Journal articles on the topic 'Genomic Wide Association Study (GWAS)'
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Shaffer, J. R., E. Feingold, and M. L. Marazita. "Genome-wide Association Studies." Journal of Dental Research 91, no. 7 (May 4, 2012): 637–41. http://dx.doi.org/10.1177/0022034512446968.
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The genomic era of biomedical research has given rise to the genome-wide association study (GWAS) approach, which attempts to discover novel genes affecting an outcome by testing a large number ( i.e., hundreds of thousands to millions) of genetic variants for association. This article discusses the issues surrounding the GWAS approach with emphasis on the prospects and challenges relevant to the oral health research community.
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Wolking, Stefan, Herbert Schulz, Anne T. Nies, Mark McCormack, Elke Schaeffeler, Pauls Auce, Andreja Avbersek, et al. "Pharmacoresponse in genetic generalized epilepsy: a genome-wide association study." Pharmacogenomics 21, no. 5 (April 2020): 325–35. http://dx.doi.org/10.2217/pgs-2019-0179.
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Aim: Pharmacoresistance is a major burden in epilepsy treatment. We aimed to identify genetic biomarkers in response to specific antiepileptic drugs (AEDs) in genetic generalized epilepsies (GGE). Materials & methods: We conducted a genome-wide association study (GWAS) of 3.3 million autosomal SNPs in 893 European subjects with GGE – responsive or nonresponsive to lamotrigine, levetiracetam and valproic acid. Results: Our GWAS of AED response revealed suggestive evidence for association at 29 genomic loci (p <10-5) but no significant association reflecting its limited power. The suggestive associations highlight candidate genes that are implicated in epileptogenesis and neurodevelopment. Conclusion: This first GWAS of AED response in GGE provides a comprehensive reference of SNP associations for hypothesis-driven candidate gene analyses in upcoming pharmacogenetic studies.
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Xiaopeng, Song, Feng Jie, Cui Zixia, Zhang Chuanliang, and Sun Daojie. "Genome-wide association study for anther length in some elite bread wheat germplasm." Czech Journal of Genetics and Plant Breeding 54, No. 3 (September 5, 2018): 109–14. http://dx.doi.org/10.17221/70/2017-cjgpb.
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The anther is a crucial organ for the development of the spike in bread wheat (Triticum aestivum L.). Long anthers contain large amounts of pollen grains; thus, they are favourable for cross-pollination and increase resilience against adverse environmental conditions. We conducted a genome-wide association study (GWAS) of anther length in 305 elite wheat lines evaluated during 2013–2015 in two locations and two growing seasons. The mapping panel was genotyped using a high-density Illumina iSelect 90K single nucleotide polymorphism (SNP) array. The GWAS used 18763 SNPs and identified 17 markers associated with anther length in wheat. The loci were mainly distributed across the chromosomes 3A, 3B and 7B. Further studies are required to determine if these are candidate genomic regions of anther length. In addition, anther length had high heritability, and positive correlations between anther length and grain weight per spike were observed.
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Mei, Hao, Lianna Li, Fan Jiang, Jeannette Simino, Michael Griswold, Thomas Mosley, and Shijian Liu. "snpGeneSets: An R Package for Genome-Wide Study Annotation." G3 Genes|Genomes|Genetics 6, no. 12 (December 1, 2016): 4087–95. http://dx.doi.org/10.1534/g3.116.034694.
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Abstract Genome-wide studies (GWS) of SNP associations and differential gene expressions have generated abundant results; next-generation sequencing technology has further boosted the number of variants and genes identified. Effective interpretation requires massive annotation and downstream analysis of these genome-wide results, a computationally challenging task. We developed the snpGeneSets package to simplify annotation and analysis of GWS results. Our package integrates local copies of knowledge bases for SNPs, genes, and gene sets, and implements wrapper functions in the R language to enable transparent access to low-level databases for efficient annotation of large genomic data. The package contains functions that execute three types of annotations: (1) genomic mapping annotation for SNPs and genes and functional annotation for gene sets; (2) bidirectional mapping between SNPs and genes, and genes and gene sets; and (3) calculation of gene effect measures from SNP associations and performance of gene set enrichment analyses to identify functional pathways. We applied snpGeneSets to type 2 diabetes (T2D) results from the NHGRI genome-wide association study (GWAS) catalog, a Finnish GWAS, and a genome-wide expression study (GWES). These studies demonstrate the usefulness of snpGeneSets for annotating and performing enrichment analysis of GWS results. The package is open-source, free, and can be downloaded at: https://www.umc.edu/biostats_software/.
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Krivoruchko, Alexander, Olesya Yatsyk, Anastasiya Kanibolockaya, and Valery Kulintsev. "Genome-wide association study (GWAS) of high productivity classes in the Karachaevsky sheep breed." Journal of Central European Agriculture 22, no. 4 (2021): 669–77. http://dx.doi.org/10.5513/jcea01/22.4.3238.
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Liu, Nana, Jiayuan Xu, Huaigui Liu, Shijie Zhang, Miaoxin Li, Yao Zhou, Wen Qin, Mulin Jun Li, and Chunshui Yu. "Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer’s disease." PLOS Genetics 17, no. 2 (February 25, 2021): e1009363. http://dx.doi.org/10.1371/journal.pgen.1009363.
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Genome-wide association studies (GWASs) have identified multiple susceptibility loci for Alzheimer’s disease (AD), which is characterized by early and progressive damage to the hippocampus. However, the association of hippocampal gene expression with AD and the underlying neurobiological pathways remain largely unknown. Based on the genomic and transcriptomic data of 111 hippocampal samples and the summary data of two large-scale meta-analyses of GWASs, a transcriptome-wide association study (TWAS) was performed to identify genes with significant associations between hippocampal expression and AD. We identified 54 significantly associated genes using an AD-GWAS meta-analysis of 455,258 individuals; 36 of the genes were confirmed in another AD-GWAS meta-analysis of 63,926 individuals. Fine-mapping models further prioritized 24 AD-related genes whose effects on AD were mediated by hippocampal expression, including APOE and two novel genes (PTPN9 and PCDHA4). These genes are functionally related to amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. By integrating the predicted hippocampal expression and neuroimaging data, we found that the hippocampal expression of QPCTL and ERCC2 showed significant difference between AD patients and cognitively normal elderly individuals as well as correlated with hippocampal volume. Mediation analysis further demonstrated that hippocampal volume mediated the effect of hippocampal gene expression (QPCTL and ERCC2) on AD. This study identifies two novel genes associated with AD by integrating hippocampal gene expression and genome-wide association data and reveals candidate hippocampus-mediated neurobiological pathways from gene expression to AD.
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Nakagawa, Hidewaki. "Prostate cancer genomics by high-throughput technologies: genome-wide association study and sequencing analysis." Endocrine-Related Cancer 20, no. 4 (April 26, 2013): R171—R181. http://dx.doi.org/10.1530/erc-13-0113.
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Prostate cancer (PC) is the most common malignancy in males. It is evident that genetic factors at both germline and somatic levels play critical roles in prostate carcinogenesis. Recently, genome-wide association studies (GWAS) by high-throughput genotyping technology have identified more than 70 germline variants of various genes or chromosome loci that are significantly associated with PC susceptibility. They include multiple 8q24 loci, prostate-specific genes, and metabolism-related genes. Somatic alterations in PC genomes have been explored by high-throughput sequencing technologies such as whole-genome sequencing and RNA sequencing, which have identified a variety of androgen-responsive events and fusion transcripts represented by E26 transformation-specific (ETS) gene fusions. Recent innovations in high-throughput genomic technologies have enabled us to analyze PC genomics more comprehensively, more precisely, and on a larger scale in multiple ethnic groups to increase our understanding of PC genomics and biology in germline and somatic studies, which can ultimately lead to personalized medicine for PC diagnosis, prevention, and therapy. However, these data indicate that the PC genome is more complex and heterogeneous than we expected from GWAS and sequencing analyses.
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Yin, Ruimin, Binbin Song, Jingjing Wang, Chaodan Shao, Yufen Xu, and HongGang Jiang. "Genome-Wide Association and Transcriptome-Wide Association Studies Identify Novel Susceptibility Genes Contributing to Colorectal Cancer." Journal of Immunology Research 2022 (July 1, 2022): 1–14. http://dx.doi.org/10.1155/2022/5794055.
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Background. Colorectal cancer (CRC) is among the most common cancers diagnosed worldwide. Although genome-wide association studies have effectively identified the genetic basis of CRC, there is still unexplained variability in genetic risk. Transcriptome-wide association studies (TWAS) integrate summary statistics from CRC genome-wide association studies (GWAS) with gene expression data to prioritize these GWAS findings and uncover additional gene-trait correlations. Methods. First, we carried out a post-GWAS analysis using summary statistics from a large-scale GWAS of CRC ( n = 4,562 cases, n = 382,756 controls). Second, combined with the expression weight sets from GTEx (v7), susceptibility genes were identified with the FUSION software. Colocalization, conditional and fine-mapping analyses, phenome-wide association study (pheWAS), and Mendelian randomization were employed to further characterize the observed correlations. Results. In the post-GWAS analyses, we first identified new genome-wide significant associations: three genomic risk loci were identified at 8q24.21 (rs6983267, P = 6.98 × 10 − 12 ), 15q13.3 (rs58658771, P = 1.40 × 10 − 10 ), and 18q21.1 (rs6507874, P = 1.91 × 10 − 14 ). In addition, the TWAS also identified four loci statistically significantly associated with CRC risk, largely explained by expression regulation, including six candidate genes (DUSP10, POU5F1B, C11orf53, COLCA1, COLCA2, and GREM1-AS1). We further discovered evidence that low expression of COLCA2 is correlated with CRC risk with Mendelian randomization. Conclusions. We discovered novel CRC risk loci and candidate functional genes by merging gene expression and GWAS summary data, offering new insight into the molecular processes underlying CRC development. This makes it easier to prioritize potential genes for follow-up functional research in CRC.
