SNPs, Haplotypes, and Cancer: Applications in Molecular Epidemiology

Over the past few years, single nucleotide polymorphisms (SNPs) have been proposed as the next generation of markers for the identification of loci associated with complex diseases and for pharmacogenetic applications (Lander and Schork 1994; Lander 1996; Risch and Merikangas 1996; Kruglyak 1997; Schafer and Hawkins 1998). SNPs are frequently present in the genome with a density of at least one common (>20% allele frequency) SNP per kilobase pair (Lai et al. 1998; Sachidanandam et al. 2001). They are mostly biallelic ( 1.6 million SNPs in the public databases (Sachidanandam et al. 2001). In this article, I will attempt to summarize what we know about SNPs and identify some of the challenges that await us in the application of SNPs in research and medicine. The first questions most people would ask are, how many SNPs are there in the human genome and have we identified most of the SNPs? The frequently cited rate of 1 SNP/kb suggests that there are 3 million common SNPs in the human genome. However, recent data have indicated that the number of SNPs in the human genome is potentially much more than 3 million. The first indication came from the comparison of the Celera SNP database with the public data. Celera Genomics claimed to contain over 3.5 million putative SNPs in their database. However, only 400,000 of their SNPs were redundant when compared to the publicly available 1.6 million. The second line of evidence came from our own experiments. We have isolated >1000 SNPs in a 20megabase region by re-sequencing eight individuals (not the same DNA source as the TSC SNPs). The overlap between our SNPs (∼1,000) and the TSC SNPs in this region is ∼5% (instead of the expected 50% if the total number of common SNP is around 3 million). These results suggest that there are potentially 10 million or more common SNPs in the human population. A theoretical modeling experiment has also predicted that there are more than 10 million SNPs in the genome (Kruglyak and Nickerson 2001). There are two important implications in the usage of SNPs as a genetic tool if there are indeed over 10 million SNPs in the human genome. The first implication is that the SNP(s) you are looking for might not be discovered yet. The second implication is the need to select a representative set of SNPs out of the 1.6 million to cover the genome. The first problem is a difficult one since it is impossible to know whether the SNP(s) of interest is present in the current databases. There are two potential solutions. The first solution is to design experiments that combine SNP discovery and genotyping (Brenner et al. 2000). However, this approach has not been demonstrated for whole genome SNP scan and could be costly even if it is technically feasible. The second solution, which is suitable for both implications mentioned above, is the development of a comprehensive whole genome SNP marker set that has a high likelihood of detecting the SNP(s) of interest by linkage disequilibrium or association (see section below on marker set development) (Jorde 2000). So how do we design a marker set that covers the genome as completely as possible? There are many suggestions and computer models using linkage disequilibrium (LD) as a guide and striking a balance between number of markers and information content (Kruglyak 1999; Jorde 2000). A number of recent studies have indicated that an average spacing of 30 kb provides a good balance (i.e., 100,000 SNPs for whole genome) (Collins 1999; Huttley et al. 1999; Goddard et al. 2000; Jorde 2000). In addiE-MAIL ehl21107@GlaxoWellcome.com; FAX (919) 315-0113. Article and publication are at www.genome.org/cgi/ doi/10.1101/gr.192301. Insight/Outlook

Genetics and biology of asthma 2010: La' ci darem la mano…

Genome-wide association studies (GWAS) have identified genetic variants in nine chromosomal regions that are associated with atrial fibrillation (AF). The mechanisms underlying these associations are unknown.To investigate these mechanisms, we...

