The genomics of adaptation to climate in European great tit (Parus major) populations.

The recognition that climate change is occurring at an unprecedented rate means that there is increased urgency in understanding how organisms can adapt to a changing environment. Wild great tit (Parus major) populations represent an attractive ecological model system to understand the genomics of climate adaptation. They are widely distributed across Eurasia and they have been documented to respond to climate change. We performed a Bayesian genome-environment analysis, by combining local climate data with single nucleotide polymorphisms genotype data from 20 European populations (broadly spanning the species' continental range). We found 36 genes putatively linked to adaptation to climate. Following an enrichment analysis of biological process Gene Ontology (GO) terms, we identified over-represented terms and pathways among the candidate genes. Because many different genes and GO terms are associated with climate variables, it seems likely that climate adaptation is polygenic and genetically complex. Our findings also suggest that geographical climate adaptation has been occurring since great tits left their Southern European refugia at the end of the last ice age. Finally, we show that substantial climate-associated genetic variation remains, which will be essential for adaptation to future changes.

Genome-wide association study reveals WRKY42 as a novel plant transcription factor that influences oviposition preference of Pieris butterflies.

Insect herbivores are amongst the most destructive plant pests, damaging both naturally occurring and domesticated plants. As sessile organisms, plants make use of structural and chemical barriers to counteract herbivores. However, over 75% of herbivorous insect species are well adapted to their host's defenses and these specialists are generally difficult to ward off. By actively antagonizing the number of insect eggs deposited on plants, future damage by the herbivore's offspring can be limited. Therefore, it is important to understand which plant traits influence attractiveness for oviposition, especially for specialist insects that are well adapted to their host plants. In this study, we investigated the oviposition preference of Pieris butterflies (Lepidoptera: Pieridae) by offering them the choice between 350 different naturally occurring Arabidopsis accessions. Using a genome-wide association study of the oviposition data and subsequent fine mapping with full genome sequences of 164 accessions, we identified WRKY42 and AOC1 as candidate genes that are associated with the oviposition preference observed for Pieris butterflies. Host plant choice assays with Arabidopsis genotypes impaired in WRKY42 or AOC1 function confirmed a clear role for WRKY42 in oviposition preference of female Pieris butterflies, while for AOC1 the effect was mild. In contrast, WRKY42-impaired plants, which were preferred for oviposition by butterflies, negatively impacted offspring performance. These findings exemplify that plant genotype can have opposite effects on oviposition preference and caterpillar performance. This knowledge can be used for breeding trap crops or crops that are unattractive for oviposition by pest insects.

Characterizing Fractal Genetic Variation in the Human Genome from the Hapmap Project.

Over the last decades, the exuberant development of next-generation sequencing has revolutionized gene discovery. These technologies have boosted the mapping of single nucleotide polymorphisms (SNPs) across the human genome, providing a complex universe of heterogeneity characterizing individuals worldwide. Fractal dimension (FD) measures the degree of geometric irregularity, quantifying how "complex" a self-similar natural phenomenon is. We compared two FD algorithms, box-counting dimension (BCD) and Higuchi's fractal dimension (HFD), to characterize genome-wide patterns of SNPs extracted from the HapMap data set, which includes data from 1184 healthy subjects of eleven populations. In addition, we have used cluster and classification analysis to relate the genetic distances within chromosomes based on FD similarities to the geographical distances among the 11 global populations. We found that HFD outperformed BCD at both grand average clusterization analysis by the cophenetic correlation coefficient, in which the closest value to 1 represents the most accurate clustering solution (0.981 for the HFD and 0.956 for the BCD) and classification (79.0% accuracy, 61.7% sensitivity, and 96.4% specificity for the HFD with respect to 69.1% accuracy, 43.2% sensitivity, and 94.9% specificity for the BCD) of the 11 populations present in the HapMap data set. These results support the evidence that HFD is a reliable measure helpful in representing individual variations within all chromosomes and categorizing individuals and global populations.

The Ser19Stop single nucleotide polymorphism (SNP) of human PHYHIPL affects the cerebellum in mice.

