl-lipid pathways, including biosynthesis of wax esters, sphingolipids, phospholipids and flavonoids, along with FA and triacylglycerol (TAG) biosynthesis, were important for increasing oil and FA content. The allelic variation found among the genes involved in many degradation pathways, and many biosynthesis pathways leading from FAs and carbon partitioning pathways, was critical for determining final FA content, changing FA ratios and, ultimately, to final oil content. The pathways and pathway networks identified in this study, and especially the acyl-lipid associated pathways identified beyond what had been found with GWAS alone, provide a real opportunity to precisely and efficiently manipulate high-oil maize genetic improvement.

Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis.

One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier.

Keanu: a novel visualization tool to explore biodiversity in metagenomes.

New approaches, like the Adverse Outcome Pathway (AOP) framework, have been developed to describe how chemicals cause toxicity by linking in vitro assays to adverse health outcomes. However, approaches, tools and resources for development of AOPs have not been well described. Here we review information resources for AOP development and define a streamlined process for linking a chemical to an existing AOP. We propose a four step process to facilitate AOP development: link the uncharacterized chemical directly to Molecular Initiating Events, Key Events, or Adverse Outcomes; identify analogs with toxicological information for the uncharacterized chemical; link the characterized chemical (initial chemical if characterized, a characterized analog if initial chemical is not) to Molecular Initiating Events, Key Events, or Adverse Outcomes; and identify AOPs that contain the Molecular Initiating Events, Key Events, or Adverse Outcomes that were found in Steps 1 and 3. The process and library of informational resources proposed and tested here served as the foundation for an informational online tool (AOPERA) that helps practitioners identify their current-state knowledge gaps, navigate the four-step process, and connect to relevant resources. AOPERA can be found at https://igbb.github.io/AOPERA_HTML. Additionally, we anticipate that by simplifying and standardizing the process of linking a chemical to a known AOP, we will lower the barrier to entry for this objective and increase its accessibility to new practitioners. In turn, this may increase the demand for new or improved AOPs to which practitioners can link chemicals, thereby contributing to the expansion of the library of known AOPs.

AOPERA: A proposed methodology and inventory of effective tools to link chemicals to adverse outcome pathways.

In recent years, a bioinformatics method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and successfully used to find significant pathways and mechanisms explaining phenotypic traits of interest in plants. However, the many scripts implementing this method were not straightforward to use, had to be customized for each project, required user supervision, and took more than 24 h to process data. PAST (Pathway Association Study Tool), a new implementation of this method, has been developed to address these concerns. PAST has been implemented as a package for the R language. Two user-interfaces are provided; PAST can be run by loading the package in R and calling its methods, or by using an R Shiny guided user interface. In testing, PAST completed analyses in approximately half an hour to one hour by processing data in parallel and produced the same results as the previously developed method. PAST has many user-specified options for maximum customization. Thus, to promote a powerful new pathway analysis methodology that interprets GWAS data to find biological mechanisms associated with traits of interest, we developed a more accessible, efficient, and user-friendly tool. These attributes make PAST accessible to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotypes.

Adam Thrash

Institute for Genomics,
Biocomputing & Biotechnology,
Institute for Genomics, Biocomputing & Biotechnology

Mississippi State University

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Adam Thrash

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Institute for Genomics,Biocomputing & Biotechnology,Institute for Genomics, Biocomputing & Biotechnology

Mississippi State University

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Institute for Genomics,
Biocomputing & Biotechnology,
Institute for Genomics, Biocomputing & Biotechnology