Interleukin-6 Sequence Variants Are not Associated with Prostate Cancer Risk

Tumorigenesis in sporadic cancers is mainly driven by somatic genetic alterations such as driver mutations in protein coding genes or chromosomal changes comprising deletions, amplifications or translocations resulting in loss of tumor suppressor proteins, gain of oncogenic proteins or expression of aberrant fusion proteins, respectively. Some cancers lack such somatic changes, but are addicted to expression of certain genes for their sustained proliferation and survival. There is evidence of such oncogenic addiction to LIM domain only 1 (LMO1) expression in neuroblastoma (NB). Genome-wide association studies (GWAS) have identified robust associations between germline single-nucleotide polymorphisms (SNPs) within LMO1 and NB susceptibility with the causal SNP being rs2168101. Investigation of the mechanism of NB dependency on LMO1 showed that LMO1 expression in NB cells is regulated by rs2168101, which resides within a highly conserved tissue-specific super enhancer in LMO1 intron 1 and drives LMO1 expression through GATA3 transcription factor binding. This makes LMO1-dependent NB a unique example of sporadic cancer driven by germline genetic variation. Numerous GWAS have identified significant associations between germline SNPs in the non-coding genome and cancer risk or outcomes. To identify additional examples of regulatory SNPs as cancer drivers, we merged published genome-wide significant associations from cancer GWAS with genome regulatory data from ENCODE (Encyclopedia of DNA Elements; Nature. 2012 Sep 6; 489 (7414): 57-74)) and searched for clusters of cancer associated SNPs that resided within gene regulatory elements. Gene regulatory elements were defined as those marked by active epigenetic features and chromatin accessibility in cancer cell lines. Of the ~1,600 unique, genome-wide significant SNPs from cancer GWAS with regulatory evidence, we identified 46 clusters of 3 or more putative regulatory SNPs near 28 genes. These clusters were particularly enriched within ovarian cancer associated loci. Mechanistic studies such as reporter assays and genome editing in relevant cell types are being considered to identify the causal SNPs from these clusters regulating gene expression and driving tumorigenesis, which in turn may lead us to new cancer targets. Citation Format: Diptee A. Kulkarni, Kijoung Song, Karl Guo. Identification of potential cancer regulatory germline single-nucleotide polymorphisms in the non-coding genome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1284. doi:10.1158/1538-7445.AM2017-1284

Gene-environment interaction in allergic disease: More questions, more answers?

The β2-adrenoceptor gene and hypertension: is it the promoter or the coding region or neither?

The human genome is estimated to contain one single nucleotide polymorphism (SNP) every 300 base pairs. The presence of LD between SNP markers can be used to save genotyping cost via appropriate SNP tagging strategies, whereas absence or low level of LD between markers generally increase genotyping cost. It is quite common that a large proportion of tagging SNPs in a tagging scheme often turn out to be singleton SNPs, that is, SNPs that only tag themselves rather than contribute power to the rest of a region. If genotyping cost is a major concern, which often is the case at the present time for genome-wide association studies, these singleton tagging SNPs would be the primary targets to be removed from genotyping. It is important, however, to understand the characteristics of such SNPs and estimate the impact of removing them in a study. Using the HapMap genotype data and genome wide expression data, we assessed the distribution and functional implications of singleton SNPs in the human genome. Our results demonstrated that SNPs of potentially higher functional importance (eg, nonsynonymous SNPs, SNPs in splicing sites and SNPs in 5' and 3' UTR) are associated with a higher tendency to be singleton SNPs than SNPs in intronic and intergenic regions. We further assessed whether singleton SNPs can be tagged using haplotypes of tagSNPs in the three genome wide chips, that is, GeneChip 500k of Affymetrix, HumanHap300 and HumanHap550 of Illumina, and discussed the general implications on genetic association studies.

5. Genetics of allergic disease

An update on the genetics of atopic dermatitis: Scratching the surface in 2009

A Flexible Bayesian Framework for Modeling Haplotype Association with Disease, Allowing for Dominance Effects of the Underlying Causative Variants

