Single Nucleotide Polymorphisms In Non-coding Regions Research Articles

Single Nucleotide Polymorphisms (SNPs) in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer.

Esophageal cancer is a complex disease influenced by genetic and environmental factors. Single nucleotide polymorphisms (SNPs) in non-coding regions of the genome have emerged as crucial contributors to esophageal cancer susceptibility. This review provides a comprehensive overview of the role of SNPs in non-coding regions and their association with esophageal cancer. The accumulation of SNPs in the genome has been implicated in esophageal cancer risk. Various studies have identified specific locations in the genome where SNPs are more likely to occur, suggesting a location-specific response. Chromatin conformational studies have shed light on the localization of SNPs and their impact on gene transcription, posttranscriptional modifications, gene expression regulation, and histone modification. Furthermore, miRNA-related SNPs have been found to play a significant role in esophageal squamous cell carcinoma (ESCC). These SNPs can affect miRNA binding sites, thereby altering target gene regulation and contributing to ESCC development. Additionally, the risk of ESCC has been linked to base excision repair, suggesting that SNPs in this pathway may influence disease susceptibility. Somatic DNA segment alterations and modified expression quantitative trait loci (eQTL) have also been associated with ESCC. These alterations can lead to disrupted gene expression and cellular processes, ultimately contributing to cancer development and progression. Moreover, SNPs have been found to be associated with the long non-coding RNA HOTAIR, which plays a crucial role in ESCC pathogenesis. This review concludes with a discussion of the current and future perspectives in the field of SNPs in non-coding regions and their relevance to esophageal cancer. Understanding the functional implications of these SNPs may lead to the identification of novel therapeutic targets and the development of personalized approaches for esophageal cancer prevention and treatment.

A systematic strategy for identifying causal single nucleotide polymorphisms and their target genes on Juvenile arthritis risk haplotypes

BackgroundAlthough genome-wide association studies (GWAS) have identified multiple regions conferring genetic risk for juvenile idiopathic arthritis (JIA), we are still faced with the task of identifying the single nucleotide polymorphisms (SNPs) on the disease haplotypes that exert the biological effects that confer risk. Until we identify the risk-driving variants, identifying the genes influenced by these variants, and therefore translating genetic information to improved clinical care, will remain an insurmountable task. We used a function-based approach for identifying causal variant candidates and the target genes on JIA risk haplotypes.MethodsWe used a massively parallel reporter assay (MPRA) in myeloid K562 cells to query the effects of 5,226 SNPs in non-coding regions on JIA risk haplotypes for their ability to alter gene expression when compared to the common allele. The assay relies on 180 bp oligonucleotide reporters (“oligos”) in which the allele of interest is flanked by its cognate genomic sequence. Barcodes were added randomly by PCR to each oligo to achieve > 20 barcodes per oligo to provide a quantitative read-out of gene expression for each allele. Assays were performed in both unstimulated K562 cells and cells stimulated overnight with interferon gamma (IFNg). As proof of concept, we then used CRISPRi to demonstrate the feasibility of identifying the genes regulated by enhancers harboring expression-altering SNPs.ResultsWe identified 553 expression-altering SNPs in unstimulated K562 cells and an additional 490 in cells stimulated with IFNg. We further filtered the SNPs to identify those plausibly situated within functional chromatin, using open chromatin and H3K27ac ChIPseq peaks in unstimulated cells and open chromatin plus H3K4me1 in stimulated cells. These procedures yielded 42 unique SNPs (total = 84) for each set. Using CRISPRi, we demonstrated that enhancers harboring MPRA-screened variants in the TRAF1 and LNPEP/ERAP2 loci regulated multiple genes, suggesting complex influences of disease-driving variants.ConclusionUsing MPRA and CRISPRi, JIA risk haplotypes can be queried to identify plausible candidates for disease-driving variants. Once these candidate variants are identified, target genes can be identified using CRISPRi informed by the 3D chromatin structures that encompass the risk haplotypes.