The HapMap Project is a major international research effort to construct a resource to facilitate the discovery of relationships between human genetic variations and health and disease. The Ser19Stop single nucleotide polymorphism (SNP) of human phytanoyl-CoA hydroxylase-interacting protein-like (PHYHIPL) gene was detected in HapMap project and registered in the dbSNP. PHYHIPL gene expression is altered in global ischemia and glioblastoma multiforme. However, the function of PHYHIPL is unknown. We generated PHYHIPL Ser19Stop knock-in mice and found that PHYHIPL impacts the morphology of cerebellar Purkinje cells (PCs), the innervation of climbing fibers to PCs, the inhibitory inputs to PCs from molecular layer interneurons, and motor learning ability. Thus, the Ser19Stop SNP of the PHYHIPL gene may be associated with cerebellum-related diseases.

Distinguishing HapMap Accessions Through Recursive Set Partitioning in Hierarchical Decision Trees.

The HapMap (haplotype map) projects have produced valuable genetic resources in life science research communities, allowing researchers to investigate sequence variations and conduct genome-wide association study (GWAS) analyses. A typical HapMap project may require sequencing hundreds, even thousands, of individual lines or accessions within a species. Due to limitations in current sequencing technology, the genotype values for some accessions cannot be clearly called. Additionally, allelic heterozygosity can be very high in some lines, causing genetic and sometimes phenotypic segregation in their descendants. Genetic and phenotypic segregation degrades the original accession's specificity and makes it difficult to distinguish one accession from another. Therefore, it is vitally important to determine and validate HapMap accessions before one conducts a GWAS analysis. However, to the best of our knowledge, there are no prior methodologies or tools that can readily distinguish or validate multiple accessions in a HapMap population. We devised a bioinformatics approach to distinguish multiple HapMap accessions using only a minimum number of genetic markers. First, we assign each candidate marker with a distinguishing score (DS), which measures its capability in distinguishing accessions. The DS score prioritizes those markers with higher percentages of homozygous genotypes (allele combinations), as they can be stably passed on to offspring. Next, we apply the "set-partitioning" concept to select optimal markers by recursively partitioning accession sets. Subsequently, we build a hierarchical decision tree in which a specific path represents the selected markers and the homogenous genotypes that can be used to distinguish one accession from others in the HapMap population. Based on these algorithms, we developed a web tool named MAD-HiDTree (Multiple Accession Distinguishment-Hierarchical Decision Tree), designed to analyze a user-input genotype matrix and construct a hierarchical decision tree. Using genetic marker data extracted from the Medicago truncatula HapMap population, we successfully constructed hierarchical decision trees by which the original 262 M. truncatula accessions could be efficiently distinguished. PCR experiments verified our proposed method, confirming that MAD-HiDTree can be used for the identification of a specific accession. MAD-HiDTree was developed in C/C++ in Linux. Both the source code and test data are publicly available at https://bioinfo.noble.org/MAD-HiDTree/.

Identification of miRNA-eQTLs in maize mature leaf by GWAS.

BACKGROUND: MiRNAs play essential roles in plant development and response to biotic and abiotic stresses through interaction with their target genes. The expression level of miRNAs shows great variations among different plant accessions, developmental stages, and tissues. Little is known about the content within the plant genome contributing to the variations in plants. This study aims to identify miRNA expression-related quantitative trait loci (miR-QTLs) in the maize genome. RESULTS: The miRNA expression level from next generation sequencing (NGS) small RNA libraries derived from mature leaf samples of the maize panel (200 maize lines) was estimated as phenotypes, and maize Hapmap v3.2.1 was chosen as the genotype for the genome-wide association study (GWAS). A total of four significant miR-eQTLs were identified contributing to miR156k-5p, miR159a-3p, miR390a-5p and miR396e-5p, and all of them are trans-eQTLs. In addition, a strong positive coexpression of miRNA was found among five miRNA families. Investigation of the effects of these miRNAs on the expression levels and target genes provided evidence that miRNAs control the expression of their targets by suppression and enhancement. CONCLUSIONS: These identified significant miR-eQTLs contribute to the diversity of miRNA expression in the maize penal at the developmental stages of mature leaves in maize, and the positive and negative regulation between miRNA and its target genes has also been uncovered.

SR4R: An Integrative SNP Resource for Genomic Breeding and Population Research in Rice.

