BACKGROUND: Colorectal cancer is still the second leading cause of cancer-related deaths and thus biomarkers allowing prediction of the resistance of patients to therapy and estimating their prognosis are needed. We designed a panel of 558 genes with pharmacogenomics records related to 5-fluorouracil resistance, genes important for sensitivity to other frequently used drugs, major oncodrivers, and actionable genes. We performed a target enrichment sequencing of DNA from tumors and matched blood samples of patients, and compared the results with patient prognosis stratified by systemic adjuvant chemotherapy. RESULTS: The median number of detected variants per tumor sample was 18.5 with 4 classified as having a high predicted functional effect and 14.5 moderate effect. APC, TP53, and KRAS were the most frequent mutated genes (64%, 59%, and 42% of mutated samples, respectively) followed by FAT4 (23%), FBXW7, and PIK3CA (16% for both). Patients with advanced stage III had more frequently APC, TP53, or KRAS mutations than those in stages I or II. KRAS mutation counts followed an increasing trend with grade (G1 < G2 < G3). The response to adjuvant therapy was worse in carriers of frameshift mutations in APC or 12D variant in KRAS, but none of these oncodrivers had prognostic value. Carriage of somatic mutations in any of the genes ABCA13, ANK2, COL7A1, NAV3, or UNC80 had prognostic relevance for worse overall survival (OS) of all patients. In contrast, mutations in FLG, GLI3, or UNC80 were prognostic in the same direction for patients untreated, and mutations in COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH, or TENM4 for patients treated with adjuvant therapy. The first association was externally validated. From all germline variants with high or moderate predicted functional effects (median 326 per patient), > 5% frequency and positive Manhattan plot based on 3-year RFS, rs72753407 in NFACS, rs34621071 in ERBB4, and rs2444274 in RIF1 were significantly associated with RFS, OS or both. CONCLUSIONS: The present study identified several putative somatic and germline genetic events with prognostic potential for colorectal cancer that should undergo functional characterization.

• MeSH
• Drug Resistance, Neoplasm genetics   MeSH
• Adult MeSH
• F-Box-WD Repeat-Containing Protein 7 genetics   MeSH
• Pharmacogenetics methods   MeSH
• Fluorouracil therapeutic use   MeSH
• Class I Phosphatidylinositol 3-Kinases MeSH
• Colorectal Neoplasms * genetics   drug therapy   pathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Mutation genetics   MeSH
• Biomarkers, Tumor genetics   MeSH
• Tumor Suppressor Protein p53 genetics   MeSH
• Prognosis MeSH
• Adenomatous Polyposis Coli Protein genetics   MeSH
• Proto-Oncogene Proteins p21(ras) genetics   MeSH
• Aged MeSH
• High-Throughput Nucleotide Sequencing MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

The genome-wide association study (GWAS) is a popular genomic approach that identifies genomic regions associated with a phenotype and, thus, aims to discover causative mutations (CM) in the genes underlying the phenotype. However, GWAS discoveries are limited by many factors and typically identify associated genomic regions without the further ability to compare the viability of candidate genes and actual CMs. Therefore, the current methodology is limited to CM identification. In our recent work, we presented a novel approach to an empowered "GWAS to Genes" strategy that we named Synthetic phenotype to causative mutation (SP2CM). We established this strategy to identify CMs in soybean genes and developed a web-based tool for accuracy calculation (AccuTool) for a reference panel of soybean accessions. Here, we describe our further development of the tool that extends its utilization for other species and named it AccuCalc. We enhanced the tool for the analysis of datasets with a low-frequency distribution of a rare phenotype by automated formatting of a synthetic phenotype and added another accuracy-based GWAS evaluation criterion to the accuracy calculation. We designed AccuCalc as a Python package for GWAS data analysis for any user-defined species-independent variant calling format (vcf) or HapMap format (hmp) as input data. AccuCalc saves analysis outputs in user-friendly tab-delimited formats and also offers visualization of the GWAS results as Manhattan plots accentuated by accuracy. Under the hood of Python, AccuCalc is publicly available and, thus, can be used conveniently for the SP2CM strategy utilization for every species.

• MeSH
• Genome-Wide Association Study * methods   MeSH
• Phenotype MeSH
• Genome MeSH
• Genomics * methods   MeSH
• Mutation MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

OBJECTIVES: Genome-wide meta-analyses of clinically defined gout were performed to identify subtype-specific susceptibility loci. Evaluation using selection pressure analysis with these loci was also conducted to investigate genetic risks characteristic of the Japanese population over the last 2000-3000 years. METHODS: Two genome-wide association studies (GWASs) of 3053 clinically defined gout cases and 4554 controls from Japanese males were performed using the Japonica Array and Illumina Array platforms. About 7.2 million single-nucleotide polymorphisms were meta-analysed after imputation. Patients were then divided into four clinical subtypes (the renal underexcretion type, renal overload type, combined type and normal type), and meta-analyses were conducted in the same manner. Selection pressure analyses using singleton density score were also performed on each subtype. RESULTS: In addition to the eight loci we reported previously, two novel loci, PIBF1 and ACSM2B, were identified at a genome-wide significance level (p<5.0×10-8) from a GWAS meta-analysis of all gout patients, and other two novel intergenic loci, CD2-PTGFRN and SLC28A3-NTRK2, from normal type gout patients. Subtype-dependent patterns of Manhattan plots were observed with subtype GWASs of gout patients, indicating that these subtype-specific loci suggest differences in pathophysiology along patients' gout subtypes. Selection pressure analysis revealed significant enrichment of selection pressure on ABCG2 in addition to ALDH2 loci for all subtypes except for normal type gout. CONCLUSIONS: Our findings on subtype GWAS meta-analyses and selection pressure analysis of gout will assist elucidation of the subtype-dependent molecular targets and evolutionary involvement among genotype, phenotype and subtype-specific tailor-made medicine/prevention of gout and hyperuricaemia.

• MeSH
• ATP Binding Cassette Transporter, Subfamily G, Member 2 genetics   MeSH
• Genome-Wide Association Study * MeSH
• Gout epidemiology   genetics   MeSH
• Phenotype MeSH
• Genetic Predisposition to Disease ethnology   MeSH
• Genetic Loci MeSH
• Genotype MeSH
• Risk Assessment MeSH
• Incidence MeSH
• Humans MeSH
• Aldehyde Dehydrogenase, Mitochondrial genetics   MeSH
• Neoplasm Proteins genetics   MeSH
• Prognosis MeSH
• Reference Values MeSH
• Case-Control Studies MeSH
• Severity of Illness Index MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Japan MeSH