• zoology
• evolution

• Animals
• Molecular Sequence Annotation
• Genome
• Chromosomes
• Sheep/genetics
• Genomics

• the Natural Science Foundation of China (No. 32101408)

• DNA sequencing
• DRAGEN
• Illumina NovaSeq 6000
• high‐throughput sequencing
• next‐generation sequencing

• Quality Control
• Humans
• Whole Genome Sequencing
• Biometry/methods
• Biostatistics/methods
• High-Throughput Nucleotide Sequencing

• 81Z0710102 German Center for Cardiovascular Research (DZHK)
• German Research Foundation
• EU Horizon 2020
• EU ERANet
• ERAPreMed Programmes
• German Ministry of Education and Research
• Kühne Foundation

• malaria
• malaria vector control
• malaria vectors
• morphological classification

• PMDS22061523254 National Research Foundation

• Bactericera trigonica
• Carrot yellows disease
• Phloem-limited

• Animals
• Daucus carota
• Waikavirus/genetics
• Hemiptera
• Phylogeny
• Plant Diseases

• DNA sequencing
• cystic fibrosis
• dried blood spots
• newborn screening
• phenylketonuria
• whole genome sequencing

Huntington's disease (HD) is a dominantly inherited neurodegenerative disease caused by an expanded CAG repeat in huntingtin (HTT). Given an important role for HTT in development and significant neurodegeneration at the time of clinical manifestation in HD, early treatment of allele-specific drugs represents a promising strategy. The feasibility of an allele-specific antisense oligonucleotide (ASO) targeting single-nucleotide polymorphisms (SNPs) has been demonstrated in models of HD. Here, we constructed a map of haplotype-specific insertion-deletion variations (indels) to develop alternative mutant-HTT-specific strategies. We mapped indels annotated in the 1000 Genomes Project data on common HTT haplotypes, revealing candidate indels for mutant-specific HTT targeting. Subsequent sequencing of an HD family confirmed candidate sites and revealed additional allele-specific indels. Interestingly, the most common normal HTT haplotype carries indels of big allele length differences at many sites, further uncovering promising haplotype-specific targets. When patient-derived cells carrying the most common HTT diplotype were treated with ASOs targeting the mutant alleles of candidate indels (rs772629195 or rs72239206), complete mutant specificity was observed. In summary, our map of haplotype-specific indels permits the identification of allele-specific targets in HD subjects, potentially contributing to the development of safe HTT-lowering therapeutics that are suitable for early treatment in HD.

• Huntington's disease
• SNP
• allele-specific targeting
• haplotype-specific indel
• indel

Gene expression (GE) analysis of FDXR, DDB2, WNT3 and POU2AF1 is a promising approach for identification of clinically relevant groups (unexposed, low- and high exposed) after radiological/nuclear events. However, results from international biodosimetry exercises have shown differences in dose estimates based on radiation-induced GE of the four genes. Also, differences in GE using next-generation-sequening (NGS) and validation with quantitative real-time polymerase chain reaction (qRT-PCR) was reported. These discrepancies could be caused by radiation-responsive differences among exons of the same gene. We performed GE analysis with qRT-PCR using TaqMan-assays covering all exon-regions of FDXR, DDB2, WNT3 and POU2AF1. Peripheral whole blood from three healthy donors was X-irradiated with 0, 0.5 and 4 Gy. After 24 and 48 h a dose-dependent up-regulation across almost all exon-regions for FDXR and DDB2 (4–42-fold) was found. A down-regulation for POU2AF1 (two- to threefold) and WNT3 (< sevenfold) at the 3’-end was found at 4 Gy irradiation only. Hence, this confirms our hypothesis for radiation-responsive exon-regions for WNT3 and POU2AF1, but not for FDXR and DDB2. Finally, we identified the most promising TaqMan-assays for FDXR (e.g. AR7DTG3, Hs00244586_m1), DDB2 (AR47X6H, Hs03044951_m1), WNT3 (Hs00902258_m1, Hs00902257_m1) and POU2AF1 (Hs01573370_g1, Hs01573371_m1) for biodosimetry purposes and acute radiation syndrome prediction, considering several criteria (detection limit, dose dependency, time persistency, inter-individual variability).