The information commons for rice (IC4R) database is a collection of 18 million single nucleotide polymorphisms (SNPs) identified by resequencing of 5152 rice accessions. Although IC4R offers ultra-high density rice variation map, these raw SNPs are not readily usable for the public. To satisfy different research utilizations of SNPs for population genetics, evolutionary analysis, association studies, and genomic breeding in rice, raw genotypic data of these 18 million SNPs were processed by unified bioinformatics pipelines. The outcomes were used to develop a daughter database of IC4R - SnpReady for Rice (SR4R). SR4R presents four reference SNP panels, including 2,097,405 hapmapSNPs after data filtration and genotype imputation, 156,502 tagSNPs selected from linkage disequilibrium-based redundancy removal, 1180 fixedSNPs selected from genes exhibiting selective sweep signatures, and 38 barcodeSNPs selected from DNA fingerprinting simulation. SR4R thus offers a highly efficient rice variation map that combines reduced SNP redundancy with extensive data describing the genetic diversity of rice populations. In addition, SR4R provides rice researchers with a web interface that enables them to browse all four SNP panels, use online toolkits, as well as retrieve the original data and scripts for a variety of population genetics analyses on local computers. SR4R is freely available to academic users at http://sr4r.ic4r.org/.

Long walk to genomics: History and current approaches to genome sequencing and assembly.

Genomes represent the starting point of genetic studies. Since the discovery of DNA structure, scientists have devoted great efforts to determine their sequence in an exact way. In this review we provide a comprehensive historical background of the improvements in DNA sequencing technologies that have accompanied the major milestones in genome sequencing and assembly, ranging from early sequencing methods to Next-Generation Sequencing platforms. We then focus on the advantages and challenges of the current technologies and approaches, collectively known as Third Generation Sequencing. As these technical advancements have been accompanied by progress in analytical methods, we also review the bioinformatic tools currently employed in de novo genome assembly, as well as some applications of Third Generation Sequencing technologies and high-quality reference genomes.

LD scores are associated with differences in allele frequencies between populations but LD score regression can still distinguish confounding from polygenicity.

The linkage disequilibrium (LD) score regression method tests whether there is an association between the LD score and allele frequency differences between cases and controls. It makes the assumption that there is no association between LD score and allele frequency differences among populations and hence that any observed association is the result of a polygenic effect rather than population stratification. This assumption was previously tested only using European cohorts. In comparisons among more diverse HapMap populations, we observe that there is indeed an association between the LD score and allele frequency differences. However, this effect is small and when we carry out simulations of large case-control samples, the effect becomes negligible. We conclude that if the intercept is small then any increase in mean chi-squared does indeed reflect a polygenic effect rather than population stratification.

Unraveling the genetic diversity of Belgian Milk Sheep using medium-density SNP genotypes.

The present study focuses on the Belgian Milk Sheep in Flanders (Belgium) and compares its genetic diversity and relationship with the Flemish Sheep, the Friesian Milk Sheep, the French Lacaune dairy sheep and other Northern European breeds. For this study, 94 Belgian Milk Sheep, 23 Flemish Sheep and 22 Friesian Milk Sheep were genotyped with the OvineSNP50 array. In addition, 29 unregistered animals phenotypically similar to Belgian Milk Sheep were genotyped using the 15K ISGC chip. Both Belgian and Friesian Milk Sheep as well as the East Friesian Sheep were found to be less diverse than the other seven breeds included in this study. Genomic inbreeding coefficients based on runs of homozygosity (ROH) were estimated at 14.5, 12.4 and 10.2% for Belgian Milk Sheep, Flemish Sheep and Friesian Milk Sheep respectively. Out of 29 unregistered Belgian Milk Sheep, 28 mapped in the registered Belgian Milk Sheep population. Ancestry analysis, PCA and FST calculations showed that Belgian Milk Sheep are more related to Friesian Milk Sheep than to Flemish Sheep, which was contrary to the breeders' expectations. Consequently, breeders may prefer to crossbreed Belgian Milk Sheep with Friesian sheep populations (Friesian Milk Sheep or East Friesian Sheep) in order to increase diversity. This research underlines the usefulness of SNP chip genotyping and ROH analyses for monitoring genetic diversity and studying genetic links in small livestock populations, profiting from internationally available genotypes. As assessment of genetic diversity is vital for long-term breed survival, these results will aid flockbooks to preserve genetic diversity.