Machine Learning Study of SNPs in Noncoding Regions to Predict Non-small Cell Lung Cancer Susceptibility

Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (<3.02e-6). Notably, rs80040741 and rs6010259 were annotated from the intron regions of MUC3A and MLC1, respectively. Together with another five SNPs previously reported in Chinese NSCLC patients and another four covariates (e.g., smoking status, age, low dose CT screening, sex), a predictive model by machine learning methods can separate the NSCLC from healthy controls with an accuracy of 86%. This is the first time to apply machine learning method in predicting the NSCLC susceptibility using both genetic and clinical characteristics. Our findings will provide a promising method in NSCLC early diagnosis and improve our understanding of applying machine learning methods in precision medicine.

MP25-04 IDENTIFYING METABOLIC AND GENOMIC SIGNATURES AS BIOMARKERS FOR UROPATHOGENIC E COLI AND ASYMPTOMATIC BACTERIURIA

You have accessJournal of UrologyInfections/Inflammation/Cystic Disease of the Genitourinary Tract: Kidney & Bladder I (MP25)1 Sep 2021MP25-04 IDENTIFYING METABOLIC AND GENOMIC SIGNATURES AS BIOMARKERS FOR UROPATHOGENIC E COLI AND ASYMPTOMATIC BACTERIURIA Benjamin Abelson, Grace Morales, Gerald Van Horn, Allison R Eberly, Douglass B Clayton, Jonathan E Schmitz, and Maria Hadjifrangiskou Benjamin AbelsonBenjamin Abelson More articles by this author , Grace MoralesGrace Morales More articles by this author , Gerald Van HornGerald Van Horn More articles by this author , Allison R EberlyAllison R Eberly More articles by this author , Douglass B ClaytonDouglass B Clayton More articles by this author , Jonathan E SchmitzJonathan E Schmitz More articles by this author , and Maria HadjifrangiskouMaria Hadjifrangiskou More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002022.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Uropathogenic Escherichia coli (UPEC) is the most common cause of cystitis, though the presence of E. coli in urine does not universally cause symptoms. We aim to identify the metabolic and genomic signatures of E. coli isolates that distinguish cystitis-causing strains from E. coli strains present in asymptomatic bacteriuria (ASB). METHODS: We used a biobank to identify E. coli isolates from adult patients. The cohort was divided into cystitis strains and asymptomatic bacteriuria strains based on medical record review. A subset of these isolates were grown in pooled human urine under hypoxic conditions and then subjected to global metabolomics. For genomic sequencing, isolates were grown overnight in lysogeny broth, samples pelleted then subjected to genomic DNA isolation, whole-genome sequencing and analysis. RESULTS: Pooled metabolomic analysis of a subset of 26 ASB and 77 cystitis isolates demonstrates lower levels of guanosine but higher levels of adenosine in the supernatant of asymptomatic bacteriuria E. coli isolates. Given that both metabolites are implicated in purine metabolism, this supplements previously published work from our group demonstrating that purine biosynthesis is necessary for uropathogenic E. coli survival with bladder urothelial cells. 138 isolates were processed for genomic analysis. This included isolates from 126 women (91.6%) and 68 (49.3%) cystitis strains. Our development of a reproducible pipeline for urinary bacterial isolate whole genome sequencing and gene assembly demonstrates the feasibility for developing continued translational analysis to examine clinical specimens for genomic signatures. Genomic analysis of known purine synthesis-related genes did demonstrate single nucleotide polymorphisms (SNPs) that may alter single amino acids, but these results did not cluster based on the clinical presentation of the isolate source. CONCLUSIONS: UPEC differ from strains isolated from patients with asymptomatic bacteria in terms of the levels of purine synthesis metabolites, though examination of known purine synthesis gene SNPs does not reveal clustering based on clinical data. Further work using genome wide association studies to identify SNPs in non-coding regions along with additional metagenomics studies will be needed to identify further metabolic and genomic signatures that differentiate uropathogenic E. coli from asymptomatic strains. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e454-e455 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Benjamin Abelson More articles by this author Grace Morales More articles by this author Gerald Van Horn More articles by this author Allison R Eberly More articles by this author Douglass B Clayton More articles by this author Jonathan E Schmitz More articles by this author Maria Hadjifrangiskou More articles by this author Expand All Advertisement Loading ...

Crosstalk of Genetic Variants, Allele-Specific DNA Methylation, and Environmental Factors for Complex Disease Risk.