Dear Editor, Neuroblastoma is the most common non-central nerve system (CNS) solid tumor in pediatrics [1]. Neuroblastoma accounts for approximately 8% of all pediatric cancers but disproportionally causes a high cancer mortality (15%) in children [2]. Pediatric patients with low-risk neuroblastoma witness a 5-year overall survival rate > 90%, whereas the 5-year overall survival rate in high-risk neuroblastoma pediatric patients is < 40% [3]. Genetic susceptibility to neuroblastoma is a promising area of research and needs to be fully investigated. For sporadic neuroblastoma, genome-wide association studies (GWASs) have identified over a dozen causal genetic loci. Studies of candidate genes also reported a decent number of variants predisposing to neuroblastoma. However, the known genetic alternations still could not unveil the full genetic underpinnings of neuroblastoma. The base excision repair (BER) pathway, one of the DNA repair systems, is responsible for repairing numerous oxidized and alkylated bases by recognizing and excising damaged bases [4]. Many core proteins are involved in the BER pathway, including poly(ADP)ribose polymerase 1 (PARP1), human 8-oxoguanine DNA glycosylase (OGG1), flap endonuclease 1 (FEN1), apurinic/apyrimidinic endonuclease 1 (APEX1), DNA ligase III (LIG3), and x-ray repair cross-complementing group 1 (XRCC1). OGG1 is a bifunctional enzyme (DNA glycosylase and AP lyase) that incises at abasic sites via an AP lyase activity, leaving a single-strand DNA break intermediate. APEX1 initiates the repair of abasic sites in DNA by cleaving the phosphodiester backbone 5′ to an AP site, creating a nick in the DNA backbone. FEN1 participates in the penultimate steps of Okazaki fragment maturation and 5′-flap removal during long-patch BER. LIG3 catalyzes the last stage of BER by sealing the gap. XRCC1 and PARP1 serve as the scaffold protein. Intensive evidence suggests that aberrant BER pathway proteins result in a variety of diseases, especially cancers [4]. Single nucleotide polymorphisms (SNPs) of the BER pathway genes are associated with the risk of various cancer types. Functional analysis revealed that SNPs in the BER pathway genes may modify the kinetics of BER proteins and the DNA repair capacity of the BER system, ultimately affecting carcinogenesis [4]. However, evidence regarding the role of BER pathway gene SNPs in the risk of neuroblastoma waits to be added. To identify more neuroblastoma susceptibility variations in the BER pathway genes, we performed a case-control study in children at three center hospitals in East China. This study was conducted in Children's Hospital of Nanjing Medical University (Nanjing, Jiangsu), Anhui Provincial Children's Hospital (Hefei, Anhui), and Yuying Children's Hospital of Wenzhou Medical University (Wenzhou, Zhejiang) in East China. A total of 313 neuroblastoma pediatric patients and 762 cancer-free children were recruited in this study. The characteristics of the study subjects are summarized in Supplementary Table S1. Age (P = 0.823) and gender (P = 0.610) were distributed equivalently between the two groups. The study design and participant recruitment were described in our previous work [5]. We successfully genotyped 20 SNPs from 6 BER pathway genes in 313 neuroblastoma pediatric patients and 762 control children (Table 1). Specifically, 3 PARP1, 3 OGG1, 2 FEN1, 3 APEX1, 3 LIG3, and 6 XRCC1 SNPs were genotyped. The genotypic distributions of all candidate SNPs were in Hardy-Weinberg equilibrium (P ≥ 0.05) in the controls. The rs174538 of the FEN1 gene was associated with decreased neuroblastoma risk under the dominant model (adjusted odd ratio [OR] = 0.71, 95% confidence interval [CI] = 0.54-0.93, P = 0.012). However, no significant associations with neuroblastoma risk were found for the remaining SNPs in the single-locus analysis (all P ≥ 0.05; Supplementary Figure S1). We conducted the stratified analyses (Supplementary Table S2) to eliminate potential influences of FEN1 genotypes on neuroblastoma susceptibility by adjusting confounding factors (age, gender, and site of tumor origin). The protective role of rs174538 AG/GG in decreasing neuroblastoma risk was found in subgroups of age ≤18 months (adjusted OR = 0.60, 95% CI = 0.40-0.89, P = 0.011), females (adjusted OR = 0.59, 95% CI = 0.40-0.87, P = 0.009), and tumors arising from the mediastinum (adjusted OR = 0.53, 95% CI = 0.35-0.81, P = 0.003). Combined analysis stated that the 2 protective genotypes (rs174538 AG/GG and rs4246215 TG/GG genotypes) also decreased neuroblastoma risk in the following subgroups: age ≤ 18 months (adjusted OR = 0.62, 95% CI = 0.42-0.93, P = 0.019), females (adjusted OR = 0.61, 95% CI = 0.41-0.91, P = 0.015), and tumors originated from the mediastinum (adjusted OR = 0.54, 95% CI = 0.36-0.83, P = 0.005). We carried out false-positive report probability (FPRP) analysis to validate significant associations (Supplementary Table S3). The threshold for FPRP was preset as 0.2. At the prior probability level of 0.1, significant associations with FEN1 rs174538 A > G (GG/AG vs. AA) remained noteworthy in all subjects (FPRP = 0.121) as well as in the subgroups of females (FPRP = 0.185) and tumors originating from the mediastinum (FPRP = 0.160). In the combined analysis, significant findings for 2 vs. 0-1 protective genotypes (FPRP = 0.166) and its subgroup tumors originated from the mediastinum (FPRP = 0.183) could be called noteworthy. We further explored the biological effects of FEN1 rs174538 A > G on the neighboring gene expression by using released data from Genotype-Tissue Expression (GTEx) Portal (https://www.gtexportal.org/). We observed that rs174538 A allele was significantly associated with increased mRNA expression levels of fatty acid desaturase 2 (FADS2) and transmembrane protein 258 (TMEM258) in the whole blood, nerve-tibial, and cell-cultured fibroblasts (Figure 1A). The rs174538 A allele was also associated with increased expression of fatty acid desaturase 1 (FADS1) mRNA in the whole blood, but with decreased expression of FADS1 mRNA in the nerve-tibial (Figure 1B). eQTL analysis of the neuroblastoma risk factor FEN1 rs174538 A > G. A. FADS2 and TMEM258 levels in the whole blood, nerve-tibial, and cell-cultured fibroblasts; B. FADS1 level in the whole blood and nerve-tibial. Abbreviations: eQTL, expression quantitative loci; FEN1, flap endonuclease 1; FADS2, fatty acid desaturase 2; TMEM258, transmembrane protein 258 The implication of the BER pathway gene SNPs in cancer susceptibility has been highly documented. Plenty of SNPs within the BER pathway genes were found to predispose to various types of cancer. Our group previously carried out a study on BER gene polymorphisms and Wilms tumor susceptibility [6]. Significant associations with Wilms tumor susceptibility were shown for the OGG1 rs1052133, FEN1 rs174538, and FEN1 rs4246215 polymorphisms. Regarding the association of the BER pathway gene SNPs with neuroblastoma risk, only 3 studies were available by far; and all of them were performed by our research group. In these studies, we found that, none of the studied APEX1 polymorphisms were associated with neuroblastoma risk [5]. Such a negative association was also observed between neuroblastoma risk and polymorphisms in the OGG1 [7] and LIG3 genes [8]. However, all these studies were conducted to analyze a single gene in the BER pathway, and the results need to be validated in another independent study. Thus, here we attempted to validate the previous studies by adopting a systematical analysis of potentially functional SNPs in 6 core genes in the BER pathway. In the current study, no significant relationships were detected between neuroblastoma risk and the SNPs in PARP1, OGG1, APEX1, LIG3, and XRCC1 genes. Such results strengthen the previous findings that these variations may be too weak to impact neuroblastoma risk. To be noted, significant conferring roles of the same BER SNPs to the risk of other cancer types have been detected, such as PARP1 rs1136410 and thyroid cancer [9], OGG1 rs1052133 and Wilms tumor [6], FEN1 rs4246215 and Wilms tumor [6], APEX1 rs1130409 and renal cell carcinoma [10], LIG3 rs1052536 and lung cancer [11]. The different roles of these SNPs in specific cancer types indicated that specific cancer types should be set before interpreting the role of SNPs. Excitedly, we demonstrated that the rs174538 of the FEN1 gene could protect from neuroblastoma. FEN1 is a structure-specific nuclease involved in the removal of 5′-flap during long-patch BER and the maturation of Okazaki fragments in DNA replication. Moreover, FEN1 is also characterized as a 5′ exonuclease and a gap-dependent endonuclease, which mediates apoptotic DNA degradation during apoptosis. The FEN1 gene is mapped to chromosome 11 (11q12.2). Yang et al. [12] identified that the rs174538 A allele of the FEN1 gene decreased risk for lung cancer by decreasing FEN1 expression. Moreover, they detected that coke oven workers who carried the AA genotype have significantly lower DNA damage level than those with GG or GA genotypes. In a meta-analysis conducted for the overall cancer, the results suggested that the subjects with FEN1 rs174538 A allele have a decreased susceptibility to cancer in Chinese populations [13]. We further performed online expression quantitative trait loci (eQTL) analysis to interpret the possible mechanism of how rs174538 impacts neuroblastoma risk. eQTL evidence suggested that the A allele in rs174538 was significantly associated with the increased mRNA expression levels of FADS2 and TMEM258. Further functional experiments conducted in neuroblastoma cells are needed to show how the FEN1 rs174538 A allele can be associated with altered expressions of these genes. FADS2 was found to function as a potential oncogene in some types of cancer [14]. TMEM258 is a central mediator of endoplasmic reticulum quality control and intestinal homeostasis, yet its role in cancer remains unknown [15]. The exact relationship of FADS2 and TMEM258 with neuroblastoma risk waits to be elucidated. Taken together, the significant role of rs174538 A allele in cancer deserves more attention for further exploration. Although at the preliminary stage, our findings represent a novel mechanism by which rs174538 may modulate the expression of multiple nearby genes, thereby impacting the risk of neuroblastoma. Our study has several limitations. First, the sample sizes were small in some stratification analyses. Second, the number of analyzed SNPs was limited. Another limitation was the lack of incorporating analysis on environment factors and genetic-environmental factors. The fourth limitation was that the current study only focused on the subjects of the Han population. Replication of these findings in additional individuals of non-Chinese descent should be helpful to validate our findings. In conclusion, we showed a robust association of genetic variants in the FEN1 gene with neuroblastoma risk in a relatively large sample size of pediatric patients in East China. Intensive future research is warranted to extend the role of FEN1 gene loci in neuroblastoma susceptibility in individuals of non-Chinese ancestries. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, and was approved by the Ethics Committees of Children's Hospital of Nanjing Medical University, Anhui Provincial Children's Hospital, and the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Each participant signed an informed consent before participating to this study. Not applicable. All data generated or analyzed during this study are included in this published article and its additional files. The authors declare that they have no competing interests. This work was supported by the grants from Natural Science Foundation of Guangdong Province (2019A1515010360) and Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease (2019B030301004). Z.Z., J.Z., H.L., J.H., and Y.W. designed the study, performed the experiments and wrote the manuscript. C.Z., Y.F., H.Z., H.W., and Y.W. collected the clinical samples and information. Z.Z. and J.H. analyzed the data and prepared all the tables and figures. Z.Z., J.H., and Y.W. coordinated the study. All authors reviewed and approved the final manuscript. Not applicable. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

These are exciting times, with a plethora of new technologies that are expediting discovery of the genetic underpinnings of human disease. Comprehensive resequencing of the human genome is now feasible and affordable, allowing each person's entire genetic makeup to be revealed. The major focus of

BackgroundThe recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs.ResultsOur analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs.ConclusionWe have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region.

The sequencing of the human genome, the identification of common single-nucleotide polymorphisms (SNPs) and haplotype blocks, and advances in microarray technology have enabled the study of complex diseases at a level of detail not previously imaginable. These have aided in the design and analyses of association and linkage studies of many complex diseases including cardiovascular disease. Recent technological advances have enabled the undertaking of large-scale genome-wide association studies (GWAS) that can assay hundreds of thousands of polymorphic sites on hundreds to thousands of individuals to find genomic regions associated with disease. Although results from these experiments enable the identification of smaller regions of association compared with previous studies, as with all linkage and association studies, there is the need for the further investigation of regions of interest for the causal genes or variants. The purpose of this review is to present a detailed demonstration as to how publicly available resources can be used to easily guide more detailed research into genomic regions of interest identified in linkage and association study data. Large-scale projects, such as the Human Genome Sequencing project,1,2 have generated large volumes and varieties of annotated genomic data necessitating the development of Internet-based tools to organize and make practically available these public data. One important tool in human disease research is the web-based graphical genome browsers that use the human genome sequence as the framework on which to organize genomic annotations, providing various ways for researchers to view and extract important information. Currently, there are 3 human genome browsers that have been developed for public use: (1) the National Center for Biotechnology Information (NCBI) Map Viewer3; (2) the University of California Santa Cruz (UCSC) Genome Browser4; and (3) the European Bioinformatics Institute’s Ensembl system.5 Although these genome browsers share common features and …