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Chronic obstructive pulmonary disorder COPD is a highly prevalent disease making it among one of the leading causes of death around the world. Lung function measurement is used as an indicator for risk prediction for COPD. There are several GWAS performed across multiple populations to measure lung function and identify risk-associated loci with COPD. The functional interpretation of GWAS results is yet to be explored. Population-specific GWAS shows that every population shows different ancestral genetic compositions causing it to show differences in phenotype for the same disease-associated loci reported in different populations. Therefore, in order to identify true causal variants, post GWAS variant and gene prioritization is required. In this study, we have utilized summary statistics results from two different population GWAS for lung function and performed a post-GWAS comparative analysis.
The study aimed to identify functional causal variants associated with disease using SNP and gene prioritization for each population GWAS. This study further aimed to perform comparative analysis and identify similar as well as unique genes and SNPs associated with COPD in each population group population. The target population GWAS utilized in this study were the Japanese and European populations. The Japanese population and European population GWAS results were re-evaluated using FUMA (v1.3.8) online tool. This study identified 20 independent significant SNPs and 4 lead SNPs in 3 genomic risk loci in Japanese GWAS while 60 independent SNPs, and 25 lead SNPs in 21 genomic risk loci in the European population. Among these identified SNPs, 50 and 84 predicted causal genes were prioritized by gene mapping in Japanese and European datasets respectively. 39 novel genes out of 50 mapped genes in the Japanese dataset and 12 novel genes out of 84 prioritized genes were identified.
The results for each population GWAS analysis were compared which revealed 28 prioritized genes similar in both populations’ prioritized gene lists. These prioritized genes were then further analyzed for functional annotation and gene expression enrichment analysis. The study identified important possible causal genes including DDAH2 which encodes for Dimethylarginine Dimethylaminohydrolase 2 enzyme involved in nitric oxide regulation, an important pathway associated with COPD prioritized in both population data analyses. Overall, our findings identified novel possible causal genes in both populations and demonstrated similar genes and pathways across both population datasets. These findings will help in exploring genetic complexity across multiple populations thus aiding in the generalizability of disease diagnosis and treatment.

The problem of finding novel chemistry, such as new antibiotics, colorants, or other chemical pathways, is always relevant for humanity. Genomic data has been an incredibly useful resource for finding new biosynthetic gene clusters (BGCs). The collection of metagenomic data has increased in the last 20 years; recent studies offer as public data more than 10,000 metagenomes of the Earth. In genomic data, EvoMining is a tool that has been used as a strategy to discover new enzymes recruited by the specialized metabolism. EvoMining has not been used in metagenomic data despite available public datasets leaving tons of BGCs wasted and unexplored. To connect EvoMining with metagenomic data, we developed a Bioconductor open-source package that enables this connection.

One of the environments that scientists have a particular interest in is polluted waters. This work uses ocean samples of hydrocarbon-polluted zones also to elucidate the microorganism’s novel solutions to deal with this problem. To explore bacteria novel-BGCs, we use an Evomining adaptation to metagenomic data. To find enzyme families repurposed into specialized metabolism, Evomining uses evolutionary-based distances in genomes. In enzyme families from conserved metabolism, EvoMining finds those families that have recently expanded their copy number on genomes in a given lineage. After comparison with a database of experimentally validated BGCs, EvoMining predicts novel natural products become an alternative strategy to analyze metagenomic information.

The difficulty of using EvoMining on metagenomic data is that there is no longer knowledge about copy numbers on a genome, and taxonomic classification may not be precise. Nevertheless, after taxonomic assignation, given an enzyme family, metagenomic information can be complemented with genomic data, allowing one to take advantage of metagenomic data that would be wasted otherwise.

Metagenomics studies the genetic content of entire communities of microorganisms that live in a certain environment. These microorganisms interact to improve his survival in an environment full of antagonism. The genomic information of these entire communities can serve us to discover new chemical pathways. This MetaEvoMining tool allows us to explore data from contaminated oceans and will help other researchers to mine the genomic information and find useful knowledge of huge amounts of metagenomic data.