Genetic variability analysis in a population from Bogotá: Towards a haplotype map / Análisis de la variabilidad genética de una muestra de la población de Bogotá: hacia la constitución de un mapa de haplotipos

Introduction: The HapMap and the 1000 Genomes projects have been important for understanding the genetic component of common diseases and normal phenotypes. However, the Colombian genetic variability included in these projects is not fully representative of our country. Objective: To contribute to the knowledge of the Colombian genetic variability through the genomic study of a sample of individuals from Bogotá. Materials and methods: A total of 2,372,784 genetic markers were genotyped in 32 individuals born in Bogotá whose parents are from the same region, using the Illumina™ platform. The genetic variability levels were determined and compared with the data available from other populations of the 1000 Genomes Project. Results: The genetic variability detected in the individuals from Bogotá was similar to those with shared ancestry. However, despite the low levels of genetic differentiation between Bogotá and Medellín, populations the principal component analysis suggested a different genetic composition in them. Conclusions: Our genomic analysis of a Bogotá sample allowed us to detect similarities and differences with other American populations. The increase of the Bogotá sample and the inclusion of samples from other regions of the country will improve our understanding of the genetic variability in Colombia, essential for studies of human health and the prevention and treatment of common diseases in our country.

Introducción. Los proyectos del mapa de haplotipos (HapMap) y de los 1.000 genomas han sido fundamentales para la compresión del componente genético de las enfermedades comunes y los fenotipos normales. Sin embargo, la variabilidad genética colombiana incluida en estos proyectos no es representativa del país. Objetivo. Contribuir al conocimiento de la variabilidad genética de la población colombiana a partir del estudio genómico de una muestra de individuos de Bogotá. Materiales y métodos. Se genotipificaron 2'372.784 marcadores genéticos de 32 individuos nacidos en Bogotá y de padres originarios de la misma ciudad utilizando la plataforma Illumina™. Los niveles de variabilidad genética se determinaron y se compararon con los datos disponibles de otras poblaciones del proyecto de los 1.000 genomas. Resultados. Los individuos analizados presentaron una variabilidad genética semejante a la de poblaciones con las que comparten ancestros. No obstante, a pesar de la poca diferenciación genética detectada en la población de Bogotá y en la de Medellín, el análisis de los componentes principales sugiere una composición genética diferente en las dos poblaciones. Conclusiones. El análisis genómico de la muestra de Bogotá permitió detectar similitudes y diferencias con otras poblaciones americanas. El aumento de tamaño de la muestra bogotana y la inclusión de muestras de otras regiones del país permitirán una mejor compresión de la variabilidad genética en Colombia, lo cual es fundamental para los estudios de salud humana, y la prevención y el tratamiento de enfermedades comunes en el país.

Análisis de la variabilidad genética de una muestra de la población de Bogotá: hacia la constitución de un mapa de haplotipos / Genetic variability analysis in a population from Bogotá: Towards a haplotype map

Resumen Introducción. Los proyectos del mapa de haplotipos (HapMap) y de los 1.000 genomas han sido fundamentales para la compresión del componente genético de las enfermedades comunes y los fenotipos normales. Sin embargo, la variabilidad genética colombiana incluida en estos proyectos no es representativa del país. Objetivo. Contribuir al conocimiento de la variabilidad genética de la población colombiana a partir del estudio genómico de una muestra de individuos de Bogotá. Materiales y métodos. Se genotipificaron 2'372.784 marcadores genéticos de 32 individuos nacidos en Bogotá y de padres originarios de la misma ciudad utilizando la plataforma Illumina™. Los niveles de variabilidad genética se determinaron y se compararon con los datos disponibles de otras poblaciones del proyecto de los 1.000 genomas. Resultados. Los individuos analizados presentaron una variabilidad genética semejante a la de poblaciones con las que comparten ancestros. No obstante, a pesar de la poca diferenciación genética detectada en la población de Bogotá y en la de Medellín, el análisis de los componentes principales sugiere una composición genética diferente en las dos poblaciones. Conclusiones. El análisis genómico de la muestra de Bogotá permitió detectar similitudes y diferencias con otras poblaciones americanas. El aumento de tamaño de la muestra bogotana y la inclusión de muestras de otras regiones del país permitirán una mejor compresión de la variabilidad genética en Colombia, lo cual es fundamental para los estudios de salud humana, y la prevención y el tratamiento de enfermedades comunes en el país.

Abstract Introduction: The HapMap and the 1000 Genomes projects have been important for understanding the genetic component of common diseases and normal phenotypes. However, the Colombian genetic variability included in these projects is not fully representative of our country. Objective: To contribute to the knowledge of the Colombian genetic variability through the genomic study of a sample of individuals from Bogotá. Materials and methods: A total of 2,372,784 genetic markers were genotyped in 32 individuals born in Bogotá whose parents are from the same region, using the Illumina™ platform. The genetic variability levels were determined and compared with the data available from other populations of the 1000 Genomes Project. Results: The genetic variability detected in the individuals from Bogotá was similar to those with shared ancestry. However, despite the low levels of genetic differentiation between Bogotá and Medellín, populations the principal component analysis suggested a different genetic composition in them. Conclusions: Our genomic analysis of a Bogotá sample allowed us to detect similarities and differences with other American populations. The increase of the Bogotá sample and the inclusion of samples from other regions of the country will improve our understanding of the genetic variability in Colombia, essential for studies of human health and the prevention and treatment of common diseases in our country.

Genotype Imputation in Genome-Wide Association Studies.

Genotype imputation infers missing genotypes in silico using haplotype information from reference samples with genotypes from denser genotyping arrays or sequencing. This approach can confer a number of improvements on genome-wide association studies: it can improve statistical power to detect associations by reducing the number of missing genotypes; it can simplify data harmonization for meta-analyses by improving overlap of genomic variants between differently-genotyped sample sets; and it can increase the overall number and density of genomic variants available for association testing. This article reviews the general concepts behind imputation, describes imputation approaches and methods for various types of genotype data, including family-based data, and identifies web-based resources that can be used in different steps of the imputation process. For practical application, it provides a step-by-step guide to implementation of a two-step imputation process consisting of phasing of the study genotypes and the imputation of reference panel genotypes into the study haplotypes. In addition, this review describes recently developed haplotype reference panel resources and online imputation servers that are capable of remotely and securely implementing an imputation workflow on uploaded genotype array data. © 2019 by John Wiley & Sons, Inc.

Gigwa v2-Extended and improved genotype investigator.

BACKGROUND: The study of genetic variations is the basis of many research domains in biology. From genome structure to population dynamics, many applications involve the use of genetic variants. The advent of next-generation sequencing technologies led to such a flood of data that the daily work of scientists is often more focused on data management than data analysis. This mass of genotyping data poses several computational challenges in terms of storage, search, sharing, analysis, and visualization. While existing tools try to solve these challenges, few of them offer a comprehensive and scalable solution. RESULTS: Gigwa v2 is an easy-to-use, species-agnostic web application for managing and exploring high-density genotyping data. It can handle multiple databases and may be installed on a local computer or deployed as an online data portal. It supports various standard import and export formats, provides advanced filtering options, and offers means to visualize density charts or push selected data into various stand-alone or online tools. It implements 2 standard RESTful application programming interfaces, GA4GH, which is health-oriented, and BrAPI, which is breeding-oriented, thus offering wide possibilities of interaction with third-party applications. The project home page provides a list of live instances allowing users to test the system on public data (or reasonably sized user-provided data). CONCLUSIONS: This new version of Gigwa provides a more intuitive and more powerful way to explore large amounts of genotyping data by offering a scalable solution to search for genotype patterns, functional annotations, or more complex filtering. Furthermore, its user-friendliness and interoperability make it widely accessible to the life science community.

HapMap-based study: CYP2A13 may be a potential key metabolic enzyme gene in the carcinogenesis of lung cancer in non-smokers.

BACKGROUND: The aim of this study was to evaluate the association between CYP2A13 polymorphisms and lung cancer susceptibility using the HapMap database. METHODS: A case-control analysis of 532 subjects with lung cancer and 614 controls with no personal history of the disease was performed. The tag SNPs rs1645690 and rs8192789 for CYP2A13 were selected, and the genetic polymorphisms were confirmed experimentally through real-time PCR, cloning, and sequencing assay. RESULTS: SNP frequency in this study was consistent with the HapMap Project database of Han-Chinese and lung cancer risk was associated with CYP2A13 polymorphisms in non-smokers. CYP2A13 shares a 93.5% identity with CYP2A6 in the amino acid sequence and the homologous sequences may interfere with the study of SNPs of CYP2A13. CONCLUSIONS: CYP2A13 may be a potential key metabolic enzyme gene in the carcinogenesis of lung cancer in non-smokers. The common polymorphisms of CYP2A13 may be candidate biomarkers for lung cancer susceptibility in Han-Chinese.

Comparison of variation in frequency for SNPs associated with asthma or liver disease between Estonia, HapMap populations and the 1000 genome project populations.