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Dwiningsih, S.Si., M.Si., Ph.D, Yheni. "Genome-Wide Association Study of Complex Traits in Maize Detects Genomic Regions and Genes for Increasing Grain Yield and Grain Quality." Advance Sustainable Science Engineering and Technology 4, no. 2 (November 6, 2022): 0220209. http://dx.doi.org/10.26877/asset.v4i2.12678.
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This review describes the current status of genome-wide association study (GWAS) of the major crops in maize (Zea mays L.) concentrate on performing association mapping as a novel method in associating genetic and complex traits, current strategy in analyzing of phenotype and genotype data to identify population structure and linkage disequilibrium. GWAS has an important role in food security because this method identified many crucial genomic regions of important traits in the most commercialize crops of the world, such as maize. These complex traits including yield, grain quality, biofortification, biotic and abiotic resistance. GWAS has many advantages correlated with reducing genotyping cost and research time, increasing mapping resolution and larger allele number. Meanwhile, GWAS has two main limitations related to population size and the number of markers. There are many software packages for data analysis in GWAS. The most commonly software that was used in GWAS especially in this crop is TASSEL because frequently updated. Recently, many research papers concentrated on GWAS in maize. GWAS analysis accelerated identification of genetic regions, candidate genes within these genomic regions and their metabolomic analysis correlated to the complex traits in maize for increasing grain yield and grain quality to fulfill the market demands.
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Kirichenko, A. V., A. S. Zlobin, T. I. Shashkova, N. A. Volkova, B. S. Iolchiev, V. A. Bagirov, P. M. Borodin, L. С. Karssen, Y. A. Tsepilov, and Y. S. Aulchenko. "The GWAS-MAP|ovis platform for aggregation and analysis of genome-wide association study results in sheep." Vavilov Journal of Genetics and Breeding 26, no. 4 (July 7, 2022): 378–84. http://dx.doi.org/10.18699/vjgb-22-46.
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In recent years, the number of genome-wide association studies (GWAS) carried out for various economically important animal traits has been increasing. GWAS discoveries provide summary statistics that can be used both for targeted marker-oriented selection and for studying the genetic control of economically important traits of farm animals. In contrast to research in human genetics, GWAS on farm animals often does not meet generally accepted standards (availability of information about effect and reference alleles, the size and direction of the effect, etc.). This greatly complicates the use of GWAS results for breeding needs. Within the framework of human genetics, there are several technological solutions for researching the harmonized results of GWAS, including one of the largest, the GWAS-MAP platform. For other types of living organisms, including economically important agricultural animals, there are no similar solutions. To our knowledge, no similar solution has been proposed to date for any of the species of economically important animals. As part of this work, we focused on creating a platform similar to GWAS-MAP for working with the results of GWAS of sheep, since sheep breeding is one of the most important branches of agriculture. By analogy with the GWAS-MAP platform for storing, unifying and analyzing human GWAS, we have created the GWAS-MAP|ovis platform. The platform currently contains information on more than 34 million associations between genomic sequence variants and traits of meat production in sheep. The platform can also be used to conduct colocalization analysis, a method that allows one to determine whether the association of a particular locus with two different traits is the result of pleiotropy or whether these traits are associated with different variants that are in linkage disequilibrium. This platform will be useful for breeders to select promising markers for breeding, as well as to obtain information for the introduction of genomic breeding and for scientists to replicate the results obtained.
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Liu, Changning, and Zhenyu Xuan. "Prioritization of Cancer-Related Genomic Variants by SNP Association Network." Cancer Informatics 14s2 (January 2015): CIN.S17288. http://dx.doi.org/10.4137/cin.s17288.
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We have developed a general framework to construct an association network of single nucleotide polymorphisms (SNPs) (SNP association network, SAN) based on the functional interactions of genes located in the flanking regions of SNPs. SAN, which was constructed based on protein-protein interactions in the Human Protein Reference Database (HPRD), showed significantly enriched signals in both linkage disequilibrium (LD) and long-range chromatin interaction (Hi-C). We used this network to further develop two methods for predicting and prioritizing disease-associated genes from genome-wide association studies (GWASs). We found that random walk with restart (RWR) using SAN (RWR-SAN) can greatly improve the prediction of lung-cancer-associated genes by comparing RWR with the use of network in HPRD (AUC 0.81 vs 0.66). In a reanalysis of the GWAS dataset of age-related macular degeneration (AMD), SAN could identify more potential AMD-associated genes that were previously ranked lower in the GWAS study. The interactions in SAN could facilitate the study of complex diseases.
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Cha, Jihye, Hyojun Choo, Krishnamoorthy Srikanth, Seung-Hwan Lee, Ju-Whan Son, Mi-Rim Park, Nayeon Kim, Gul Won Jang, and Jong-Eun Park. "Genome-Wide Association Study Identifies 12 Loci Associated with Body Weight at Age 8 Weeks in Korean Native Chickens." Genes 12, no. 8 (July 29, 2021): 1170. http://dx.doi.org/10.3390/genes12081170.
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Meat from Korean native chickens (KNCs) has high consumer demand; however, slow growth performance and high variation in body weight (BW) of KNCs remain an issue. Genome-wide association study (GWAS) is a powerful method to identify quantitative trait-associated genomic loci. A GWAS, based on a large-scale KNC population, is needed to identify underlying genetic mechanisms related to its growth traits. To identify BW-associated genomic regions, we performed a GWAS using the chicken 60K single nucleotide polymorphism (SNP) panel for 1328 KNCs. BW was measured at 8 weeks of age, from 2018 to 2020. Twelve SNPs were associated with BW at the suggestive significance level (p < 2.95 × 10−5) and located near or within 11 candidate genes, including WDR37, KCNIP4, SLIT2, PPARGC1A, MYOCD and ADGRA3. Gene set enrichment analysis based on the GWAS results at p < 0.05 (1680 SNPs) showed that 32 Gene Ontology terms and two Kyoto Encyclopedia of Genes and Genomes pathways, including regulation of transcription, motor activity, the mitogen-activated protein kinase signaling pathway, and tight junction, were significantly enriched (p < 0.05) for BW-associated genes. These pathways are involved in cell growth and development, related to BW gain. The identified SNPs are potential biomarkers in KNC breeding.
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Santos, Cristiano Silva dos, Massaine Bandeira Sousa, Ana Carla Brito, Luciana Alves de Oliveira, Carlos Wanderlei Piler Carvalho, and Eder Jorge de Oliveira. "Genome-wide association study of cassava starch paste properties." PLOS ONE 17, no. 1 (January 21, 2022): e0262888. http://dx.doi.org/10.1371/journal.pone.0262888.
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An understanding of cassava starch paste properties (CSPP) can contribute to the selection of clones with differentiated starches. This study aimed to identify genomic regions associated with CSPP using different genome-wide association study (GWAS) methods (MLM, MLMM, and Farm-CPU). The GWAS was performed using 23,078 single-nucleotide polymorphisms (SNPs). The rapid viscoanalyzer (RVA) parameters were pasting temperature (PastTemp), peak viscosity (PeakVisc), hot-paste viscosity (Hot-PVisc), cool-paste viscosity (Cold-PVisc), final viscosity (FinalVis), breakdown (BreDow), and setback (Setback). Broad phenotypic and molecular diversity was identified based on the genomic kinship matrix. The broad-sense heritability estimates (h2) ranged from moderate to high magnitudes (0.66 to 0.76). The linkage disequilibrium (LD) declined to between 0.3 and 2.0 Mb (r2 <0.1) for most chromosomes, except chromosome 17, which exhibited an extensive LD. Thirteen SNPs were found to be significantly associated with CSPP, on chromosomes 3, 8, 17, and 18. Only the BreDow trait had no associated SNPs. The regional marker-trait associations on chromosome 18 indicate a LD block between 2907312 and 3567816 bp and that SNP S18_3081635 was associated with SetBack, FinalVis, and Cold-PVisc (all three GWAS methods) and with Hot-PVisc (MLM), indicating that this SNP can track these four traits simultaneously. The variance explained by the SNPs ranged from 0.13 to 0.18 for SetBack, FinalVis, and Cold-PVisc and from 0.06 to 0.09 for PeakVisc and Hot-PVisc. The results indicated additive effects of the genetic control of Cold-PVisc, FinalVis, Hot-PVisc, and SetBack, especially on the large LD block on chromosome 18. One transcript encoding the glycosyl hydrolase family 35 enzymes on chromosome 17 and one encoding the mannose-p-dolichol utilization defect 1 protein on chromosome 18 were the most likely candidate genes for the regulation of CSPP. These results underline the potential for the assisted selection of high-value starches to improve cassava root quality through breeding programs.
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Sieber, Karsten B., Anna Batorsky, Kyle Siebenthall, Kelly L. Hudkins, Jeff D. Vierstra, Shawn Sullivan, Aakash Sur, et al. "Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci." Journal of the American Society of Nephrology 30, no. 3 (February 13, 2019): 421–41. http://dx.doi.org/10.1681/asn.2018030309.
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BackgroundLinking genetic risk loci identified by genome-wide association studies (GWAS) to their causal genes remains a major challenge. Disease-associated genetic variants are concentrated in regions containing regulatory DNA elements, such as promoters and enhancers. Although researchers have previously published DNA maps of these regulatory regions for kidney tubule cells and glomerular endothelial cells, maps for podocytes and mesangial cells have not been available.MethodsWe generated regulatory DNA maps (DNase-seq) and paired gene expression profiles (RNA-seq) from primary outgrowth cultures of human glomeruli that were composed mainly of podocytes and mesangial cells. We generated similar datasets from renal cortex cultures, to compare with those of the glomerular cultures. Because regulatory DNA elements can act on target genes across large genomic distances, we also generated a chromatin conformation map from freshly isolated human glomeruli.ResultsWe identified thousands of unique regulatory DNA elements, many located close to transcription factor genes, which the glomerular and cortex samples expressed at different levels. We found that genetic variants associated with kidney diseases (GWAS) and kidney expression quantitative trait loci were enriched in regulatory DNA regions. By combining GWAS, epigenomic, and chromatin conformation data, we functionally annotated 46 kidney disease genes.ConclusionsWe demonstrate a powerful approach to functionally connect kidney disease-/trait–associated loci to their target genes by leveraging unique regulatory DNA maps and integrated epigenomic and genetic analysis. This process can be applied to other kidney cell types and will enhance our understanding of genome regulation and its effects on gene expression in kidney disease.
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You, Frank, Jin Xiao, Pingchuan Li, Zhen Yao, Gaofeng Jia, Liqiang He, Santosh Kumar, et al. "Genome-Wide Association Study and Selection Signatures Detect Genomic Regions Associated with Seed Yield and Oil Quality in Flax." International Journal of Molecular Sciences 19, no. 8 (August 6, 2018): 2303. http://dx.doi.org/10.3390/ijms19082303.
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A genome-wide association study (GWAS) was performed on a set of 260 lines which belong to three different bi-parental flax mapping populations. These lines were sequenced to an averaged genome coverage of 19× using the Illumina Hi-Seq platform. Phenotypic data for 11 seed yield and oil quality traits were collected in eight year/location environments. A total of 17,288 single nucleotide polymorphisms were identified, which explained more than 80% of the phenotypic variation for days to maturity (DTM), iodine value (IOD), palmitic (PAL), stearic, linoleic (LIO) and linolenic (LIN) acid contents. Twenty-three unique genomic regions associated with 33 quantitative trait loci (QTL) for the studied traits were detected, thereby validating four genomic regions previously identified. The 33 QTL explained 48–73% of the phenotypic variation for oil content, IOD, PAL, LIO and LIN but only 8–14% for plant height, DTM and seed yield. A genome-wide selective sweep scan for selection signatures detected 114 genomic regions that accounted for 7.82% of the flax pseudomolecule and overlapped with the 11 GWAS-detected genomic regions associated with 18 QTL for 11 traits. The results demonstrate the utility of GWAS combined with selection signatures for dissection of the genetic structure of traits and for pinpointing genomic regions for breeding improvement.
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González-Castro, Thelma Beatriz, Alma Delia Genis-Mendoza, Carlos Alfonso Tovilla-Zárate, José Jaime Martínez-Magaña, Isela Esther Juárez-Rojop, Emmanuel Sarmiento, and Humberto Nicolini. "Genome-wide association study of suicide attempt in a Mexican population: a study protocol." BMJ Open 9, no. 4 (April 2019): e025335. http://dx.doi.org/10.1136/bmjopen-2018-025335.
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IntroductionSuicidality is a complex behaviour and a major health problem; the specific features that could predispose to suicidal behaviour have been extensively investigated, most frequently in European and Asian populations. Therefore, our aim is to present a protocol that will explore suicide attempt in Mexican individuals diagnosed with psychiatric disorders, through a genome-wide association study (GWAS).Method and analysisWe will perform a GWAS by comparing 700 individuals who have suicide attempt history, with control subjects without suicide attempt history (n=500). The genotyping will be conducted using the Infinium PsychArray BeadChip and quality controls will be applied to single nucleotides (SNPs) genotyped. After that, we will perform the imputation using reference panels provided by the Haplotype Reference Consortium. We will perform two different workflows: (A) the classic GWAS analysis applying the same weight to all the variants and (B) an algorithm with prediction of deleteriousness of variants.Ethics and disseminationThis study was approved by the ethics and investigation committees of the National Institute of Genomic Medicine on 22 July 2015, No CEI 215/13. We plan to disseminate research findings in scientific conferences and as a manuscript in peer-reviewed journals.Trial registration numberCEI 215/13.
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Kuksa, Pavel P., Chien-Yueh Lee, Alexandre Amlie-Wolf, Prabhakaran Gangadharan, Elizabeth E. Mlynarski, Yi-Fan Chou, Han-Jen Lin, et al. "SparkINFERNO: a scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants." Bioinformatics 36, no. 12 (April 24, 2020): 3879–81. http://dx.doi.org/10.1093/bioinformatics/btaa246.
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Abstract Summary We report Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants (SparkINFERNO), a scalable bioinformatics pipeline characterizing non-coding genome-wide association study (GWAS) association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWASs and show that SparkINFERNO is more than 60 times efficient and scales with data size and amount of computational resources. Availability and implementation SparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno. Contact lswang@pennmedicine.upenn.edu Supplementary information Supplementary data are available at Bioinformatics online
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McLaren, Christine E., Chad P. Garner, Clare C. Constantine, Stela Masle, Christopher D. Vulpe, Beverly M. Snively, James D. Cook, et al. "Genome-Wide Association Study Identifies Genetic Loci Associated with Iron Deficiency." Blood 114, no. 22 (November 20, 2009): 4048. http://dx.doi.org/10.1182/blood.v114.22.4048.4048.
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Abstract Abstract 4048 Poster Board III-983 Introduction Iron deficiency is the most common nutritional disorder in the world with an estimated two billion affected persons. Although commonly considered environmental in origin, the existence of multiple genetic disorders of iron metabolism in man, rodents and other vertebrates suggest a genetic contribution to iron deficiency. Methods: The Hemochromatosis and Iron Overload Screening (HEIRS) Study is a multi-center, multi-ethnic study in which transferrin saturation (TS), serum ferritin (SF), and HFE mutations were determined in 101,168 adults. To identify genomic locations associated with iron deficiency, we performed a genome-wide association study (GWAS) using DNA collected from white HEIRS Study participants who had SF ≤ 12 μg/L (cases) and an equal number of white controls (SF > 100 μg/L in men, SF > 50 μg/L in women) frequency-matched to cases by sex and geographic location. Men aged ≥ 25 y and women ≥ 50 y were included in both groups. Tissue body iron, an index of iron deficiency, was estimated from serum transferrin receptor (sTfR) and SF. Genotyping was performed with the Illumina HumanCNV370K Beadchip platform. Quality control filters excluded single nucleotide polymorphisms (SNPs) or samples with > 5% missing genotypes, SNPs showing heterozygosity or Hardy-Weinberg deviations (P<10−7), and SNPs with minor allele frequency < 0.02. Population admixture/structure was assessed using principal component analysis. Regression analysis was used to examine the association between outcomes (case-control status, tissue body iron, serum ferritin, transferrin receptor, serum iron, total iron-binding capacity [TIBC], and unsaturated iron-binding capacity [UIBC]) and each SNP genotype variable; covariates included age, sex, and geographic location. Replication for 56 SNPs was conducted in a population attending primary care clinics at a Veterans Affairs (VA) medical center using the iPlex platform. Eligibility within the VA replication population was restricted to age and self-reported white ethnicity as for the HEIRS subset from a total of 2559 people (138 women). VA participants with SF ≤ 20 μg/L were classified as iron-deficient cases and frequency matched 1:2 with controls (men with SF > 100 μg/L and women with SF > 50 μg/L). Results The GWAS genomic control parameter was not significantly different from 1.0. There were 392 cases (96 men) and 390 controls (96 men) in the HEIRS subset GWAS with average age (SD) of 59 (10) y and 61 (11) y, respectively. Geometric mean SF (minimum, maximum), and mean (SD) for sTfR and tissue body iron in the HEIRS subset were 7.5 (1.2, 12) μg/L, 6.4 (3.77) mg/kg and -2.0 (2.50) for cases and 141 (51, 881) μg/L, 3.0 (0.98) mg/kg and 10.8 (2.5) for controls. After quality control tests, GWAS analysis included genotype data for 331,060 SNPs in 734 individuals (364 cases, 370 controls). For the VA replication population there were 67 male and 11 female cases, and 136 male and 27 female controls for whom DNA was successfully prepared; the average age (SD) was 68 (12) y for cases and 65 (11) y for controls. Regression analysis identified seven SNPs within four independent regions that replicated associations found in the GWAS (GWAS P<1×10-4 and VA P<0.05).The SNP rs6735681 on chromosome 2p24 was associated with serum iron (GWAS P<3.9×10-5, VA P=0.038). Three SNPs on chromosome 2p14 (rs6750096, rs2698541 and rs2698530) significantly influenced both TIBC and UIBC (GWAS P<2.9×10-5, VA P< 0.04 for all). Two SNPs in the TF gene region on chromosome 3q22 also showed significant effects on TIBC and UIBC (GWAS P<4.7×10-6, VA P<0.03 for all). The SNP rs10512064 on chromosome 9q21 was associated with serum ferritin concentration and tissue body iron (GWAS P<2.5×10-5, VA P<0.05 for both). Conclusion: From these GWAS and replication studies, we have identified three new genetic loci and one known iron gene, TF, associated with iron phenotype variability. These results point to specific loci as targets for gene identification and TF polymorphisms as determinants of iron metabolism, which in turn may play a role in regulation of body iron status. Disclosures: No relevant conflicts of interest to declare.
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Ghobril, Jean-Pierre, Dusan Petrovic, Georg Ehret, Belén Ponte, Menno Pruijm, Daniel Ackermann, Bruno Vogt, et al. "PhenoExplorer: An Interactive Web-based Platform for Exploring (Epi)Genome-Wide Associations Using a Swiss Population-based Study." CHIMIA 76, no. 12 (December 21, 2022): 1052. http://dx.doi.org/10.2533/chimia.2022.1052.
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The recent advent of high-throughput sequencing technologies has allowed exploring the contribution of thousands of genomic, epigenomic, transcriptomic, or proteomic variants to complex phenotypic traits. Here, we sought to conduct large-scale (Epi)Genome-Wide Association Studies (GWAS/EWAS) to investigate the associations between genomic (Single Nucleotide Polymorphism; SNP) and epigenomic (Cytosine-Phospho-Guanine; CpG) markers, with multiple phenotypic traits in a population-based context. We used data from SKIPOGH, a family- and population-based cohort conducted in the cities of Lausanne, Geneva, and Bern (N=1100). We used 7,577,572 SNPs, 420,444 CpGs, and 825 phenotypes, including anthropometric, clinical, blood, urine, metabolite, and metal measures. GWAS analyses assessed the associations between SNPs and metabolites and metals (N=279), using regression models adjusted for age, sex, recruitment center, and familial structure, whereas EWAS analyses explored the relations between CpGs and 825 phenotypes, additionally adjusting for the seasonality of blood sampling and technical nuisance. Following the implementation of GWAS and EWAS analyses, we developed a web-based platform, PhenoExplorer, aimed at providing an open access to the obtained results. Of the 279 phenotypes included in GWAS, 103 displayed significant associations with 2804 SNPs (2091 unique SNPs) at Bonferroni threshold, whereas 109 of the 825 phenotypes included in EWAS analyses were associated with 4893 CpGs (2578 unique CpGs). All of the obtained GWAS and EWAS results were eventually made available using the in-house built web-based PhenoExplorer platform, with the purpose of providing an open-access to the tested associations. In conclusion, we provide a comprehensive outline of GWAS and EWAS associations performed in a Swiss population-based study. Further, we set up a web-based PhenoExplorer platform with the purpose of contributing to the overall understanding of the role of molecular variants in regulating complex phenotypes.
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Zheng, Xiao-Ming, Tingting Gong, Hong-Ling Ou, Dayuan Xue, Weihua Qiao, Junrui Wang, Sha Liu, Qingwen Yang, and Kenneth M. Olsen. "Genome-wide association study of rice grain width variation." Genome 61, no. 4 (April 2018): 233–40. http://dx.doi.org/10.1139/gen-2017-0106.
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Seed size is variable within many plant species, and understanding the underlying genetic factors can provide insights into mechanisms of local environmental adaptation. Here we make use of the abundant genomic and germplasm resources available for rice (Oryza sativa) to perform a large-scale genome-wide association study (GWAS) of grain width. Grain width varies widely within the crop and is also known to show climate-associated variation across populations of its wild progenitor. Using a filtered dataset of >1.9 million genome-wide SNPs in a sample of 570 cultivated and wild rice accessions, we performed GWAS with two complementary models, GLM and MLM. The models yielded 10 and 33 significant associations, respectively, and jointly yielded seven candidate locus regions, two of which have been previously identified. Analyses of nucleotide diversity and haplotype distributions at these loci revealed signatures of selection and patterns consistent with adaptive introgression of grain width alleles across rice variety groups. The results provide a 50% increase in the total number of rice grain width loci mapped to date and support a polygenic model whereby grain width is shaped by gene-by-environment interactions. These loci can potentially serve as candidates for studies of adaptive seed size variation in wild grass species.
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Kooshyar, Mohammad Mehdi, Mohammadreza Nassiri, Aliasghar Aslaminezad, and Morteza Betaraf. "Feasibility study of SNPs detection associated with breast cancer by genome-wide association virtual studies." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e22167-e22167. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e22167.
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e22167 Background: Available genomic data and genome-wide association virtual studies (GWAS), provide possibility of genetic markers detection known to be associated with the complex diseases. Genome-wide association studies, involving direct testing of genetic polymorphisms in large series of disease cases versus controls, provide a powerful approach to identify lower penetrance alleles that cannot be detected by genetic linkage studies. Methods: By utilizing genotyping platforms that can type hundreds of thousands of SNPs simultaneously, it is possible to conduct association studies using sets of SNPs that tag most known common variants in the genome, and hence scan associations without prior knowledge of function or position. GWAS have been conducted in five of the most common cancer types: breast, prostate, colorectal, lung and melanoma. GWAS of breast cancer was performed by simulation of 7 known SNPs on chromosomes 2, 5, 6, 8, 10, 11 and 16 for 10,000 women. Results: The strongest associations were found for rs2981582 in the FGFR2 gene. SNPs: rs889312 ° rs2180341 and rs3817198 were associated with breast cancer in benferroni significant level. However, SNPs: rs 13387042 ° rs2180341 and rs13281615 on chromosomes 2, 6 and 8 were not associated with breast. Conclusions: Results of this research show that the detection of SNPs associated with disease is easily possible through employing virtual systems based on real data of Hap Map project by using R software.
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Azam, Afifah Binti, and Elena Aisha Binti Azizan. "Brief Overview of a Decade of Genome-Wide Association Studies on Primary Hypertension." International Journal of Endocrinology 2018 (January 30, 2018): 1–14. http://dx.doi.org/10.1155/2018/7259704.
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Primary hypertension is widely believed to be a complex polygenic disorder with the manifestation influenced by the interactions of genomic and environmental factors making identification of susceptibility genes a major challenge. With major advancement in high-throughput genotyping technology, genome-wide association study (GWAS) has become a powerful tool for researchers studying genetically complex diseases. GWASs work through revealing links between DNA sequence variation and a disease or trait with biomedical importance. The human genome is a very long DNA sequence which consists of billions of nucleotides arranged in a unique way. A single base-pair change in the DNA sequence is known as a single nucleotide polymorphism (SNP). With the help of modern genotyping techniques such as chip-based genotyping arrays, thousands of SNPs can be genotyped easily. Large-scale GWASs, in which more than half a million of common SNPs are genotyped and analyzed for disease association in hundreds of thousands of cases and controls, have been broadly successful in identifying SNPs associated with heart diseases, diabetes, autoimmune diseases, and psychiatric disorders. It is however still debatable whether GWAS is the best approach for hypertension. The following is a brief overview on the outcomes of a decade of GWASs on primary hypertension.
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Lozano-Ramirez, Nerida, Susanne Dreisigacker, Carolina P. Sansaloni, Xinyao He, José Sergio Sandoval-Islas, Paulino Pérez-Rodríguez, Aquiles Carballo Carballo, Cristian Nava Diaz, Masahiro Kishii, and Pawan K. Singh. "Genome-Wide Association Study for Spot Blotch Resistance in Synthetic Hexaploid Wheat." Genes 13, no. 8 (August 4, 2022): 1387. http://dx.doi.org/10.3390/genes13081387.
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Spot blotch (SB) caused by Bipolaris sorokiniana (Sacc.) Shoem is a destructive fungal disease affecting wheat and many other crops. Synthetic hexaploid wheat (SHW) offers opportunities to explore new resistance genes for SB for introgression into elite bread wheat. The objectives of our study were to evaluate a collection of 441 SHWs for resistance to SB and to identify potential new genomic regions associated with the disease. The panel exhibited high SB resistance, with 250 accessions showing resistance and 161 showing moderate resistance reactions. A genome-wide association study (GWAS) revealed a total of 41 significant marker–trait associations for resistance to SB, being located on chromosomes 1B, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4D, 5A, 5D, 6D, 7A, and 7D; yet none of them exhibited a major phenotypic effect. In addition, a partial least squares regression was conducted to validate the marker–trait associations, and 15 markers were found to be most important for SB resistance in the panel. To our knowledge, this is the first GWAS to investigate SB resistance in SHW that identified markers and resistant SHW lines to be utilized in wheat breeding.
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A, KARTHIKEYAN, AMIT KUMAR, RAJNI CHAUDHARY, AAMIR BASHIR WARA, AKANSHA SINGH, N. R. SAHOO, MOHD BAQIR, and B. P. MISHRA. "Genome-wide association study of birth weight and pre-weaning body weight of crossbred pigs." Indian Journal of Animal Sciences 90, no. 2 (March 6, 2020): 195–200. http://dx.doi.org/10.56093/ijans.v90i2.98781.