• drug resistance
• elimination
• malaria
• next generation sequencing
• whole genome sequencing

• 120368 National Research Foundation

Chronic myeloid leukemia is a type of blood cancer that is regarded as a success story in determining the exact biological origin, pathogenesis and development of a molecularly targeted (mutation-specific) therapy that has led to successful treatment of this fatal cancer. It is caused by the BCR-ABL fusion gene, which is formed from the translocation between chromosomes 9 and 22. Anti-BCR-ABL drugs, known as tyrosine kinase inhibitors (TKIs), have led to long-term remissions in more than 80% of CML patients and even cure in about one-third of patients. Nevertheless, many patients face drug resistance, and disease progression occurs in about 30% of CML patients, leading to morbidities and mortality. Unfortunately, no biomarkers of CML progression are available due to a poor understanding of the mechanism of progression. Therefore, finding reliable molecular biomarkers of CML progression is one of the most attractive research areas in 21st-century cancer research. In this study, we report novel genomic variants exclusively found in all our advanced-phase CML patients. This study will help in identifying CML patients at risk of disease progression and timely therapeutic interventions to avoid or at least delay fatal disease progression in this cancer.

Background: Chronic myeloid leukemia (CML) is initiated in bone marrow due to chromosomal translocation t(9;22) leading to fusion oncogene BCR-ABL. Targeting BCR-ABL by tyrosine kinase inhibitors (TKIs) has changed fatal CML into an almost curable disease. Despite that, TKIs lose their effectiveness due to disease progression. Unfortunately, the mechanism of CML progression is poorly understood and common biomarkers for CML progression are unavailable. This study was conducted to find novel biomarkers of CML progression by employing whole-exome sequencing (WES). Materials and Methods: WES of accelerated phase (AP) and blast crisis (BC) CML patients was carried out, with chronic-phase CML (CP-CML) patients as control. After DNA library preparation and exome enrichment, clustering and sequencing were carried out using Illumina platforms. Statistical analysis was carried out using SAS/STAT software version 9.4, and R package was employed to find mutations shared exclusively by all AP-/BC-CML patients. Confirmation of mutations was carried out using Sanger sequencing and protein structure modeling using I-TASSER followed by mutant generation and visualization using PyMOL. Results: Three novel genes (ANKRD36, ANKRD36B and PRSS3) were mutated exclusively in all AP-/BC-CML patients. Only ANKRD36 gene mutations (c.1183_1184 delGC and c.1187_1185 dupTT) were confirmed by Sanger sequencing. Protein modeling studies showed that mutations induce structural changes in ANKRD36 protein. Conclusions: Our studies show that ANKRD36 is a potential common biomarker and drug target of early CML progression. ANKRD36 is yet uncharacterized in humans. It has the highest expression in bone marrow, specifically myeloid cells. We recommend carrying out further studies to explore the role of ANKRD36 in the biology and progression of CML.

• ANRD36
• CML
• common biomarker
• disease progression
• drug target

• Annotation
• Bioinformatics
• Genomics
• Genotyping
• Pipelines
• Prediction
• Tools
• Variants
• WES
• WGS

• BAM, Binary Alignment Map
• BWA, Burrows-Wheeler Aligner
• Background error
• CLL, Chronic Lymphocytic Leukaemia
• COSMIC, Catalogue of Somatic Mutations in Cancer
• Cancer genomics
• Clinical samples
• ESP, Exome Sequencing Project
• FF, Fresh Frozen
• FFPE, Formalin Fixed Paraffin Embedded
• FL, Follicular Lymphoma
• GATK, Genome Analysis Toolkit
• ICGC, International Cancer Genome Consortium
• MBC, Molecular Barcode
• NCCN, the National Comprehensive Cancer Network®
• NGS, Next Generation Sequencing
• NHL, Non-Hodgkin Lymphoma
• NSCLC, Non-Small Cell Lung Carcinoma
• PCR duplicates
• QC, Quality Control
• SAM, Sequence Alignment Map
• TCGA, The Cancer Genome Atlas
• TS, Targeted Sequencing
• Targeted sequencing
• UMI, Unique Molecular Identifiers
• VAF, Variant Allele Frequency
• Variant calling
• WES, Whole Exome Sequencing
• WGS, Whole Genome Sequencing
• tFL, Transformed Follicular Lymphoma

• Genotyping
• Human leukocyte antigen
• Next generation sequencing

• DNA
• Ion Torrent
• NGS
• Saliva
• WES
• Whole blood

• Adult
• DNA/chemistry
• DNA/genetics
• DNA/standards
• Female
• High-Throughput Nucleotide Sequencing/methods
• High-Throughput Nucleotide Sequencing/standards
• Humans
• Male
• Saliva/chemistry
• Exome Sequencing/methods
• Exome Sequencing/standards

• software
• genomics

• Breast Neoplasms/genetics
• Carcinogenesis/genetics
• Computational Biology/methods
• Female
• Genome, Human/genetics
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Mutation/genetics
• Sequence Analysis, DNA/methods
• Software

• Molecular and clinical diagnosis
• Next-generation sequencing
• Skeletal dysplasias

• Animals
• Bone Diseases/genetics
• Genomics/methods
• Glycosaminoglycans/biosynthesis
• High-Throughput Nucleotide Sequencing
• Humans
• Rare Diseases/genetics

• Antiviral immunity
• Exome
• Gene annotation
• Host genetics
• Read depth
• Sequence alignment
• Variant annotation
• Variant calling
• Whole-exome sequencing

• Cell Line
• Exome
• Genetic Predisposition to Disease
• High-Throughput Nucleotide Sequencing/methods
• Humans
• Virus Diseases/genetics
• Virus Diseases/immunology
• Viruses/genetics
• Viruses/immunology

• Diffusion of Innovation
• Genome, Human
• Genome-Wide Association Study/methods
• Genotype
• Humans
• Patient Acceptance of Health Care
• Phenotype
• Qatar

The increasing adoption of clinical whole-genome resequencing (WGS) demands for highly accurate and reproducible variant calling (VC) methods. The observed discordance between state-of-the-art VC pipelines, however, indicates that the current practice still suffers from non-negligible numbers of false positive and negative SNV and INDEL calls that were shown to be enriched among discordant calls but also in genomic regions with low sequence complexity.

Here, we describe our method ReliableGenome (RG) for partitioning genomes into high and low concordance regions with respect to a set of surveyed VC pipelines. Our method combines call sets derived by multiple pipelines from arbitrary numbers of datasets and interpolates expected concordance for genomic regions without data. By applying RG to 219 deep human WGS datasets, we demonstrate that VC concordance depends predominantly on genomic context rather than the actual sequencing data which manifests in high recurrence of regions that can/cannot be reliably genotyped by a single method. This enables the application of pre-computed regions to other data created with comparable sequencing technology and software. RG outperforms comparable efforts in predicting VC concordance and false positive calls in low-concordance regions which underlines its usefulness for variant filtering, annotation and prioritization. RG allows focusing resource-intensive algorithms (e.g. consensus calling methods) on the smaller, discordant share of the genome (20–30%) which might result in increased overall accuracy at reasonable costs. Our method and analysis of discordant calls may further be useful for development, benchmarking and optimization of VC algorithms and for the relative comparison of call sets between different studies/pipelines.

RG was implemented in Java, source code and binaries are freely available for non-commercial use at https://github.com/popitsch/wtchg-rg/.

Supplementary data are available at Bioinformatics online.

• indels
• mutation detection
• next-generation sequencing
• snvs
• trio calling
• variant calling
• varscan 2

Even though the fungal kingdom contains more than 3 million species, little is known about the biological roles of RNA silencing in fungi. The Colletotrichum genus comprises fungal species that are pathogenic for a wide range of crop species worldwide. To investigate the role of RNA silencing in the ascomycete fungus Colletotrichum higginsianum, knock-out mutants affecting genes for three RNA-dependent RNA polymerase (RDR), two Dicer-like (DCL), and two Argonaute (AGO) proteins were generated by targeted gene replacement. No effects were observed on vegetative growth for any mutant strain when grown on complex or minimal media. However, Δdcl1, Δdcl1Δdcl2 double mutant, and Δago1 strains showed severe defects in conidiation and conidia morphology. Total RNA transcripts and small RNA populations were analyzed in parental and mutant strains. The greatest effects on both RNA populations was observed in the Δdcl1, Δdcl1Δdcl2, and Δago1 strains, in which a previously uncharacterized dsRNA mycovirus [termed Colletotrichum higginsianum non-segmented dsRNA virus 1 (ChNRV1)] was derepressed. Phylogenetic analyses clearly showed a close relationship between ChNRV1 and members of the segmented Partitiviridae family, despite the non-segmented nature of the genome. Immunoprecipitation of small RNAs associated with AGO1 showed abundant loading of 5’U-containing viral siRNA. C. higginsianum parental and Δdcl1 mutant strains cured of ChNRV1 revealed that the conidiation and spore morphology defects were primarily caused by ChNRV1. Based on these results, RNA silencing involving ChDCL1 and ChAGO1 in C. higginsianum is proposed to function as an antiviral mechanism.

Colletotrichum sp. comprises a diverse group of fungal pathogens that attack over 3000 plant species worldwide. Understanding the underlying mechanisms that govern fungal development and pathogenicity may enable more effective and sustainable approaches to crop disease management and control. In most organisms, RNA silencing is an important mechanism to control endogenous and exogenous RNA. RNA silencing utilizes small regulatory molecules (small RNAs) produced by proteins called Dicer (DCL), and exercise their function though effector proteins named Argonaute (AGO). Here, we investigated the role of RNA silencing machinery in the fungus Colletotrichum higginsianum, by generating deletions in genes encoding RNA silencing components. Severe defects were observed in both conidiation and conidia morphology in the Δdcl1, Δdcl1Δdcl2, and Δago1 strains. Analysis of transcripts and small RNAs revealed an uncharacterized dsRNA virus persistently infecting C. higginsianum. The virus was shown (1) to be de-repressed in the Δdcl1, Δdcl1Δdcl2 and Δago1 strains, and (2) to cause the conidiation and spore mutant phenotypes. Our results indicate that C. higginsianum employs RNA silencing as an antiviral mechanism to suppress viruses and their debilitating effects.

Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.

• mutation annotation
• pathway analysis
• sequence alignment
• single-nucleotide polymorphism
• template preparation

• mutation spectra
• next-generation sequencing
• transgenic rodent assay
• benzo[a]pyrene
• muta™mouse

• Animals
• Benzo(a)pyrene/toxicity
• High-Throughput Nucleotide Sequencing
• Lac Operon
• Mice
• Mice, Transgenic
• Mutagenesis
• Mutation/drug effects
• Mutation/genetics
• Sequence Analysis, DNA

• Canadian Institutes of Health Research

The study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.).

Moreover, the huge volume of data generated by NGS platforms introduces unprecedented computational and technological challenges to efficiently analyze and store sequence data and results.

In order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq).

Through a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs.

The rapid advances in genome sequencing technologies have resulted in an unprecedented number of genome variations being discovered in humans. However, there has been very limited coverage of interpretation of the personal genome sequencing data in terms of diseases.

In this paper we present the first computational analysis scheme for interpreting personal genome data by simultaneously considering the functional impact of damaging variants and curated disease-gene association data. This method is based on mutual information as a measure of the relative closeness between the personal genome and diseases. We hypothesize that a higher mutual information score implies that the personal genome is more susceptible to a particular disease than other diseases.

The method was applied to the sequencing data of 50 acute myeloid leukemia (AML) patients in The Cancer Genome Atlas. The utility of associations between a disease and the personal genome was explored using data of healthy (control) people obtained from the 1000 Genomes Project. The ranks of the disease terms in the AML patient group were compared with those in the healthy control group using "Leukemia, Myeloid, Acute" (C04.557.337.539.550) as the corresponding MeSH disease term.

The mutual information rank of the disease term was substantially higher in the AML patient group than in the healthy control group, which demonstrates that the proposed methodology can be successfully applied to infer associations between the personal genome and diseases.

Overall, the area under the receiver operating characteristics curve was significantly larger for the AML patient data than for the healthy controls. This methodology could contribute to consequential discoveries and explanations for mining personal genome sequencing data in terms of diseases, and have versatility with respect to genomic-based knowledge such as drug-gene and environmental-factor-gene interactions.

• Cancer
• database
• next generation sequencing (NGS)
• single nucleotide polymorphism (SNP)

• epigenetics
• metagenomics
• molecular therapy
• next-generation sequencing
• somatic mutation

• GC bias
• HLA genotyping
• Illumina Nextera
• NGS
• Transposase

The successful completion of the Human Genome Project (HGP) was an unprecedented scientific advance that has become an invaluable resource in the search for genes that cause monogenic and common (polygenic) diseases. Prior to the HGP, linkage analysis had successfully mapped many disease genes for monogenic disorders; however, the limitations of this approach were particularly evident for identifying causative genes in rare genetic disorders affecting lifespan and/or reproductive fitness, such as skeletal dysplasias. In this review, we illustrate the challenges of mapping disease genes in such conditions through the ultra-rare disorder fibrodysplasia ossificans progressiva (FOP) and we discuss the advances that are being made through current massively parallel (“next generation”) sequencing (MPS) technologies.

• Non-human sequences
• Sequencing contamination
• Unmapped reads

• DNA Contamination
• DNA, Bacterial/chemistry
• DNA, Plant/chemistry
• DNA, Viral/chemistry
• Genome, Human
• Humans

• U01 HG005719 NHGRI NIH HHS
• 1U01HG-005719-01 NHGRI NIH HHS

• Animals
• Avian Proteins/genetics
• Chickens/genetics
• Chromosome Mapping
• DNA Mutational Analysis
• Female
• Gene Ontology
• Genome
• High-Throughput Nucleotide Sequencing
• INDEL Mutation
• Molecular Sequence Annotation
• Polymorphism, Single Nucleotide

High-throughput sequencing studies (HTS) have been highly successful in identifying the genetic causes of human disease, particularly those following Mendelian inheritance. Many HTS studies to date have been performed without utilizing available family relationships between samples. Here, we discuss the many merits and occasional pitfalls of using identity by descent information in conjunction with HTS studies. These methods are not only applicable to family studies but are also useful in cohorts of apparently unrelated, ‘sporadic’ cases and small families underpowered for linkage and allow inference of relationships between individuals. Incorporating familial/pedigree information not only provides powerful filtering options for the extensive variant lists that are usually produced by HTS but also allows valuable quality control checks, insights into the genetic model and the genotypic status of individuals of interest. In particular, these methods are valuable for challenging discovery scenarios in HTS analysis, such as in the study of populations poorly represented in variant databases typically used for filtering, and in the case of poor-quality HTS data.

Genotypes generated in next generation sequencing studies contain errors which can significantly impact the power to detect signals in common and rare variant association tests. These genotyping errors are not explicitly filtered by the standard GATK Variant Quality Score Recalibration (VQSR) tool and thus remain a source of errors in whole exome sequencing (WES) projects that follow GATK’s recommended best practices. Therefore, additional data filtering methods are required to effectively remove these errors before performing association analyses with complex phenotypes. Here we empirically derive thresholds for genotype and variant filters that, when used in conjunction with the VQSR tool, achieve higher data quality than when using VQSR alone.

The detailed filtering strategies improve the concordance of sequenced genotypes with array genotypes from 99.33% to 99.77%; improve the percent of discordant genotypes removed from 10.5% to 69.5%; and improve the Ti/Tv ratio from 2.63 to 2.75. We also demonstrate that managing batch effects by separating samples based on different target capture and sequencing chemistry protocols results in a final data set containing 40.9% more high-quality variants. In addition, imputation is an important component of WES studies and is used to estimate common variant genotypes to generate additional markers for association analyses. As such, we demonstrate filtering methods for imputed data that improve genotype concordance from 79.3% to 99.8% while removing 99.5% of discordant genotypes.

The described filtering methods are advantageous for large population-based WES studies designed to identify common and rare variation associated with complex diseases. Compared to data processed through standard practices, these strategies result in substantially higher quality data for common and rare association analyses.

Technological advances coupled with decreasing costs are bringing whole genome and whole exome sequencing closer to routine clinical use. One of the hurdles to clinical implementation is the high number of variants of unknown significance. For cancer-susceptibility genes, the difficulty in interpreting the clinical relevance of the genomic variants is compounded by the fact that most of what is known about these variants comes from the study of highly selected populations, such as cancer patients or individuals with a family history of cancer. The genetic variation in known cancer-susceptibility genes in the general population has not been well characterized to date. To address this gap, we profiled the nonsynonymous genomic variation in 158 genes causally implicated in carcinogenesis using high-quality whole genome sequences from an ancestrally diverse cohort of 681 healthy individuals. We found that all individuals carry multiple variants that may impact cancer susceptibility, with an average of 68 variants per individual. Of the 2,688 allelic variants identified within the cohort, most are very rare, with 75% found in only 1 or 2 individuals in our population. Allele frequencies vary between ancestral groups, and there are 21 variants for which the minor allele in one population is the major allele in another. Detailed analysis of a selected subset of 5 clinically important cancer genes, BRCA1, BRCA2, KRAS, TP53, and PTEN, highlights differences between germline variants and reported somatic mutations. The dataset can serve a resource of genetic variation in cancer-susceptibility genes in 6 ancestry groups, an important foundation for the interpretation of cancer risk from personal genome sequences.

• Adolescent
• Adult
• Alleles
• Cohort Studies
• Female
• Gene Frequency/genetics
• Gene Pool
• Genes, Neoplasm
• Genetic Predisposition to Disease
• Genome, Human/genetics
• Germ-Line Mutation/genetics
• Health
• Humans
• Male
• Middle Aged
• Models, Molecular
• Neoplasms/genetics
• Open Reading Frames/genetics
• Phylogeny
• Sequence Analysis, DNA
• Young Adult

• Computer Security
• Electronic Health Records/ethics
• Electronic Health Records/organization & administration
• Genetic Privacy/ethics
• Genetic Privacy/legislation & jurisprudence
• Genomics/ethics
• Genomics/organization & administration
• Health Information Management/ethics
• Health Information Management/organization & administration
• Health Records, Personal
• Humans
• Precision Medicine/methods

• Adult
• Female
• Genetic Predisposition to Disease
• Genetic Privacy
• Genetic Testing/methods
• Genome, Human
• Genomics/methods
• High-Throughput Nucleotide Sequencing
• Humans
• Male
• Middle Aged
• Mutation
• Neoplasms/genetics
• Oncogenes/genetics
• Polymerase Chain Reaction
• Precision Medicine/methods
• Precision Medicine/trends

• P30 CA008748 NCI NIH HHS

Colorectal cancer (CRC) is one of the most frequent neoplasms and an important cause of mortality in the developed world. This cancer is caused by both genetic and environmental factors although 35% of the variation in CRC susceptibility involves inherited genetic differences. Mendelian syndromes account for about 5% of the total burden of CRC, with Lynch syndrome and familial adenomatous polyposis the most common forms. Excluding hereditary forms, there is an important fraction of CRC cases that present familial aggregation for the disease with an unknown germline genetic cause. CRC can be also considered as a complex disease taking into account the common disease-commom variant hypothesis with a polygenic model of inheritance where the genetic components of common complex diseases correspond mostly to variants of low/moderate effect. So far, 30 common, low-penetrance susceptibility variants have been identified for CRC. Recently, new sequencing technologies including exome- and whole-genome sequencing have permitted to add a new approach to facilitate the identification of new genes responsible for human disease predisposition. By using whole-genome sequencing, germline mutations in the POLE and POLD1 genes have been found to be responsible for a new form of CRC genetic predisposition called polymerase proofreading-associated polyposis.

• Colorectal neoplasm, genetic predisposition to disease
• Next generation sequencing
• Genotype-phenotype correlation
• Genetic variant
• Single nucleotide polymorphism

• Adenomatous Polyposis Coli/diagnosis
• Adenomatous Polyposis Coli/genetics
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms, Hereditary Nonpolyposis/diagnosis
• Colorectal Neoplasms, Hereditary Nonpolyposis/genetics
• Environment
• Genetic Association Studies
• Genetic Predisposition to Disease
• Genetic Variation
• Genome, Human
• Geography
• Germ-Line Mutation
• Humans
• Incidence
• Polymorphism, Single Nucleotide
• Risk Factors
• Sequence Analysis, DNA