Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions. GWAS have used microarrays that allow parallel assessment of hundreds of thousands of single nucleotide polymorphisms (SNPs), and now also copy number variant (CNVs). The technology of Affymetrix (http://affymetrix.com), Illumina (http://illumina.com) and Perlegen (http://perlegen.com) have been most often used for these studies. While these microarrays have been designed to efficiently explore genetic variation across the entire human genome, they each provide better coverage in some genomic regions than in others Genome-wide association studies (GWAS) have pushed human genetics into a new era. Advances in technology and affordability are rapidly allowing GWAS to identify genetic variants that affect risk for human disease  is known to be expressed in neurons in the human adult cerebral cortex, midbrain, thalamus and medulla regions CDH13 contains 2,414 SNPs that are common in the African population, and only 50% of these are tagged by the by the Illumina 610 Quad microarray. http://zork.wustl.edu/nida/neurosnp.html) to systematically determine how to supplement these commercial microarrays for addiction. Our database includes a SNP prioritization score based on the genomic information network (GIN) method introduced by S. Saccone and colleagues a priori biological hypotheses into the prioritization of SNPs after a genome-wide association study. The method begins with a set of SNPs that are ranked by their association p-values, and then increases the rank of a SNP when it is determined to be biologically relevant to the phenotype according to an a priori set of conditions, such as being in a biologically relevant gene, and additionally, perhaps, being a missense mutation. The score is a measure of biological relevance to addiction, and can be used independently of association p-values to prioritize which SNPs are selected to supplement commercial microarrays. The score incorporates SNP/gene functional properties (such as coding and promoter regions), human/mouse evolutionary conservation, and a quantitative trait locus (QTL) mapping method that utilizes mouse models to identify genes associated with addiction phenotypes . We have designed a SNP database  that identified 7,842 additional genes with potential biological relevance to addiction through mouse QTL and gene expression correlation analysis and the GIN prioritization scores reflect this quantitative assessment of biological relevance. Genes with a large number of mouse associations are prioritized more highly, and those with a relatively low number receive little increase in the prioritization score relative to arbitrary genes , which contains a list of all human diseases that have been linked to the gene, including links to publications. The spreadsheet also contains links to the Knowledgebase of Addiction Related Genes  http://genenetwork.org) for additional information on mouse systems genetics data. We have also created a web site (http://zork.wustl.edu/nida/neurosnp.html) that contains a searchable database of this set of genes, as well as downloadable files for the gene and SNP databases. These resources will allow investigators to both gather new biologically relevant targets for genetic association studies of addiction, and also to discover new information on well-known targets, such as the extent of tagged coverage in various population by commercial SNP microarrays.The combined set of 910 genes used for our analysis of SNP microarrays is available as a spreadsheet in supporting http://zork.wustl.edu/nida/neurosnp.html, and the top 5,000 SNPs ranked by GIN prioritization score Our complete SNP database is available for download from our web site at We have found that in order to achieve comprehensive tagged coverage of genes that are biologically relevant to addiction in the African, Chinese, European-American and Japanese populations, all the commercial SNP microarrays we considered require significant supplementation. The approaches used here will aid other investigators to supplement these arrays, to target specific genomic regions such as genes and linkage regions, and also improve the general selection of SNPs for genetic studies of addiction based on the “biological role in addiction” criterion. The development of a database of addiction related genes is similar to existing methods and resources in the literature a priori biological hypotheses, either within the context of a GWAS, or for a more targeted study such as studying specific addiction-related genes or fine mapping a region of genetic linkage for an addiction-related phenotype. The need for this kind of approach has been discussed in the literature a priori