Over the past decades, genome-wide association studies (GWAS) have identified thousands of phenotype-associated DNA sequence variants for potential explanations of inter-individual phenotypic differences and disease susceptibility. However, it remains a challenge for translating the associations into causative mechanisms for complex diseases, partially due to the involved variants in the noncoding regions and the inconvenience of functional studies in human population samples. So far, accumulating evidence has suggested a complex crosstalk among genetic variants, allele-specific binding of transcription factors (ABTF), and allele-specific DNA methylation patterns (ASM), as well as environmental factors for disease risk. This review aims to summarize the current studies regarding the interactions of the aforementioned factors with a focus on epigenetic insights. We present two scenarios of single nucleotide polymorphisms (SNPs) in coding regions and non-coding regions for disease risk, via potentially impacting epigenetic patterns. While a SNP in a coding region may confer disease risk via altering protein functions, a SNP in non-coding region may cause diseases, via SNP-altering ABTF, ASM, and allele-specific gene expression (ASE). The allelic increases or decreases of gene expression are key for disease risk during development. Such ASE can be achieved via either a “SNP-introduced ABTF to ASM” or a “SNP-introduced ASM to ABTF.” Together with our additional in-depth review on insulator CTCF, we are convinced to propose a working model that the small effect of a SNP acts through altered ABTF and/or ASM, for ASE and eventual disease outcome (named as a “SNP intensifier” model). In summary, the significance of complex crosstalk among genetic factors, epigenetic patterns, and environmental factors requires further investigations for disease susceptibility.

Application of whole genome sequence data in analyzing the molecular epidemiology of Shiga toxin-producing Escherichia coli O157:H7/H-

Seventeen clusters of Shiga toxin-producing Escherichia coli O157:H7/- (O157) strains, determined by cluster analysis of pulsed-field gel electrophoresis patterns, were analyzed using whole genome sequence (WGS) data to investigate this pathogen's molecular epidemiology. The 17 clusters included 136 strains containing strains from nine outbreaks, with each outbreak caused by a single source contaminated with the organism, as shown by epidemiological contact surveys. WGS data of these strains were used to identify single nucleotide polymorphisms (SNPs) by two methods: short read data were directly mapped to a reference genome (mapping derived SNPs) and common SNPs between the mapping derived SNPs and SNPs in assembled data of short read data (common SNPs). Among both SNPs, those that were detected in genes with a gap were excluded to remove ambiguous SNPs from further analysis. The effectiveness of both SNPs was investigated among all the concatenated SNPs that were detected (whole SNP set); SNPs were divided into three categories based on the genes in which they were located (i.e., backbone SNP set, O-island SNP set, and mobile element SNP set); and SNPs in non-coding regions (intergenic region SNP set). When SNPs from strains isolated from the nine single source derived outbreaks were analyzed using an unweighted pair group method with arithmetic mean tree (UPGMA) and a minimum spanning tree (MST), the maximum pair-wise distances of the backbone SNP set of the mapping derived SNPs were significantly smaller than those of the whole and intergenic region SNP set on both UPGMAs and MSTs. This significant difference was also observed when the backbone SNP set of the common SNPs were examined (Steel-Dwass test, P≤0.01). When the maximum pair-wise distances were compared between the mapping derived and common SNPs, significant differences were observed in those of the whole, mobile element, and intergenic region SNP set (Wilcoxon signed rank test, P≤0.01). When all the strains included in one complex on an MST or one cluster on a UPGMA were designated as the same genotype, the values of the Hunter-Gaston Discriminatory Power Index for the backbone SNP set of the mapping derived and common SNPs were higher than those of other SNP sets. In contrast, the mobile element SNP set could not robustly subdivide lineage I strains of tested O157 strains using both the mapping derived and common SNPs. These results suggested that the backbone SNP set were the most effective for analysis of WGS data for O157 in enabling an appropriation of its molecular epidemiology.

Diversity of the Photosynthetic Paulinella Species, with the Description of Paulinella micropora sp. nov. and the Chromatophore Genome Sequence for strain KR01

The thecate filose amoeba Paulinella chromatophora is a good model organism for understanding plastid organellogenesis because its chromatophore was newly derived from an alpha-cyanobacterium. Paulinella chromatophora was the only known photosynthetic Paulinella species until recent studies that suggested a species level of diversity. Here, we described a new photosynthetic species P. micropora sp. nov. based on morphological and molecular evidence from a newly established strain KR01. The chromatophore genome of P. micropora KR01 was fully determined; the genome was 976,991bp in length, the GC content was 39.9%, and 908 genes were annotated. A pairwise comparison of chromatophore genome sequences between strains KR01 and FK01, representing two different natural populations of P. micropora, showed a 99.85% similarity. Differences between the two strains included single nucleotide polymorphisms (SNPs) in CDSs, which resulted in 357 synonymous and 280 nonsynonymous changes, along with 245 SNPs in non-coding regions. Indels (37) and microinversions (14) were also detected. Species diversity for photosynthetic Paulinella was surveyed using samples collected from around the world. We compared our new species to two photosynthetic species, P. chromatophora and P. longichromatophora. Phylogenetic analyses using four gene markers revealed three distinct lineages of photosynthetic Paulinella species including P. micropora sp. nov.

Milk protein polymorphisms and casein haplotypes in Butana cattle.

Butana is a Bos indicus dairy cattle breed that is well adapted to the local environment of Sudan. The breed has been gradually declining in number due to breed substitution. Therefore, conservation and improvement strategies are required to maintain this breed. The aim of the present study was to assess genetic variation that is characteristic for Butana cattle in the milk protein genes CSN1S1, CSN2, CSN1S2, CSN3, LALBA, and LGB. In a first step, genomic DNA of five unrelated individuals was comparatively sequenced across all exon and flanking sequences. Ninety-three single nucleotide polymorphisms (SNPs) were identified in Butana cattle compared with the Bos taurus reference sequence at Ensembl. We confirmed the recently identified protein variants CSN2*J, CSN2*L, and LALBA*E. Fifty-two SNPs in non-coding regions are novel. Among the novel SNPs, five are located in promoter regions, three of them are in putative transcription factor binding sites (TFBSs) of the CSN1S2 promoter. Fifteen SNPs potentially affect miRNA target sites. In a second step, 50 unrelated Butana cattle were genotyped. This allowed deriving haplotypes for the casein gene cluster on BTA6. The most frequent haplotype was CSN1S1*C-CSN2*A 2 -CSN1S2*A-CSN3*A (C-A 2 -A-A, frequency 0.1546). Considering the newly identified CSN1S2 promoter variants, the most frequent haplotype was C-A 2 -TTC-A-A (0.1046), with TTC as the promoter variant. The information on protein and promoter variants can be used for the development of conservation and breeding strategies for this local breed.

CCSI: a database providing chromatin-chromatin spatial interaction information.

Distal regulatory elements have been shown to regulate gene transcription through spatial interactions, and single nucleotide polymorphisms (SNPs) are linked with distal gene expression by spatial proximity, which helps to explain the causal role of disease-associated SNPs in non-coding region. Therefore, studies on spatial interactions between chromatin have created a new avenue for elucidating the mechanism of transcriptional regulation in disease pathogenesis. Recently, a growing number of chromatin interactions have been revealed by means of 3C, 4C, 5C, ChIA-PET and Hi-C technologies. To interpret and utilize these interactions, we constructed chromatin–chromatin spatial interaction (CCSI) database by integrating and annotating 91 sets of chromatin interaction data derived from published literature, UCSC database and NCBI GEO database, resulting in a total of 3 017 962 pairwise interactions (false discovery rate < 0.05), covering human, mouse and yeast. A web interface has been designed to provide access to the chromatin interactions. The main features of CCSI are (i) showing chromatin interactions and corresponding genes, enhancers and SNPs within the regions in the search page; (ii) offering complete interaction datasets, enhancer and SNP information in the download page; and (iii) providing analysis pipeline for the annotation of interaction data. In conclusion, CCSI will facilitate exploring transcriptional regulatory mechanism in disease pathogenesis associated with spatial interactions among genes, regulatory regions and SNPs. Database URL: http://120.79.23.67/ccsi/search.php

Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome.

Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs.

Abstract B2-14: Ancestrally derived SNPs influence on gene expression in breast cancer data sets

Abstract There is a critical need to identify factors driving aggressive breast cancer (BCa) that is more likely to develop in African American (AA) women who have a lower incidence BCa, but have a higher mortality from all invasive BCa subtypes compared to Caucasian (CA) women. The etiology driving these disparities has remained confounding though experimental evidence has shown a statistically significant biological difference stemming from an immunobiological gene signature in prostate, breast or colorectal tumor tissues. It is currently unknown if this signature can influence tumor initiation, progression and/or therapeutic intervention. Furthermore, there is a significant lack of a mechanistic understanding in the measured gene expression network signature between AA and CA BCa tumors. Using existing data from the NIH's Cancer Genome Atlas (TCGA), we correlated gene expression between AA and CA tumors with 63 genes containing ancestry informative missense mutations. The genes were selected from single nucleotide polymorphisms (SNPs) using the Affymetrix SNP 6 array. Briefly, SNPs with a minor allele frequency (MAF) greater than 0.3 in the HapMap's Yoruba population from Ibadan, Nigeria (YRI) as well as a MAF less than 0.05 in the Caucasian (CEU), Chinese (CHB) and Japanese (JPT) populations resulted in a list of 17,995 SNP candidates. Those SNPs in non-coding regions were removed resulting in 7,782 gene annotated SNPS. Finally, only those SNPs in exonic regions with protein coding mutations (non-synonymous SNPs) lead to the 63 gene list. To compare these genes with existing gene expression, admixture analysis was performed on the TCGA genotypes from the Affymetrix Genome-Wide Human SNP Array 6.0 using normal tissues. An initial independent list of 4,323 cross-platform comparable SNPs were chosen for admixture analysis. Each SNP for each individual was filtered for a birdseed confidence score &amp;lt;0.1. Using PLINK v1.07, an additional 828 SNPs were excluded from further analysis if they had a minor allele frequency (MAF) &amp;lt;0.05 or displayed deviation from the Hardy-Weinberg equilibrium (p &amp;lt; 0.00001). A total of 641 SNPs were used in admixture analysis from the Bayesian Markov Chain Monte Carlo method from the program Structure 2.3.4. Gene expression analysis was performed on tumors by stage (I to III) or subtype (Luminal A, Luminal B, HER2 positive or Triple Negative) between AA with CA patients. Though additional genes related to an immunobiological signature were in the list of 63 candidate genes (e.g. CD86 molecule and chemokine C-X-C motif receptor 6) only a statistically significant correlation between only one gene with the filtered SNP mutation list was found. This gene was the interleukin 6 signal transducer (IL6ST) with a SNP mutation in rs2228046 resulting in a switch from isoleucine to threonine. The functional consequence of this switch in breast cancer is unknown. The use of this approach may uncover additional tumor gene expression that is a result of ancestral variation in coding sequences. The functional consequences of these variations in normal and tumor cells may potentiate tumor initiation, progression and response to therapy. Future investigation of sequence specific variant analysis between CA and AA tumors and impact on additional gene as well as protein expression using the TCGA data is currently under investigation. Citation Format: John Tyson McDonald, Luisel Ricks-Santi. Ancestrally derived SNPs influence on gene expression in breast cancer data sets. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-14.

Functional Variant in Methionine Synthase Reductase Intron-1 Significantly Increases the Risk of Congenital Heart Disease in the Han Chinese Population

Homocysteine is known to be an independent risk factor for congenital heart disease (CHD). Methionine synthase reductase (MTRR) is essential for the adequate remethylation of homocysteine, which is the dominant pathway for homocysteine removal during early embryonic development. Here, we report that the c.56+781 A>C (rs326119) variant of intron-1 of MTRR significantly increases the risk of CHD in the Han Chinese population. In 3 independent case-control studies involving a total of 2340 CHD patients and 2270 healthy control participants from different geographic areas, we observed that patients carrying the heterozygous AC and homozygous CC genotype had a 1.40-fold (odds ratio=1.40; P=2.32×10(-7)) and 1.84-fold (odds ratio=1.84; P=2.3×10(-11)) increased risk, respectively, of developing CHD than those carrying the wild-type AA genotype. Both in vivo quantitative real-time polymerase chain reaction analysis of MTRR mRNA in cardiac tissue samples from CHD patients and in vitro luciferase assays in transfected cells demonstrated that the c.56+781 C allele profoundly decreased MTRR transcription. Further analysis demonstrated that the c.56+781 C allele manifested reduced CCAAT/enhancer binding protein-α binding affinity. In addition, healthy individuals with the homozygous CC genotype had significantly elevated levels of plasma homocysteine compared with the wild-type AA carriers. We have demonstrated that the MTRR c.56+781 A>C variant is an important genetic marker for increased CHD risk because this variant results in functionally reduced MTRR expression at the transcriptional level. Our results accentuate the significance of functional single-nucleotide polymorphisms in noncoding regions of the homocysteine/folate metabolism pathway core genes for their potential contributions to the origin of CHD.

Use of chromosome walking in discovery of single-nucleotide polymorphism in noncoding regions of a candidateactin gene inPinus radiata

Untranslated regions (UTRs) of eukaryotic mRNAs play crucial roles in post-transcriptional regulation of gene expression via the modulation of nucleocytoplasmic mRNA transport, translation efficiency, subcellular localization, and message stability. Single-nucleotide polymorphisms (SNPs) in UTRs of a candidate gene may also change the post-transcriptional regulation of a gene or function by nucleotide mutation. For species that have not been entirely sequenced genomically, new methods need to be devised to discover SNPs in noncoding regions of candidate genes. In this study, based on the expressed sequence tag (EST) of Pinus radiata (Monterey pine), we obtained all the sequences of UTRs of the actin gene by using a chromosome walking method. We also detected all the SNPs in and around the coding region of the actin gene. In this way, the full genomic sequence (2154 bp) of the actin gene was identified, including the 5&rsquo;UTR, introns, the coding sequence, and the 3&rsquo;UTR. PCR amplification and DNA fragment sequencing from 200 unrelated P. radiata trees revealed a total of 21 SNPs in the actin gene, of which 3 were located in the 5&rsquo;UTR, 3 in the introns, 10 in the coding sequence, and 5 in the 3&rsquo;UTR. We show that chromosome walking can be used for obtaining the sequence of UTRs, and then, based on this sequence, to discover SNPs in the noncoding regions of candidate genes from this species without an entire genomic sequence.

Abstract 2217: Analysis of mitochondrial variations in non-small cell lung cancer (NSCLC) by microarray-based genome resequencing

Abstract The human mitochondrial genome is a circular double-stranded DNA molecule (mtDNA) of 16,569 base pairs, encoding 13 proteins that are essential in oxidative phosphorylation, two ribosomal genes, and 22 tRNAs. The mtDNA genome has a high copy number per cell, replicates independently of the nuclear genome, and has a maternal-inherited pattern. Mutations in mtDNA genome have long been suspected to play an important role in the development of cancer. Although most cancer cells harbor mtDNA mutations, the question of whether such mutations are associated with clinical prognosis of cancer remains unclear. In this study, we resequenced the entire mitochondrial genomes of tumor tissue from a population of 249 Korean non-small cell lung cancer (NSCLC) patients using the Affymetrix GeneChips Human Mitochondrial Resequencing Array 2.0 (Santa Clara, CA). A total of 892 single nucleotide polymorphisms (SNPs) were identified, including 269 (30.2%) synonymous SNPs, 142 (15.9%) non-synonymous SNPs, 78 (8.7%) SNPs in non-coding region, 188 SNPs (21.1%) in control region, as well as 215 (24.1%) previously unreported SNPs. Each tumor sample contained at least 6 mitochondrial SNPs, with maximum 41 SNPs. We selected 189 (21.2%) SNPs with frequency &amp;gt;2.5% for subsequent analyses. Single SNP analysis using Cox proportional hazards models identified 19 SNPs with p value &amp;lt; 0.05, but none of them remained significant after adjustment for multiple comparisons. Unsupervised clustering analysis revealed two major SNP signature panels, including Signature Panel 1 (8 SNPs) and Signature Panel 2 (5 SNPs). Accordingly, the patient population was divided into three groups: 68 (27.3%) subjects with both signature panels, 73 (29.3%) subjects with only the Signature Panel 1 but not the Signature Panel 2, and 108 (43.4%) subjects without signature panel. In early stage (stage I/ II) NSCLC, patients with the Signature Panel 2 had the worst clinical prognosis, and patients without any signature panel performed best in the survival analyses (adjust hazard ratio=1.83; 95% confidence interval, 1.25-2.66; p=0.003). Further mitochondrial haplogroup analysis identified that the Signature Panel 1 had most defining mutations for haplogroup M and N, and the Signature Panel 2 contained all defining mutations of haplogroup D4a. Interestingly, haplogroup D4a was previous reported as a marker for extreme longevity in Japanese. We conclude that mtDNA mutations may play an important role in survival among early stage lung cancer patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2217.

SNP@Promoter: a database of human SNPs (Single Nucleotide Polymorphisms) within the putative promoter regions

BackgroundAnalysis of single nucleotide polymorphism (SNP) is becoming a key research in genomics fields. Many functional analyses of SNPs have been carried out for coding regions and splicing sites that can alter proteins and mRNA splicing. However, SNPs in non-coding regulatory regions can also influence important biological regulation. Presently, there are few databases for SNPs in non-coding regulatory regions.DescriptionWe identified 488,452 human SNPs in the putative promoter regions that extended from the +5000 bp to -500 bp region of the transcription start sites. Some SNPs occurring in transcription factor (TF) binding sites were also predicted (47,832 SNP; 9.8%). The result is stored in a database: SNP@promoter. Users can search the SNP@Promoter database using three entries: 1) by SNP identifier (rs number from dbSNP), 2) by gene (gene name, gene symbol, refSeq ID), and 3) by disease term. The SNP@Promoter database provides extensive genetic information and graphical views of queried terms.ConclusionWe present the SNP@Promoter database. It was created in order to predict functional SNPs in putative promoter regions and predicted transcription factor binding sites. SNP@Promoter will help researchers to identify functional SNPs in non-coding regions.

ABCA1 regulatory variants influence coronary artery disease independent of effects on plasma lipid levels.

The authors have previously shown that individuals heterozygous for ABCA1 mutations have decreased high density lipoprotein cholesterol, increased triglycerides and an increased frequency of coronary artery disease (CAD), and that single nucleotide polymorphisms (SNPs) in the coding region of the ABCA1 gene significantly impact plasma lipid levels and the severity of CAD in the general population. They have now identified several SNPs in non-coding regions of ABCA1 which may be important for the appropriate regulation of ABCA1 expression (i.e. in the promoter, intron 1 and the 5' untranslated region), and have examined the phenotypic effects of these SNPs in the REGRESS population. Out of 12 SNPs, four were associated with a clinical outcome. A threefold increase in coronary events with an increased family history of CAD was evident for the G-191C variant. Similarly, the C69T SNP was associated with a twofold increase in events. In contrast, the C-17G was associated with a decrease in coronary events and the InsG319 was associated with less atherosclerosis. For all these SNPs, the changes in atherosclerosis and CAD occurred without detectable changes in plasma lipid levels. These data suggest that common variation in non-coding regions of ABCA1 may significantly alter the severity of atherosclerosis, without necessarily influencing plasma lipid levels.

Linkage Disequilibrium and Allele-Frequency Distributions for 114 Single-Nucleotide Polymorphisms in Five Populations

Single-nucleotide polymorphisms (SNPs) may be extremely important for deciphering the impact of genetic variation on complex human diseases. The ultimate value of SNPs for linkage and association mapping studies depends in part on the distribution of SNP allele frequencies and intermarker linkage disequilibrium (LD) across populations. Limited information is available about these distributions on a genomewide scale, particularly for LD. Using 114 SNPs from 33 genes, we compared these distributions in five American populations (727 individuals) of African, European, Chinese, Hispanic, and Japanese descent. The allele frequencies were highly correlated across populations but differed by >20% for at least one pair of populations in 35% of SNPs. The correlation in LD was high for some pairs of populations but not for others (e.g., Chinese American or Japanese American vs. any other population). Regardless of population, average minor-allele frequencies were significantly higher for SNPs in noncoding regions (20%-25%) than for SNPs in coding regions (12%-16%). Interestingly, we found that intermarker LD may be strongest with pairs of SNPs in which both markers are nonconservative substitutions, compared to pairs of SNPs where at least one marker is a conservative substitution. These results suggest that population differences and marker location within the gene may be important factors in the selection of SNPs for use in the study of complex disease with linkage or association mapping methods.