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In piggery, birth weight and body weight remains most vital economic trait as they directly influence on the production performance of the farm. Implementing the genomic selection would pay way for rapid genetic gain along with increased accuracy than conventional breeding. Prior to genomic selection, genome wide association study (GWAS) has to be conducted in order to find informative SNPs associated with the traits of interest in a given population. Under this study 96 crossbred pigs were genotyped using double digest genotype by sequencing (GBS) technique using Hiseq platform. Raw FASTQ data were processed using dDOCENT Pipeline on Reference based method and variants were called using Free Bayes (version 1.1.0-3). Using Plink (v1.09b), variants having MAF>0.01, HWE<0.001 and genotyping rate >80% were filtered out and 20,467 SNPs were retained after quality control, for ascertaining GWAS in 96 pigs. Before conducting association studies, the data were adjusted for significant nongenetic factors affecting the traits of interest. GWAS was performed using Plink software (v1.9b) identified 9, 11, 12, 23, 28, 24, 30, 33 and 42 SNPs significantly (adjusted P<0.001) associated with birth weight, body weight at weekly interval from 1st week to 8th week, respectively. A large proportion of significant (adjusted P<0.001) SNPs were located on SSC10, SSC6, SSC13, SSC8 and SSC1. One genome wide significant SNP and four genome wide suggestive SNPs were identified. Two common SNPs affecting all body weight at different weeks were located on SSC5:40197442 and SSC13:140562 base pair position. This study helps to identify the genome wide scattered significant SNPs associated with traits of interest which could be used for genomic selection, but further validation studies of these loci in larger population are recommended.
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Alseekh, Saleh, Dimitrina Kostova, Mustafa Bulut, and Alisdair R. Fernie. "Genome-wide association studies: assessing trait characteristics in model and crop plants." Cellular and Molecular Life Sciences 78, no. 15 (July 1, 2021): 5743–54. http://dx.doi.org/10.1007/s00018-021-03868-w.
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AbstractGWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.
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Bhatta, Madhav, Alexey Morgounov, Vikas Belamkar, Stephen N. Wegulo, Abdelfattah A. Dababat, Gül Erginbas-Orakci, Mustapha El Bouhssini, et al. "Genome-Wide Association Study for Multiple Biotic Stress Resistance in Synthetic Hexaploid Wheat." International Journal of Molecular Sciences 20, no. 15 (July 26, 2019): 3667. http://dx.doi.org/10.3390/ijms20153667.
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Genetic resistance against biotic stress is a major goal in many wheat breeding programs. However, modern wheat cultivars have a limited genetic variation for disease and pest resistance and there is always a possibility of the evolution of new diseases and pests to overcome previously identified resistance genes. A total of 125 synthetic hexaploid wheats (SHWs; 2n = 6x = 42, AABBDD, Triticum aestivum L.) were characterized for resistance to fungal pathogens that cause wheat rusts (leaf; Puccinia triticina, stem; P. graminis f.sp. tritici, and stripe; P. striiformis f.sp. tritici) and crown rot (Fusarium spp.); cereal cyst nematode (Heterodera spp.); and Hessian fly (Mayetiola destructor). A wide range of genetic variation was observed among SHWs for multiple (two to five) biotic stresses and 17 SHWs that were resistant to more than two stresses. The genomic regions and potential candidate genes conferring resistance to these biotic stresses were identified from a genome-wide association study (GWAS). This GWAS study identified 124 significant marker-trait associations (MTAs) for multiple biotic stresses and 33 of these were found within genes. Furthermore, 16 of the 33 MTAs present within genes had annotations suggesting their potential role in disease resistance. These results will be valuable for pyramiding novel genes/genomic regions conferring resistance to multiple biotic stresses from SHWs into elite bread wheat cultivars and providing further insights on a wide range of stress resistance in wheat.
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Xu, Huang, Xiang Li, Yaning Yang, Yi Li, Jose Pinheiro, Kate Sasser, Hisham Hamadeh, Xu Steven, and Min Yuan. "High-throughput and efficient multilocus genome-wide association study on longitudinal outcomes." Bioinformatics 36, no. 10 (February 25, 2020): 3004–10. http://dx.doi.org/10.1093/bioinformatics/btaa120.
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Abstract Motivation With the emerging of high-dimensional genomic data, genetic analysis such as genome-wide association studies (GWAS) have played an important role in identifying disease-related genetic variants and novel treatments. Complex longitudinal phenotypes are commonly collected in medical studies. However, since limited analytical approaches are available for longitudinal traits, these data are often underutilized. In this article, we develop a high-throughput machine learning approach for multilocus GWAS using longitudinal traits by coupling Empirical Bayesian Estimates from mixed-effects modeling with a novel ℓ0-norm algorithm. Results Extensive simulations demonstrated that the proposed approach not only provided accurate selection of single nucleotide polymorphisms (SNPs) with comparable or higher power but also robust control of false positives. More importantly, this novel approach is highly scalable and could be approximately >1000 times faster than recently published approaches, making genome-wide multilocus analysis of longitudinal traits possible. In addition, our proposed approach can simultaneously analyze millions of SNPs if the computer memory allows, thereby potentially allowing a true multilocus analysis for high-dimensional genomic data. With application to the data from Alzheimer's Disease Neuroimaging Initiative, we confirmed that our approach can identify well-known SNPs associated with AD and were much faster than recently published approaches (≥6000 times). Availability and implementation The source code and the testing datasets are available at https://github.com/Myuan2019/EBE_APML0. Supplementary information Supplementary data are available at Bioinformatics online.
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Bai, Xuehua, Xin Wang, Yanzhou Wang, Yiping Wei, Yafen Fu, Jing Rao, Yonghong Ma, et al. "Genome-Wide Association Study of Six Forage Traits in Ramie (Boehmeria nivea L. Gaud)." Plants 11, no. 11 (May 28, 2022): 1443. http://dx.doi.org/10.3390/plants11111443.
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Genome-wide association study (GWAS) of six forage traits using whole-genome sequencing data generated from 301 ramie accessions found that traits were continuously distributed; the maximum variant coefficient was fresh weight per clump (FWPC) (2019) and individual plant height (IPH) (2019) minimum. Correlation analysis demonstrated that 2019 and 2020 results were similar; all traits were correlated. GWAS analysis demonstrated that six traits exhibited consistent and precise association signals. Of the latter, 104 were significant and detected in 43 genomic regions. By screening forage trait-associated single nucleotide polymorphisms and combining Manhattan map with genome annotation, signals were categorized according to functional annotations. One loci associated with fresh weight per plant (FWP) (chromosome 5; Bnt05G007759), two associated with FWPC (chromosome 13; Bnt13G018582, and Bnt13G018583), and two associated with leaf dry weight per plant (LDWP) and dry weight per plant (DWP) (chromosome 4; Bnt04G005779 and Bnt04G005780), were identified. We describe forage trait candidate genes that are highly correlated with FWP and FWPC; Bnt05G007759 may be involved in nitrogen metabolism, while Bnt13G018582 and Bnt13G018583 may encode TEOSINTE branch 1/CYCLOIDEA/proliferating cytokine 1 (TCP) domains. Bnt04G005779 and Bnt04G005780, which may regulate growth and development, are highly related to LDWP and DWP. These genomic resources will provide a basis for breeding varieties.
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Fatumo, Segun, Tinashe Chikowore, Robert Kalyesubula, Rebecca N. Nsubuga, Gershim Asiki, Oyekanmi Nashiru, Janet Seeley, et al. "Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans." Human Molecular Genetics 30, no. 16 (March 30, 2021): 1559–68. http://dx.doi.org/10.1093/hmg/ddab088.
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Abstract Genome-wide association studies (GWAS) of kidney function have uncovered hundreds of loci, primarily in populations of European ancestry. We have undertaken the first continental African GWAS of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define chronic kidney disease (CKD). We conducted GWAS of eGFR in 3288 East Africans from the Uganda General Population Cohort (GPC) and replicated in 8224 African Americans from the Women’s Health Initiative. Loci attaining genome-wide significant evidence for association (P < 5 × 10−8) were followed up with Bayesian fine-mapping to localize potential causal variants. The predictive power of a genetic risk score (GRS) constructed from previously reported trans-ancestry eGFR lead single nucleotide polymorphism (SNPs) was evaluated in the Uganda GPC. We identified and validated two eGFR loci. At the glycine amidinotransferase (GATM) locus, the association signal (lead SNP rs2433603, P = 1.0 × 10−8) in the Uganda GPC GWAS was distinct from previously reported signals at this locus. At the haemoglobin beta (HBB) locus, the association signal (lead SNP rs141845179, P = 3.0 × 10−8) has been previously reported. The lead SNP at the HBB locus accounted for 88% of the posterior probability of causality after fine-mapping, but did not colocalise with kidney expression quantitative trait loci. The trans-ancestry GRS of eGFR was not significantly predictive into the Ugandan population. In the first GWAS of eGFR in continental Africa, we validated two previously reported loci at GATM and HBB. At the GATM locus, the association signal was distinct from that previously reported. These results demonstrate the value of performing GWAS in continental Africans, providing a rich genomic resource to larger consortia for further discovery and fine-mapping. The study emphasizes that additional large-scale efforts in Africa are warranted to gain further insight into the genetic architecture of CKD.
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Ni, Jing, Peng Wang, Kang-Jia Yin, Xiao-Ke Yang, Han Cen, Cong Sui, Guo-Cui Wu, and Hai-Feng Pan. "Novel insight into the aetiology of rheumatoid arthritis gained by a cross-tissue transcriptome-wide association study." RMD Open 8, no. 2 (September 2022): e002529. http://dx.doi.org/10.1136/rmdopen-2022-002529.
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BackgroundAlthough genome-wide association studies (GWASs) have identified more than 100 loci associated with rheumatoid arthritis (RA) susceptibility, the causal genes and biological mechanisms remain largely unknown.MethodsA cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signaturestool was performed to integrate GWAS summary statistics from 58 284 individuals (14 361 RA cases and 43 923 controls) with gene-expression matrix in the Genotype-Tissue Expression project. Subsequently, a single tissue by using FUSION software was conducted to validate the significant associations. We also compared the TWAS with different gene-based methodologies, including Summary Data Based Mendelian Randomization (SMR) and Multimarker Analysis of Genomic Annotation (MAGMA). Further in silico analyses (conditional and joint analysis, differential expression analysis and gene-set enrichment analysis) were used to deepen our understanding of genetic architecture and comorbidity aetiology of RA.ResultsWe identified a total of 47 significant candidate genes for RA in both cross-tissue and single-tissue test after multiple testing correction, of which 40 TWAS-identified genes were verified by SMR or MAGMA. Among them, 13 genes were situated outside of previously reported significant loci by RA GWAS. Both TWAS-based and MAGMA-based enrichment analyses illustrated the shared genetic determinants among autoimmune thyroid disease, asthma, type I diabetes mellitus and RA.ConclusionOur study unveils 13 new candidate genes whose predicted expression is associated with risk of RA, providing new insights into the underlying genetic architecture of RA.
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Paulino, Jean Fausto de Carvalho, Caléo Panhoca de Almeida, César Júnior Bueno, Qijian Song, Roberto Fritsche-Neto, Sérgio Augusto Morais Carbonell, Alisson Fernando Chiorato, and Luciana Lasry Benchimol-Reis. "Genome-Wide Association Study Reveals Genomic Regions Associated with Fusarium Wilt Resistance in Common Bean." Genes 12, no. 5 (May 18, 2021): 765. http://dx.doi.org/10.3390/genes12050765.
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Fusarium wilt (Fusarium oxysporum f. sp. phaseoli, Fop) is one of the main fungal soil diseases in common bean. The aim of the present study was to identify genomic regions associated with Fop resistance through genome-wide association studies (GWAS) in a Mesoamerican Diversity Panel (MDP) and to identify potential common bean sources of Fop’s resistance. The MDP was genotyped with BARCBean6K_3BeadChip and evaluated for Fop resistance with two different monosporic strains using the root-dip method. Disease severity rating (DSR) and the area under the disease progress curve (AUDPC), at 21 days after inoculation (DAI), were used for GWAS performed with FarmCPU model. The p-value of each SNP was determined by resampling method and Bonferroni test. For UFV01 strain, two significant single nucleotide polymorphisms (SNPs) were mapped on the Pv05 and Pv11 for AUDPC, and the same SNP (ss715648096) on Pv11 was associated with AUDPC and DSR. Another SNP, mapped on Pv03, showed significance for DSR. Regarding IAC18001 strain, significant SNPs on Pv03, Pv04, Pv05, Pv07 and on Pv01, Pv05, and Pv10 were observed. Putative candidate genes related to nucleotide-binding sites and carboxy-terminal leucine-rich repeats were identified. The markers may be important future tools for genomic selection to Fop disease resistance in beans.
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Dashti, Hassan S., Jordi Merino, Jacqueline M. Lane, Yanwei Song, Caren E. Smith, Toshiko Tanaka, Nicola M. McKeown, et al. "Genome-wide association study of breakfast skipping links clock regulation with food timing." American Journal of Clinical Nutrition 110, no. 2 (June 13, 2019): 473–84. http://dx.doi.org/10.1093/ajcn/nqz076.
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ABSTRACT Background Little is known about the contribution of genetic variation to food timing, and breakfast has been determined to exhibit the most heritable meal timing. As breakfast timing and skipping are not routinely measured in large cohort studies, alternative approaches include analyses of correlated traits. Objectives The aim of this study was to elucidate breakfast skipping genetic variants through a proxy-phenotype genome-wide association study (GWAS) for breakfast cereal skipping, a commonly assessed correlated trait. Methods We leveraged the statistical power of the UK Biobank (n = 193,860) to identify genetic variants related to breakfast cereal skipping as a proxy-phenotype for breakfast skipping and applied several in silico approaches to investigate mechanistic functions and links to traits/diseases. Next, we attempted validation of our approach in smaller breakfast skipping GWAS from the TwinUK (n = 2,006) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (n = 11,963). Results In the UK Biobank, we identified 6 independent GWAS variants, including those implicated for caffeine (ARID3B/CYP1A1), carbohydrate metabolism (FGF21), schizophrenia (ZNF804A), and encoding enzymes important for N6-methyladenosine RNA transmethylation (METTL4, YWHAB, and YTHDF3), which regulates the pace of the circadian clock. Expression of identified genes was enriched in the cerebellum. Genome-wide correlation analyses indicated positive correlations with anthropometric traits. Through Mendelian randomization (MR), we observed causal links between genetically determined breakfast skipping and higher body mass index, more depressive symptoms, and smoking. In bidirectional MR, we demonstrated a causal link between being an evening person and skipping breakfast, but not vice versa. We observed association of our signals in an independent breakfast skipping GWAS in another British cohort (P = 0.032), TwinUK, but not in a meta-analysis of non-British cohorts from the CHARGE consortium (P = 0.095). Conclusions Our proxy-phenotype GWAS identified 6 genetic variants for breakfast skipping, linking clock regulation with food timing and suggesting a possible beneficial role of regular breakfast intake as part of a healthy lifestyle.
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Rice, Brian R., Samuel B. Fernandes, and Alexander E. Lipka. "Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture." Plant and Cell Physiology 61, no. 8 (May 14, 2020): 1427–37. http://dx.doi.org/10.1093/pcp/pcaa039.
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Abstract Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.
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Arning, Astrid, Milan Hiersche, Christoph Bidlingmaier, Monika Stoll, and Ulrike Nowak-Gottl. "A Genome Wide Association Study Identifies Novel Susceptibility Genes for Pediatric Venous Thrombosis,." Blood 118, no. 21 (November 18, 2011): 3336. http://dx.doi.org/10.1182/blood.v118.21.3336.3336.
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Abstract Abstract 3336 Background: Genome wide association studies (GWAS) are the current method of choice to dissect the genetic basis of common complex diseases. Up-to-date, studies in families with a known first onset of symptomatic arterial or venous thrombosis (VT) in early childhood are lacking. Methods: Here, we performed a GWAS in a large family-based study sample comprising 241 nuclear families with pediatric VT using the Illumina 660W Infinium SNP array. The average genotype call rate was >99.5%, and the genomic inflation factor was a= 1.012. Single point and haplotype association was assessed using the Transmission Disequilibrium Test (TDT) as implemented in PLINK and FBAT, respectively, and corrected for multiple testing using permutation testing. In addition, associations were corrected for age, gender, and in a subsequent analysis for Factor VLeiden. Results and Conclusion: Four SNPs exceeded the threshold for genome wide significance in this dataset as determined by permutation testing using 100.000 bootstrap permutations (p<10−5), and are likely true associations. Among these, 2 SNPs reside in a region on chromosome 6q13 comprising the gene for beta-1,3-glucoronyltransferase 2 (B3GAT2), a member of the human natural killer 1 (HNK1) carbohydrate pathway are associated with pediatric VT with p-values for rs1304029 (p=3.42×10−6) and rs2748331 (p=6.92×10−6). The corresponding haplotype association test resulted in a p=5.37×10−6 for the GA-haplotype, further underlining the robustness of the association. The SNPs rs636434 on chromosome 6q12 and rs1565242 on chromosome 15 both reside within hypothetical genes and are associated with VT with a p=2.70×10−6 and p=8.24×10−6, respectively. Additional SNPs exceeding a p<10−5 are included in subsequent analyses looking at gene networks and replication in independent study samples including our second GWAS on pediatric thromboembolic stroke (TS). Future studies using larger study samples are warranted to validate these findings and to characterize the genetic architecture underlying VT and TS in children. Disclosures: No relevant conflicts of interest to declare.
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Mohammadi, Ali, Sadegh Alijani, Seyed Abbas Rafat, and Rostam Abdollahi-Arpanahi. "Genome-Wide Association Study and Pathway Analysis for Female Fertility Traits in Iranian Holstein Cattle." Annals of Animal Science 20, no. 3 (July 1, 2020): 825–51. http://dx.doi.org/10.2478/aoas-2020-0031.
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AbstractFemale fertility is an important trait that contributes to cow’s profitability and it can be improved by genomic information. The objective of this study was to detect genomic regions and variants affecting fertility traits in Iranian Holstein cattle. A data set comprised of female fertility records and 3,452,730 pedigree information from Iranian Holstein cattle were used to predict the breeding values, which were then employed to estimate the de-regressed proofs (DRP) of genotyped animals. A total of 878 animals with DRP records and 54k SNP markers were utilized in the genome-wide association study (GWAS). The GWAS was performed using a linear regression model with SNP genotype as a linear covariate. The results showed that an SNP on BTA19, ARS-BFGL-NGS-33473, was the most significant SNP associated with days from calving to first service. In total, [69] significant SNPs were located within 27 candidate genes. Novel potential candidate genes include OSTN, DPP6, EphA5, CADPS2, Rfc1, ADGRB3, Myo3a, C10H14orf93, KIAA1217, RBPJL, SLC18A2, GARNL3, NCALD, ASPH, ASIC2, OR3A1, CHRNB4, CACNA2D2, DLGAP1, GRIN2A and ME3. These genes are involved in different pathways relevant to female fertility and other characteristics in mammals. Gene set enrichment analysis showed that thirteen GO terms had significant overrepresentation of genes statistically associated with female fertility traits. The results of network analysis identified CCNB1 gene as a hub gene in the progesterone-mediated oocyte maturation pathway, significantly associated with age at first calving. The candidate genes identified in this study can be utilized in genomic tests to improve reproductive performance in Holstein cattle.
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Dementieva, Natalia, Andrey Kudinov, Marina Pozovnikova, Elena Nikitkina, Nikolay Pleshanov, Julia Silyukova, Anna Krutikova, and Kirill Plemyashov. "14 Genome-wide association study of frozen sperm quality in cocks." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 12. http://dx.doi.org/10.1093/jas/skaa278.023.
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Abstract Preservation of the genetic material of unique breeds of hens involves the use of semen cryobanks. The aim of the study was to search for genomic associations with rooster sperm quality after freezing. We took into account the decrease in sperm motility after thawing. Genotyping of 96 individuals (roosters) using the Affymetrix Axiom®600k Array chip was performed. Genomic data were obtained using the Plink 1.9 software. DNA samples were taken with genotyping quality at SNP loci more than 95%, SNP with MAF > 0.01. GWAS was performed using EMMAX. SNP information in the corresponding genes was determined using the genomic browsers NCBI and Ensembl. Assessment of rooster sperm quality showed high individual variability in total motility before freezing (20–95%), and after freezing (5–70%). Significant SNPs were detected on the chromosomes GGA1 (rs315598192, rs316943858), GGA2 (rs312981435), GGA3 (rs13694743), GGA11 (rs314024471), GGA19 (rs316346648), GGA22 (rs31797024248, rga31780824824, gga22 (rs3178082424). Candidate genes ZC3HC1, CACNA2D1, BCKDHB, AMFR, and GOSR1 were identified. The annotated genes were associated with cell cycle regulation, the formation of calcium cytoskeleton, and the formation of a multienzyme complex associated with the inner membrane of mitochondria, glycosylated transmembrane receptor. The experimental data and results can be used to study the localization of regions and genes associated with phenotypic traits of frozen sperm quality. Authors acknowledge financial support from Russian Science Foundation № 18-16-00071.
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Alomari, Dalia, Kai Eggert, Nicolaus von Wirén, Andreas Polley, Jörg Plieske, Martin Ganal, Fang Liu, Klaus Pillen, and Marion Röder. "Whole-Genome Association Mapping and Genomic Prediction for Iron Concentration in Wheat Grains." International Journal of Molecular Sciences 20, no. 1 (December 25, 2018): 76. http://dx.doi.org/10.3390/ijms20010076.
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Malnutrition of iron (Fe) affects two billion people worldwide. Therefore, enhancing grain Fe concentration (GFeC) in wheat (Triticum aestivum L.) is an important goal for breeding. Here we study the genetic factors underlying GFeC trait by genome-wide association studies (GWAS) and the prediction abilities using genomic prediction (GP) in a panel of 369 European elite wheat varieties which was genotyped with 15,523 mapped single-nucleotide polymorphism markers (SNP) and a subpanel of 183 genotypes with 44,233 SNP markers. The resulting means of GFeC from three field experiments ranged from 24.42 to 52.42 μg·g−1 with a broad-sense heritability (H2) equaling 0.59 over the years. GWAS revealed 41 and 137 significant SNPs in the whole and subpanel, respectively, including significant marker-trait associations (MTAs) for best linear unbiased estimates (BLUEs) of GFeC on chromosomes 2A, 3B and 5A. Putative candidate genes such as NAC transcription factors and transmembrane proteins were present on chromosome 2A (763,689,738–765,710,113 bp). The GP for a GFeC trait ranged from low to moderate values. The current study reported GWAS of GFeC for the first time in hexaploid wheat varieties. These findings confirm the utility of GWAS and GP to explore the genetic architecture of GFeC for breeding programs aiming at the improvement of wheat grain quality.
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Yang, Yi, Xingjie Shi, Yuling Jiao, Jian Huang, Min Chen, Xiang Zhou, Lei Sun, Xinyi Lin, Can Yang, and Jin Liu. "CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies." Bioinformatics 36, no. 7 (November 22, 2019): 2009–16. http://dx.doi.org/10.1093/bioinformatics/btz880.
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Abstract Motivation Although genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required. Results In this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data. Availability and implementation The implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM. Supplementary information Supplementary data are available at Bioinformatics online.
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Ramos, Erin M., Douglas Hoffman, Heather A. Junkins, Donna Maglott, Lon Phan, Stephen T. Sherry, Mike Feolo, and Lucia A. Hindorff. "Phenotype–Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources." European Journal of Human Genetics 22, no. 1 (May 22, 2013): 144–47. http://dx.doi.org/10.1038/ejhg.2013.96.
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Yoosefzadeh-Najafabadi, Mohsen, Milad Eskandari, Sepideh Torabi, Davoud Torkamaneh, Dan Tulpan, and Istvan Rajcan. "Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components." International Journal of Molecular Sciences 23, no. 10 (May 16, 2022): 5538. http://dx.doi.org/10.3390/ijms23105538.
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A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.
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Takahashi, Sakae. "Casual Genes of Schizophrenia Detected by Genome-Wide Association Study (GWAS)." Journal of Nihon University Medical Association 73, no. 2 (2014): 106–8. http://dx.doi.org/10.4264/numa.73.106.
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Dang, Dongdong, Yuan Guan, Hongjian Zheng, Xuecai Zhang, Ao Zhang, Hui Wang, Yanye Ruan, and Li Qin. "Genome-Wide Association Study and Genomic Prediction on Plant Architecture Traits in Sweet Corn and Waxy Corn." Plants 12, no. 2 (January 9, 2023): 303. http://dx.doi.org/10.3390/plants12020303.
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Sweet corn and waxy corn has a better taste and higher accumulated nutritional value than regular maize, and is widely planted and popularly consumed throughout the world. Plant height (PH), ear height (EH), and tassel branch number (TBN) are key plant architecture traits, which play an important role in improving grain yield in maize. In this study, a genome-wide association study (GWAS) and genomic prediction analysis were conducted on plant architecture traits of PH, EH, and TBN in a fresh edible maize population consisting of 190 sweet corn inbred lines and 287 waxy corn inbred lines. Phenotypic data from two locations showed high heritability for all three traits, with significant differences observed between sweet corn and waxy corn for both PH and EH. The differences between the three subgroups of sweet corn were not obvious for all three traits. Population structure and PCA analysis results divided the whole population into three subgroups, i.e., sweet corn, waxy corn, and the subgroup mixed with sweet and waxy corn. Analysis of GWAS was conducted with 278,592 SNPs obtained from resequencing data; 184, 45, and 68 significantly associated SNPs were detected for PH, EH, and TBN, respectively. The phenotypic variance explained (PVE) values of these significant SNPs ranged from 3.50% to 7.0%. The results of this study lay the foundation for further understanding the genetic basis of plant architecture traits in sweet corn and waxy corn. Genomic selection (GS) is a new approach for improving quantitative traits in large plant breeding populations that uses whole-genome molecular markers. The marker number and marker quality are essential for the application of GS in maize breeding. GWAS can choose the most related markers with the traits, so it can be used to improve the predictive accuracy of GS.
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Lakhssassi, Kenza, Belén Lahoz, Pilar Sarto, Laura Pilar Iguácel, José Folch, José Luis Alabart, Malena Serrano, and Jorge Hugo Calvo. "Genome-Wide Association Study Demonstrates the Role Played by the CD226 Gene in Rasa Aragonesa Sheep Reproductive Seasonality." Animals 11, no. 4 (April 19, 2021): 1171. http://dx.doi.org/10.3390/ani11041171.
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A genome-wide association study (GWAS) was used to identify genomic regions influencing seasonality reproduction traits in Rasa Aragonesa sheep. Three traits associated with either ovarian function based on blood progesterone levels (total days of anoestrus and progesterone cycling months) or behavioral signs of oestrous (oestrous cycling months) were studied. The GWAS included 205 ewes genotyped using the 50k and 680k Illumina Ovine Beadchips. Only one SNP associated with the progesterone cycling months overcame the genome-wide significance level (rs404991855). Nine SNPs exhibited significant associations at the chromosome level, being the SNPs rs404991855 and rs418191944, that are located in the CD226 molecule (CD226) gene, associated with the three traits. This gene is related to reproductive diseases. Two other SNPs were located close to the neuropeptide Y (NPY) gene, which is involved in circadian rhythms. To validate the GWAS, partial characterization of both genes by Sanger sequencing, and genotyping of two synonymous and two nonsynonymous SNPs in the NPY and CD226 genes, respectively, were performed. SNP association analysis showed that only SNP rs404360094 in the exon 3 of the CD226 gene, which produces an amino acid substitution from asparagine (uncharged polar) to aspartic acid (acidic), was associated with the three seasonality traits. Our results suggest that the CD226 gene may be involved in the reproductive seasonality in Rasa Aragonesa.
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Ramzan, Faisal, Mehmet Gültas, Hendrik Bertram, David Cavero, and Armin Otto Schmitt. "Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations." Genes 11, no. 8 (August 5, 2020): 892. http://dx.doi.org/10.3390/genes11080892.
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Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype–phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.
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Abdalla, Emhimad A. E., Bayode O. Makanjuola, Benjamin J. Wood, and Christine F. Baes. "Genome-wide association study reveals candidate genes relevant to body weight in female turkeys (Meleagris gallopavo)." PLOS ONE 17, no. 3 (March 10, 2022): e0264838. http://dx.doi.org/10.1371/journal.pone.0264838.
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The underlying genetic mechanisms affecting turkey growth traits have not been widely investigated. Genome-wide association studies (GWAS) is a powerful approach to identify candidate regions associated with complex phenotypes and diseases in livestock. In the present study, we performed GWAS to identify regions associated with 18-week body weight in a turkey population. The data included body weight observations for 24,989 female turkeys genotyped based on a 65K SNP panel. The analysis was carried out using a univariate mixed linear model with hatch-week-year and the 2 top principal components fitted as fixed effects and the accumulated polygenic effect of all markers captured by the genomic relationship matrix as random. Thirty-three significant markers were observed on 1, 2, 3, 4, 7 and 12 chromosomes, while 26 showed strong linkage disequilibrium extending up to 410 kb. These significant markers were mapped to 37 genes, of which 13 were novel. Interestingly, many of the investigated genes are known to be involved in growth and body weight. For instance, genes AKR1D1, PARP12, BOC, NCOA1, ADCY3 and CHCHD7 regulate growth, body weight, metabolism, digestion, bile acid biosynthetic and development of muscle cells. In summary, the results of our study revealed novel candidate genomic regions and candidate genes that could be managed within a turkey breeding program and adapted in fine mapping of quantitative trait loci to enhance genetic improvement in this species.
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Chapleau, Richard R., Dara D. Regn, and Mauricio J. de Castro. "Surveying the Genomic Landscape Supporting the Development of Precision Military Aerospace Medicine." Aerospace Medicine and Human Performance 93, no. 2 (February 1, 2022): 89–93. http://dx.doi.org/10.3357/amhp.5929.2022.
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INTRODUCTION: Precision medicine is an approach to healthcare that is modifying clinical management by leveraging technological advances in genomics that assess a patient’s genetic information to identify unique predispositions. While the civilian sector is integrating genomics widely to personalize diagnosis and treatment, the military medical environment has reacted more slowly. The operational requirements of military service encourage a tailored approach for focusing military precision medicine on occupation-specific conditions. Here, we present a survey of the genomic landscape related to military aerospace medicine.METHODS: We collated observations from genome-wide association studies (GWAS) relating genetic markers to conditions that may negatively influence flight operations and for which the U.S. Air Force School of Aerospace Medicine’s Aeromedical Consult Service (ACS) provides aeromedical waiver guidance. Our sources for identifying relevant literature were the GWAS Catalog, the Atlas of GWAS Summary Statistics, and PubMed/Google Scholar searches.RESULTS: Using the ACS guidance as a starting point, we found 1572 papers describing 84 clinical conditions with genetic associations. The earliest aeromedical GWAS publication was in 2006, increasing to 225 publications in 2019. We identified 42,020 polymorphisms from more than 84 million participants across the studies.CONCLUSION: Our study revealed areas where deeper investigations into how genetic markers manifest in clinical diagnosis, prevention, or risk management could lead to increased medical readiness. Additionally, our results show those clinical areas for which guidance could include genetic risk considerations.Chapleau RR, Regn DD, de Castro MJ. Surveying the genomic landscape supporting the development of precision military aerospace medicine. Aerosp Med Hum Perform. 2022; 93(2):89–93.
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Liu, Shuai, Weiming Gong, Lu Liu, Ran Yan, Shukang Wang, and Zhongshang Yuan. "Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis." International Journal of Molecular Sciences 23, no. 21 (November 4, 2022): 13555. http://dx.doi.org/10.3390/ijms232113555.
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Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10−6, PeMAGMA = 1.94 × 10−7 for whole blood), IL-6R (PFUSION = 8.63 × 10−7, PeMAGMA = 2.74 × 10−6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10−5, PeMAGMA = 1.08 × 10−6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10−7, PeMAGMA = 3.93 × 10−8 for Liver), LAT (PFUSION = 1.53 × 10−4, PeMAGMA = 4.62 × 10−7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10−5, PeMAGMA = 1.73 × 10−7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.
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Deng, Chao, Wenzhu Peng, Zhi Ma, Caihuan Ke, Weiwei You, and Ying Wang. "AquaGWAS: A Genome-Wide Association Study Pipeline for Aquatic Animals and Its Application to Reference-Required and Reference-Free Genome-Wide Association Study for Abalone." Frontiers in Marine Science 9 (February 16, 2022). http://dx.doi.org/10.3389/fmars.2022.841561.
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Aquaculture is a rapidly growing industry that brings huge economic benefits. Genome-wide association study (GWAS) is critical for aquaculture species’ productivity, sustainability, and product quality. The current integrated GWAS pipeline either includes only specific limited steps or requires a complex prerequisite environment and configurations. In this study, we developed AquaGWAS, a highly user-friendly graphical user interface (GUI) GWAS pipeline, by integrating four well-known GWAS models. AquaGWAS is a complete GWAS pipeline from preprocessing, multiple choice of GWAS models, postprocessing to visualizations. AquaGWAS offers GUI easy running on Linux and automatically generates running command lines for high-performance computing (HPC) or non-GUI servers. AquaGWAS is free from installation, configurations, and complicated augment inputs. It offers whole packages of required reference files for 27 common aquatic species. Furthermore, aiming at the issue that the availability of genomic reference sequences limits single-nucleotide polymorphism (SNP) detection, we attempted to detect SNPs in Pacific abalone using classical alignment-based reference-required strategy and k-mer-based reference-free strategy combined with downstream AquaGWAS. On 222 resequencing data of Pacific abalone, two strategies detected 221,061 and 230,213 variants, respectively, with 180,161 common variants. The two strategies emphasized different variant situations: capturing variants missed by incomplete or inaccurate reference genomic sequence (k-mer-based) and capturing the indel variants having the baseline of genomic sequence (alignment-based). Combining the two strategies offers a complementary framework to obtain the accurate and complete GWAS analysis for non-model organism species. AquaGWAS is available at https://github.com/Ying-Lab/AquaGWAS.
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Berhe, Muez, Komivi Dossa, Jun You, Pape Adama Mboup, Idrissa Navel Diallo, Diaga Diouf, Xiurong Zhang, and Linhai Wang. "Genome-wide association study and its applications in the non-model crop Sesamum indicum." BMC Plant Biology 21, no. 1 (June 22, 2021). http://dx.doi.org/10.1186/s12870-021-03046-x.
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Abstract Background Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame. Results In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD (http://sigedid.ucad.sn/) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame. Conclusions Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop.
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Sheoran, S., S. Jaiswal, N. Raghav, R. Sharma, Sabhyata, A. Gaur, J. Jaisri, et al. "Genome-Wide Association Study and Post-genome-Wide Association Study Analysis for Spike Fertility and Yield Related Traits in Bread Wheat." Frontiers in Plant Science 12 (February 11, 2022). http://dx.doi.org/10.3389/fpls.2021.820761.
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Spike fertility and associated traits are key factors in deciding the grain yield potential of wheat. Genome-wide association study (GWAS) interwoven with advanced post-GWAS analysis such as a genotype-phenotype network (geno-pheno network) for spike fertility, grain yield, and associated traits allow to identify of novel genomic regions and represents attractive targets for future marker-assisted wheat improvement programs. In this study, GWAS was performed on 200 diverse wheat genotypes using Breeders’ 35K Axiom array that led to the identification of 255 significant marker-trait associations (MTAs) (–log10P ≥ 3) for 15 metric traits phenotyped over three consecutive years. MTAs detected on chromosomes 3A, 3D, 5B, and 6A were most promising for spike fertility, grain yield, and associated traits. Furthermore, the geno-pheno network prioritised 11 significant MTAs that can be utilised as a minimal marker system for improving spike fertility and yield traits. In total, 119 MTAs were linked to 81 candidate genes encoding different types of functional proteins involved in various key pathways that affect the studied traits either way. Twenty-two novel loci were identified in present GWAS, twelve of which overlapped by candidate genes. These results were further validated by the gene expression analysis, Knetminer, and protein modelling. MTAs identified from this study hold promise for improving yield and related traits in wheat for continued genetic gain and in rapidly evolving artificial intelligence (AI) tools to apply in the breeding program.