Allele-specific analyses to understand frequency differences across populations, particularly populations not well studied, are important to help identify variants that may have a functional effect on disease mechanisms and phenotypic predisposition, facilitating new Genome-Wide Association Studies (GWAS). We aimed to compare the allele frequency of 11 asthma-associated and 16 liver disease-associated single nucleotide polymorphisms (SNPs) between the Estonian, HapMap and 1000 genome project populations. When comparing EGCUT with HapMap populations, the largest difference in allele frequencies was observed with the Maasai population in Kinyawa, Kenya, with 12 SNP variants reporting statistical significance. Similarly, when comparing EGCUT with 1000 genomes project populations, the largest difference in allele frequencies was observed with pooled African populations with 22 SNP variants reporting statistical significance. For 11 asthma-associated and 16 liver disease-associated SNPs, Estonians are genetically similar to other European populations but significantly different from African populations. Understanding differences in genetic architecture between ethnic populations is important to facilitate new GWAS targeted at underserved ethnic groups to enable novel genetic findings to aid the development of new therapies to reduce morbidity and mortality.

Genotype Imputation in Winter Wheat Using First-Generation Haplotype Map SNPs Improves Genome-Wide Association Mapping and Genomic Prediction of Traits.

Genome-wide single nucleotide polymorphism (SNP) variation allows for the capture of haplotype structure in populations and prediction of unobserved genotypes based on inferred regions of identity-by-descent (IBD). Here we have used a first-generation wheat haplotype map created by targeted re-sequencing of low-copy genomic regions in the reference panel of 62 lines to impute marker genotypes in a diverse panel of winter wheat cultivars from the U.S. Great Plains. The IBD segments between the reference population and winter wheat cultivars were identified based on SNP genotyped using the 90K iSelect wheat array and genotyping by sequencing (GBS). A genome-wide association study and genomic prediction of resistance to stripe rust in winter wheat cultivars showed that an increase in marker density achieved by imputation improved both the power and precision of trait mapping and prediction. The majority of the most significant marker-trait associations belonged to imputed genotypes. With the vast amount of SNP variation data accumulated for wheat in recent years, the presented imputation framework will greatly improve prediction accuracy in breeding populations and increase resolution of trait mapping hence, facilitate cross-referencing of genotype datasets available across different wheat populations.

Using variant databases for variant prioritization and to detect erroneous genotype-phenotype associations.

BACKGROUND: In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics. RESULTS: Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases. CONCLUSIONS: We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided.

Polymorphisms of cancer-related genes and risk of multipleprimary malignancies involving colorectal cancer

Background/aim: This study aimed to investigate the relationship between single nucleotide polymorphisms (SNPs) of cancer-related genes and the risk of multiple primary malignancies involving colorectal cancer.Materials and methods: We collected tissue samples from 22 multiple primary cancer patients with primary colorectal cancer and performed genotyping assay for 116 SNP loci from 62 genes encoding peptides functioning in various signaling pathways using the DNA MassARRAY system. The chi-square test was used to compare the differences in base frequencies between patients and a control Chinese population from HapMap through the NCBI database.Results: No significant differences in frequencies were detected for 81 SNPs (P > 0.05), while serious frequency differences were observed for 35 SNPs from 31 genes (P < 0.05), which included ERCC6 (rs2228526), ERCC1 (rs3212986), CASP8 (rs3834129, rs3769818), and others presented. Five of these SNPs were previously reported to be associated with the pathogenesis of colorectal cancer.Conclusion: The 35 SNPs from 31 genes may be associated with the risk of multiple primary malignancies involving colorectal cancer.

Limitations of lymphoblastoid cell lines for functional analysis of SNPs.

OBJECTIVE: Single nucleotide polymorphisms (SNPs) are widely linked to the susceptibility and penetrance of diseases. SNP rs886205 (A/G) located in the aldehyde dehydrogenase 2 (ALDH2) promoter is associated with esophageal carcinoma in alcohol-dependent patients. Previously, we found an interaction of the SNP with the methylation of promoter regions as well as the protein levels of ALDH2 in alcohol-dependent patients. To study the DNA-protein interactions involved in rs886205 mediated regulation of ALDH2, we chose lymphoblastoid cell lines harboring AA/GA/GG genotype and acquired two for each genotype from National Human Genome Research Institute repository. We measured the promoter methylation of ALDH2 by using bisulfite sequencing and quantified protein expression of ALDH2 by western blot to compare the cell lines with the previous findings in patients. RESULTS: DNA methylation showed significant differences not only based on genotype but also due to the different background of the cells owing to their origin from different individuals. Although ALDH2 protein expression seemed to be driven by the rs886205 genotype, results were not in consensus with data from the patient cohorts. Our findings show the limitations of the usage of lymphoblastoid cell lines due to the unavoidable background genetic differences that may influence the effect of SNP.