biological hypotheses These methods and resources were developed with the intention that they would be useful to researchers who wish to test While the primary utility of the resource we have developed is the supplementation of commercial SNP microarrays, it has several other useful applications. For example, the GIN prioritization scoring method is useful for interpreting the results of a GWAS, and can be used to prioritize SNPs for further study after a GWAS http://www.illumina.com), and because some of the SNPs used for Our results highlight the need to supplement commercial SNP microarrays for genetic studies of addiction in order to have adequate coverage for genes in relevant neurobiological pathways. Our SNP database will help researchers to fill the missing gaps by providing a quantitative measure of biological relevance to help prioritize SNPs for supplementation. Addiction researchers should find this resource to be a valuable tool, both in the design and interpretation stages of a GWAS. It helps prioritize coverage of biologically relevant regions, and highlights association signals in those regions when selecting SNPs for replication. It also helps prioritize tests of gene-gene interaction, which can limit multiple testing issues. In this study the focus was on addiction, but our method can be extended to other diseases by creating a new database of biologically relevant genes.http://grants.nih.gov/grants/guide/notice-files/NOT-DA-07-010.html). Most of these were identified based on involvement in neurobiological pathways relevant to substance abuse and were in part vetted by addiction neurobiologists (see Acknowledgments). These included genes involved in biosynthesis, metabolism, transport, receptor binding, and intracellular signaling. Common biochemical pathways and systems included the serotonergic, noradrenergic, dopaminergic, GABA-ergic, glutamatergic, opioid, alcohol metabolizing and nicotinic systems. In addition, genes involved in the metabolism of FDA-approved medications for substance abuse were also included ; genes involved in the medications' pharmacodynamic effects were selected as part of the pathway-based approach. 34 of these genes were added to the initial results of the nomination process because they used for recent custom panel of SNPs for alcoholism and other addiction related traits We assembled a list of 486 genes biologically relevant for addiction mainly through an expert nomination process .An additional 424 genes were nominated based on behavioral genetic analysis of 255 measures of addiction related phenotypes obtained by the Tennessee Mouse Genome Consortium in the recently expanded panel of over 60 BXD recombinant inbred mouse lines . Because we are interested in identifying addiction associated pathway members that are polymorphic in humans, but not necessarily polymorphic in mice, our approach identified both candidate genes for mouse phenotypic variation, and biomolecular correlates of the mouse phenotypes. Each gene was chosen because it either resided in a significant or suggestive QTL interval or was a gene expression correlate of multiple addiction-related phenotypes. The criteria used to place genes on the list included correlations with http://www.ncbi.nlm.nih.gov/projects/SNP), which was our source of physical mapping data and SNP/gene functional properties. We then examined the SNP coverage of these genes as provided by seven commercial microarrays: the Illumina HumanHap300 Duo, HumanHap550, HumanHap650Y and Human1M (http://www.illumina.com), and the Affymetrix Genome-Wide Human SNP Array 5.0 and 6.0 (http://www.affymetrix.com). To assess genomic coverage of common SNPs by these microarrays, we used genotype data for four populations from the International HapMap Project, Public Release 23a (http://www.hapmap.org): African , Chinese (Beijing – CHB), European-Americans (CEPH – CEU), and Japanese (Japan – JPT). To estimate LD, we used the program HaploView  r2 for all SNPs within 500 kb of each other. The commonly used condition r2≥0.8 was used to assess whether a SNP is tagged through LD in a given population by a given SNP microarray. General database management was done with a combination of SAS To explore genetic variation in this combined set of 910 human genes, we created a general purpose genome-wide SNP relational database. The foundation for this database was Build 128 of dbSNP (http://ecrbase.dcode.org). The original GIN method introduced by Saccone and colleagues incorporated LD into the prioritization score through the use of LD proxies. This is more appropriate when prioritizing SNPs for replication after an initial GWAS. In our current implementation, where we are selecting SNPs to supplement arrays for the discovery phase of a GWAS, we have eliminated the LD component in order to avoid redundancy among the selected SNPs.To provide a mechanism for prioritizing SNPs when supplementing SNP microarrays for addiction, we used the genomic information network (GIN) technique introduced by Saccone and colleagues N/6,1), where N is the number of phenotypes identified for a gene using the systems genetic methods. This means that the score is 1 for all genes where N is greater than 6, which corresponds to the top 5% of QTL mapping results. The score is scaled down linearly when N is less than 6. Note that this particular GIN model combines information from the expert nomination process and the mouse systems genetics data in the sense that SNPs in the 486 expert nominated genes, the determination of which did not involve the mouse data, would receive an increased score if there was additional evidence from the mouse data of relevance to addiction.The scoring method is identical to Saccone and colleagues Table S12≥0.8 in the specified HapMap population . We explore how coverage varies with biological relevance by considering SNPs with a GIN prioritization score greater than a given threshold: the larger the score, the greater the biological relevance. For direct coverage, common SNPs must have a MAF of at least 5% in one of the HapMap populations. For tagged coverage, SNPs must satisfy this condition in the specified population.The coverage of genomic regions biologically relevant to addiction in four commercial SNP microarrays. The table is divided into direct coverage, the percentage of common SNPs actually on the array, and tagged coverage, the percentage of common SNPs tagged by an array through LD at r(0.06 MB DOC)Click here for additional data file.Table S2Results of the systems genetics study to identify mouse genes related to addiction. For each trait the table shows the overall number of genes identified by QTL and gene expression analysis. For our analysis of SNP microarray coverage we used the top 5% from the mouse systems genetics project ranked by the number of phenotypes linked to each gene. The third column shows the number of genes from the top 5% identified for each trait.(0.06 MB DOC)Click here for additional data file.Figure S12≥0.8 (the rightmost bar in the histogram).The number of genes biologically relevant to addiction that require varying amounts supplementary coverage for the Illumina 610 Quad microarray. Here we consider our primary set of 910 genes. The horizontal axis shows the percentage of SNPs in the gene not tagged by the array in the corresponding population. For example, in the African population, there are 35 genes (3.97%) where at least 90% of the SNPs in those genes are not tagged by this array with r(0.12 MB DOC)Click here for additional data file.Figure S2The distribution of the prioritization scores S from the genomic information network (GIN) for addiction. We considered all known SNPs using dbSNP build 128. The score is a cumulative measure of biological relevance based on several factors: our expert nomination process for genes related to addiction, SNP/gene functional properties, human/mouse evolutionary conservation, and mouse QTL mapping methods. For example, SNPs with a score of 0 are not in genes, and are not in LD with a gene or human/mouse evolutionary conserved region with 500 Kb. SNPs in genes have a score of at least 1. The score increases if the gene is biologically relevant to addiction, and increases further depending on the number of mouse QTLs for that gene, and also the functional properties of SNP, such being nonsynonymous or being in a promoter region.(0.05 MB DOC)Click here for additional data file.Figure S3The genomic information network (GIN) model for addiction. The network represents the process of determining a numeric prioritization score for a SNP. The scores are a cumulative measure of biological relevance using SNP/gene functional properties (the Gene node), evolutionary conserved regions (the ECR node), genes biologically relevant to addiction (the Addiction Systems node), and mouse QTL mapping results. The overall GIN prioritization scores can be used to prioritize SNPs when supplementing commercial microarrays for addiction.(0.05 MB DOC)Click here for additional data file.File S1This workbook contains two sheets: (1) an annotated sheet with the primary set of 910 genes used for analysis (2) a list of column descriptions(0.95 MB XLS)Click here for additional data file.File S2This workbook contains two sheets: (1) an annotated sheet with the top 5,000 SNPs ranked by prioritization score (2) a list of column descriptions(3.76 MB XLS)Click here for additional data file.


Cholesterol concentrations in blood are related to cardiovascular diseases. Recent genome-wide association studies (GWAS) of cholesterol levels identified a number of single-locus effects on total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) levels. Here, we report single-locus and epistasis SNP effects on TC and HDL-C using the Framingham Heart Study (FHS) data.-8) and no dominance effect reached genome-wide significance. Two new gene regions were detected, the RAB3GAP1-R3HDM1-LCT-MCM6 region of chr02 for TC identified by six new SNPs, and the OSBPL8-ZDHHC17 region (chr12) for HDL-C identified by one new SNP. The remaining 22 single-locus SNP effects confirmed previously reported genes or gene regions. For TC, three SNPs identified two gene regions that were tightly linked with previously reported genes associated with TC, including rs599839 that was 10 bases downstream PSRC1 and 3.498 kb downstream CELSR2, rs4970834 in CELSR2, and rs4245791 in ABCG8 that slightly overlapped with ABCG5. For HDL-C, LPL was confirmed by 12 SNPs 8-45 kb downstream, CETP by two SNPs 0.5-11 kb upstream, and the LIPG-ACAA2 region by five SNPs inside this region. Two epistasis effects on TC and thirteen epistasis effects on HDL-C reached the significance of "suggestive linkage". The most significant epistasis effect (p = 5.72 × 10-13) was close to reaching "significant linkage" and was a dominance × dominance effect of HDL-C between LMBRD1 (chr06) and the LRIG3 region (chr12), and this pair of gene regions had six other D × D effects with "suggestive linkage".Single-locus effects and pairwise epistasis effects of 432,096 SNP markers were tested for their significance on log-transformed TC and HDL-C levels. Twenty nine additive SNP effects reached single-locus genome-wide significance (p < 7.2 × 10Genome-wide association analysis of the FHS data detected two new gene regions with genome-wide significance, detected epistatic SNP effects on TC and HDL-C with the significance of suggestive linkage in seven pairs of gene regions, and confirmed some previously reported gene regions associated with TC and HDL-C. Total cholesterol (TC) is related to coronary diseases and high-density lipoprotein (HDL-C) cholesterol is anti-atherogenic. Genome-wide association studies (GWAS) and human genetic studies have identified a number of genes and gene regions affecting cholesterol phenotypes including TC and HDL-C -11. A meThe single-locus tests detected nine SNPs with additive  effects on TC and twenty SNPs with additive effects on HDL-C that reached genome-wide significance Tables -2. No do-8  that did not have significant SNPs. Eleven other SNPs spanning a 1.23 Mb region . These less significant results in the same neighborhood should add to the significance of the RAB3GAP1-R3HDM1-LCT-MCM6 region to TC. Three SNPs identified two genes that were tightly linked with previously reported genes associated with TC [PSRC1 (chr01) and 3.498 kb downstream CELSR2, rs4970834 in intron 28 of CELSR2, and rs4245791 in intron 3 of ABCG8 (chr02) that slightly overlapped with ABCG5, where CELSR2 and ABCG5 regions were reported to be associated with TC in a recent GWAS report [PSRC1 and ABCG8 also were reported to affect low-density lipoprotein cholesterol (LDL-C) [PSRC1 had the most significant single-locus effect on TC (p = 3.7 × 10-16), while the SNP inside CELSR2 (rs4970834) had the second most significant single-locus effect on TC (p = 1.29 × 10-10).For TC, nine SNPs with additive  effects reached genome-wide significance with p < 7.2 × 10C Figure . Of the n Figure  that inc to NCBI ), two SNOSBPL8-ZDHHC17 region  and rs17259942 was 18 kb downstream RPL21P98 and 43 kb upstream RPL7P43. The other nineteen SNPs confirmed previously reported gene regions, including twelve SNPs 8-45 kb downstream LPL, two SNPs 0.5-11 kb upstream CETP, and five SNPs in the LIPG-ACAA2 region (39.812 kb downstream LIG and 65.963 kb upstream ACAA2) [LPL, CETP and LIPG were reported to be associated with HDL-C in four recent GWAS reports [ACAA2 was reported in [CETP (rs1800775) was the most significant effect (p = 8.61 × 10-34) in this study.For HDL-C, twenty SNPs with additive effects reached genome-wide significance Table . SNP rs1 ZDHHC17 . Accordi ZDHHC17 , the OSBQQ plot for single SNP tests on TC and HDL-C showed that p-values of significant results all deviated from the expected p-values under the null hypothesis Figure .LMBRD1 (chr06)  Figure  interactfined in  or genomAmong the seven different pairs of gene regions with epistasis effects, four pairs had A × A effects, one pair had A × D effect, and two pairs had D × D effects Table . For theCELSR2 and ABCG5 reported in [RAB3GAP1-R3HDM1-LCT-MCM6 region with the SNP in LCT being the most significant. Six of these seven effects had p-values for LDL-C in the range of 0.007-0.056 from a meta-analysis  while all the other SNPs confirmed previously reported gene regions, although only seven of the twenty significant SNPs for HDL-C were reported previously (Table OSBPL8 encodes a group of intracellular lipid receptors and suppresses ABCA1 [ABCA1 was found to affect HDL-C level [LIPG-ACAA region (p > 0.55) but confirmed the other effects. In light of different samples and different methods of data analysis between our study and those in previous reports, the confirmations of gene results we observed for TC and HDL-C should be considered strong confirmations. This study used log-transformed TC and HDL-C values while recent GWAS on TC [The single-locus results in this study had strong confirmations with existing studies. For TC, we confirmed orted in ,8. Theseorted in ,8. We denalysis , and genotype × genotype interactions.The FHS GWAS data (version 2) had 6575 individuals with SNP genotypes of the 500 k SNP panel from dbGAP. Of the 6575 individuals, 6431 had observations on TC and 6078 individuals had observations on HDL-C. A total of 496,858 SNP markers had known chromosome locations and 432,096 of these SNP markers with minor allele frequencies greater than or equal to 0.01 were analyzed.-8 was used as the threshold p-value to declare genome-wide significance [-1 = 3.266 × 10-13. This 5% significance level is equivalent to "significant linkage" defined in [Original TC and HDL-C observations deviated from normality and had outliers Figure . The Boxificance . To asseificance . Each pafined in . Since tfined in . In addifined in  was usedfined in  and NCBIfined in  based onGWAS: genome-wide association study. SNP: single nucleotide polymorphism. TC: total cholesterol. HDL-C: high-density lipoprotein cholesterol. LDL-C: low-density lipoprotein cholesterol. LD: linkage disequilibrium. HWD: Hardy-Weinberg disequilibrium. A × A: additive × additive epistasis effect. A × D: additive × dominance epistasis effect. D × A: dominance × additive epistasis effect. D × D: dominance × dominance epistasis effect. GLS: generalized least squares.The authors declare that they have no competing interests.In Silico replication using the InCHIANTI cohort. YD coordinated this research and prepared the manuscript with LM. All authors read and approved this manuscript.LM processed the FHS data, implemented the data analysis and prepared the manuscript with YD. JY contributed to the data analysis and manuscript preparation. HBR directed the parallel computing implementation of the GWAS analysis. TT, LF and SB conducted the The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/11/55/prepub


Cancer cell lines are used extensively in various research. Knowledge of genetic alterations in these lines is important for understanding mechanisms underlying their biology. However, since paired normal tissues are usually unavailable for comparison, precisely determining genetic alterations in cancer cell lines is difficult. To address this issue, a highly efficient and reliable method is developed. Aims: Establishing a highly efficient and reliable experimental system for genetic profiling of cell lines.A widely used breast cancer cell line, MCF-7, was genetically profiled with 4,396 single nucleotide polymorphisms (SNPs) spanning 11 whole chromosomes and two other small regions using a newly developed high-throughput multiplex genotyping approach.The fractions of homozygous SNPs in MCF-7 (13.3%) were significantly lower than those in the control cell line and in 24 normal human individuals . Homozygous SNPs in MCF-7 were found in clusters. The sizes of these clusters were significantly larger than the expected based on random allelic combination. Fourteen such regions were found on chromosomes 1p, 1q, 2q, 6q, 13, 15q, 16q, 17q and 18p in MCF-7 and two in the small regions.These results are generally concordant with those obtained using different approaches but are better in defining their chromosomal positions. The used approach provides a reliable way to detecting possible genetic alterations in cancer cell lines without paired normal tissues. Cancer cell lines have been used extensively in various cancer research. Knowledge of genetic aberrations in these cell lines is important for understanding mechanisms underlying biological behaviors of the cells including their responses to drugs. However, a large portion of genetic alterations such as those caused by loss of heterozygosity (LOH) and chromosomal segment amplification may not be precisely determined without comparing with the genotypes of paired normal tissue, which usually are unavailable.MCF-7 is one of the widely used cancer cell lines. It was established 30 years ago from a p2468Extensive chromosomal aberrations in the MCF-7 genome were identified with CGH.–1013–15 1016et al.[et al.[The advent of high-throughput SNPs genotyping technologies has made it possible to use single nucleotide polymorphism (SNP) markers for detecting LOH and/or amplification of chromosome segments. Bignell et al. and Huanl.[et al. used AffRecently we developed a novel high-throughput SNPs genotyping system.–25 With μg/ml streptomycin at 37°C with 5% CO2 and 95% humidity. Genomic DNA was extracted with the TRIzol Reagents kit  according to the manufacturer's instructions and quantified by spectrophotometry. DNA of a control cell line, NB00637, which originated from a female Caucasian, and DNAs from 24 unrelated individuals from four ethnic groups, African American, Caucasian, Chinese and American Indian, six each, were purchased from the Coriell Cell Repositories The MCF-7 cell line was kindly provided by Drs. William N. Hait/Jin-Ming Yang laboratory, which was originally from the laboratory of Dr. Kenneth Cowan of the Eppley Institute for Research in Cancer and Allied Diseases . The cell line was maintained in RPMI 1640 media containing 10% FBS, 100 units/ml penicillin, and 100 http://www.ncbi.nlm.nih.gov/SNP/index.html) maintained by the National Center for Biotechnology Information (NCBI). To use a two-color fluorescence (Cy3 and Cy5) labeling system for genotype determination, only transition SNPs (A/T to G/C changes or vice versa) were selected. A panel of 4,396 SNPs spanning 11 entire chromosomes and two regions on other chromosomes [SNPs were selected from the dbSNP database (omosomes  were incomosomes 24Primers and probes were designed using the software developed in our laboratory. For eachμM in a well of a 384-well plate. Probes were then spotted onto the washed glass slides by an OminGrid Accent microarray spotter (GeneMachines) under a humidity within a range of 50% to 55% at a temperature within a range of 22°C to 25°C. Each array consisted of 4 × 4 subarrays with 20 × 16 spots in each subarray. The total number of spots on the array was 5,056 including 4,602 SNP probes [Glass slides used for microarray were prepared according to the procedure described previously. Briefly,P probes , 246 negμl solution containing 1× PCR buffer , 250 μM dNTPs (Invitrogen), 627-1172 pairs of primers (20 nM of each) for each multiplex group, 7.5 units of HotStar Taq DNA polymerase (Qiagen Inc.) and 50ng DNA. PCR samples were first heated to 94°C for 15 min to activate the Taq DNA polymerase followed by 40 PCR cycles. Each PCR cycle consisted of 40 sec at 94°C for denaturation, 2 min at 55°C followed by 5 min of ramping from 55°C to 70°C and 10 sec at 70°C for annealing and extension. A final extension was carried out at 72°C for 3 min after the cycles. PCR amplifications were performed in a T3 Thermocycler (Biometra). ssDNA was generated with 1.5 μl of the multiplex PCR product from each group, one primer-probe for each SNP in 30 μl PCR solution as described above, and 45 thermal cycles consisting of 30 sec at 94°C for denaturation, and 1.5 min at 55°C followed by 3 min of ramping from 55°C to 70°C and 10 sec at 70°C for annealing and extension.PCR was performed in a 30-μl of hybridization mixture containing 50 μl of ssDNA (10 μl from each group) and 35 μl of hybridization solution  was added to cover a microarray under a cover slip. The slide was sealed in a Hybridization Chamber , and the chamber was then soaked in a 56°C water bath for three hours. The slide was then taken out and washed at 56°C with 1× SSC and 0.1% SDS for 10 min, twice with 0.5× SSC for 30 sec and twice with 0.2× SSC for 30 sec.Eighty-five μl of labeling solution containing 10.2 μl of Sequenase buffer (supplied by the Sequenase vendor), 32 units of Sequenase , and 750 nM of Cy3-ddUTP and Cy5-ddCTP under a cover slip. Then, the slide was sealed in the chamber, immediately soaked in a 70°C water bath for 10 min, and then was washed again under the conditions used after hybridization as described above. After drying, the microarray slide was scanned by a GenePix 4000B microarray scanner (Axon Instruments) at 10 micron per pixel, 100% power and 480 to 560 PMT gain.The slide was spun to dry as above. The array was covered by 80 The microarray images from scanning were digitized with the computer program, GenePix Pro (Axon Instruments). Genotype calls for each SNP were determined from signal intensity data by a computer program developed in our laboratory as described previously. After no2 = 185.66, P<0.001) reflects a substantial genetic difference between the two cell lines.Out of the 4,396 SNPs included in the study, 4,172 (94.9%) could be detected from the MCF-7 cell line  and 4,142 = 188.74, P<0.001).Results from the MCF-7 cell line were also compared with those obtained from typing 24 human individuals from four ethnic groups  analyzed with SNPs from Group I ∼ III covering nine chromosomes . The average fraction of heterozygous SNPs was 26.3% for the four ethnic groups and 27.6% for Caucasian group alone, to which the donors of the two cell lines belong. The difference in fractions of heterozygous SNPs in the three multiplex groups between MCF-7 and the Caucasian panel analyzed was also significant .Expected Size of Homozygous Zones in the Human Population. To further explore the cause of the differences between the fractions of heterozygous SNPs in different cell lines and in the human individuals, we examined the allelic states of the SNPs on individual chromosomes. Chromosomes with the fraction of heterozygous SNPs in MCF-7 significantly smaller than those in the control cell line and in the Caucasian individuals could be affected by LOH or amplification. As shown in For most cancer cell lines, it is difficult or impossible to obtain the paired normal tissue from the donors. To identify chromosomal regions possibly affected by loss of heterozygosity, we developed an effective method based on both statistic and genetic considerations. First, we assume that there is no linkage disequilibrium (LD) between the SNPs included in the present study. Genetically, when markers are in LD that is often the case when markers are located closely, certain alleles of the SNPs are associated from generation to generation in the population. Since SNPs used in the present study were 220 to 500 KB apart, and LD is usually not observed between markers separated by such distances, the alleles of the SNPs selected in the present study should be random combined in the human population. When the frequencies of the alleles of a group of SNPs in a given human population is known, the fractions of the homozygous and heterozygous individuals with respect to these SNPs can be estimated in this population. Statistically, the maximal number of the clustering homozygous SNPs is a function of the fraction of homozygous SNPs in the population and the number of SNPs in the regions under consideration, and can be calculated based on binomial distribution. Although the mathematical approach for the calculation was described by Philippou and Muwafi in 1982, since thSince the MCF-7 donor was a Caucasian, the average fraction of homozygous SNPs, which is 72.4% in the Caucasian population, was used for the analysis. Because only SNPs on the same chromosome may form homozygous SNP clusters or Homozygous Zones (HZs), the maximal size of HZs that may be found based on random combination was calculated for each chromosome separately based on the number of SNPs used in the present study. As defined by the simulation method, the maximal number homozygous SNPs in each cluster from random combination is calculated based on a probability of 0.01. Results are listed in Fourteen HZs were found with size greater than the maximum number of homozygous SNPs expected based on random combination on nine chromosomes, 1, 2, 6, 13-18, 20 and X in the MCF-7 cell line  and noneet al, unpublished data). No HZ of comparable size was found from the 16 samples. Therefore, the HZ with 397 SNPs and 2.24 Mb on chromosome 21 in MCF-7 should be considered as nonrandom. Since the HZ identified on chromosome 21 extends to the end of the 2.5-Mb region, it is likely that the entire HZ could be larger than what we can learn from the present study. Four SNPs in this zone were detected as heterozygous. However, we have shown that a significant portion (up to 4.4%) of the SNPs in the version of database used for SNP selection may not be real polymorphism site and may be paralogous sequence variants , the percentage (4/430 = 0.93%) of heterozygous SNPs in this HZ are within this range. The positions and distribution of all HZs in MCF-7 are shown in The analyzed regions on chromosomes 21 and X were small  with very high SNP densities . Since closely located markers are often found in LD, the assumption that markers are randomly associated may not be applied to these two regions. Therefore, the HZ in these regions may not be defined by the criteria described above. However, the entire examined region with 47 homozygous SNPs spun 1.8 Mb on chromosome X and a region with 397 affected SNPs covered a major portion (2.24 Mb) of the 2.5-Mb region on chromosome 21. Based on the data reported, the size of the haplotype blocks (genomic regions with markers in LD) in the human genome is estimated to be ