Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analysed across 11 populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping, and gene-based association analyses to identify candidate severe malaria resistance genes. We further applied rare variant analysis to raw GWAS datasets (N = 11,000) of three malaria endemic populations including Kenya, Malawi, and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. We performed network and pathway analyses to investigate their shared biological functions. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci, while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signalling elements, and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes, highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.

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Functional analysis of genome-wide dataset from 17000 individuals identifies multiple candidate malaria resistance genes enriched in malaria pathogenic pathways

10.1101/2020.08.15.20175471 ◽

2020 ◽

Author(s):

Delesa Damena ◽

Francis Agamah ◽

Peter O Kimathi ◽

Ntumba E Kabongo ◽

Hundaol Girma ◽

...

Keyword(s):

Functional Analysis ◽

Severe Malaria ◽

Resistance Genes ◽

Population Genetic ◽

Association Studies ◽

Chromatin Interaction ◽

Genome Wide Association Studies ◽

Biological Functions ◽

Population Genetic Analysis ◽

Genome Wide

Recent genome-wide association studies (GWASs) of severe malaria have identified several association variants. However, much about the underlying biological functions are yet to be discovered. Here, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics(N=17,000) meta-analyzed across eleven populations in malaria endemic regions. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping and gene-based association analyses to identify candidate severe malaria resistance genes. We performed network and pathway analyses to investigate their shared biological functions. We further applied rare variant analysis to raw GWAS datasets (N=11,000) of three malaria endemic populations including Kenya, Malawi and Gambia and performed various population genetic structures of the identified genes in the three populations and global populations. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signaling elements and neuronal systems. Our population genetic analysis revealed that the minor allele frequencies (MAF) of the single nucleotide polymorphisms (SNPs) residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes; highlighting the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways.

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SNP2GO: Functional Analysis of Genome-Wide Association Studies

Genetics ◽

10.1534/genetics.113.160341 ◽

2014 ◽

Vol 197 (1) ◽

pp. 285-289 ◽

Cited By ~ 18

Author(s):

David Szkiba ◽

Martin Kapun ◽

Arndt von Haeseler ◽

Miguel Gallach

Keyword(s):

Functional Analysis ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Genome-wide association study in a Korean population identifies six novel susceptibility loci for rheumatoid arthritis

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-217663 ◽

2020 ◽

Vol 79 (11) ◽

pp. 1438-1445

Author(s):

Young-Chang Kwon ◽

Jiwoo Lim ◽

So-Young Bang ◽

Eunji Ha ◽

Mi Yeong Hwang ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Genome Wide Association Study ◽

Association Studies ◽

Large Fraction ◽

Genome Wide Association ◽

Chromatin Interaction ◽

Genome Wide Association Studies ◽

Bioinformatics Analyses ◽

Specific Expression ◽

Genome Wide

ObjectiveGenome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case–control population.MethodsWe newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations.ResultsWe identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta<5×10−8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases.ConclusionThis study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.

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Genome-wide Identification of Powdery Mildew Resistance in Common Bean

10.21203/rs.3.rs-27731/v2 ◽

2020 ◽

Author(s):

Papias Hongera Binagwa ◽

Sy M. Traore ◽

Marceline Egnin ◽

Gregory C. Bernard ◽

Inocent Ritte ◽

...

Keyword(s):

Disease Resistance ◽

Common Bean ◽

Resistance Genes ◽

Agronomic Traits ◽

Association Studies ◽

Significant Snps ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Disease Resistance Protein ◽

Resistance Protein

Abstract Background: Genome-wide association studies (GWAS) was utilized to detect genetic variations related to the powdery mildew (PM) resistance and several agronomic traits in common bean. However, its application in common bean and the PM interactions to identify genes and their location in the common bean genome has not been fully addressed. Results: Genome-wide association studies (GWAS) through marker-trait association are useful molecular tools for the identification of disease resistance and other agronomic traits. SNP genotyping with a BeadChip containing 5398 SNPs was used to detect genetic variations related to resistance to PM disease in a panel of 206 genotypes grown under field conditions for two consecutive years. Significant SNPs identified on chromosomes 4 and 10 (Pv04 and Pv10) were repeatable, confirming the reliability of the phenotypic data scored from the genotypes grown in two locations within two years. A cluster of resistance genes was revealed on the chromosome 4 of common bean genome among which CNL and TNL like resistance genes were identified. Furthermore, two resistance genes Phavu_010G1320001g and Phavu_010G136800g were also identified on Pv10; further sequence analysis showed that these genes were homologs to the Arabidopsis disease resistance protein (RLM1A-like) and the putative disease resistance protein (At4g11170.1), respectively. Two LRR receptor-like kinases (RLK) were also identified on Pv11 in samples collected in 2018 only. Many genes encoding auxin-responsive protein, TIFY10A protein, growth-regulating factor 5-like, ubiquitin-like protein, cell wall protein RBR3-like protein related to PM resistance were identified nearby significant SNPs. These results suggested that the resistance to PM pathogen involves a network of many genes constitutively co-expressed and may generate several layers of defense barriers or inducible reactions.Conclusion: Our results provide new insights into common bean and PM interactions, and revealed putative resistance genes as well as their location on common bean genome that could be used for marker-assisted selection, functional genomic study approaches to confirm the role of these putative genes; hence, developing common bean resistance lines to the PM disease.

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Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Scientific Reports ◽

10.1038/s41598-021-03864-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fabricio Almeida-Silva ◽

Thiago M. Venancio

Keyword(s):

Candidate Genes ◽

Resistance Genes ◽

Association Studies ◽

Fungal Species ◽

Genome Wide Association ◽

Economic Losses ◽

Genome Wide Association Studies ◽

Link Type ◽

Physical Defense ◽

Genome Wide

AbstractSoybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application (https://soyfungigcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyFungiGCN) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

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Genome-wide association experiments have uncovered a slew of cardiometabolic trait-associated variants. This information can be useful in the implementation of new diagnostic and treatment strategies.

10.31219/osf.io/4vws8 ◽

2021 ◽

Author(s):

Moataz Dowaidar

Keyword(s):

Association Studies ◽

Treatment Strategies ◽

Noncoding Region ◽

Genome Wide Association ◽

Diagnosis And Treatment ◽

Genome Wide Association Studies ◽

Biological Functions ◽

Treatment Methods ◽

Genome Wide ◽

Cardiometabolic Disorders

A slew of cardiometabolic trait-associated variants have been discovered thanksto genome-wide association studies. The majority of imputed SNPs are locatedin the noncoding region, suggesting that noncoding genes are significant. In thissense, lncRNAs are gaining attention due to their biological functions, andrising data suggests that lncRNAs could be able to assist researchers in betterunderstanding the pathogenesis of cardiometabolic disorders. The existing stateof knowledge about lncRNAs in cardiometabolic disorders is still in its earlystages, and further research will help us better understand how the noncodingsector regulates us. This knowledge would be important in the development ofmodern diagnosis and treatment methods.

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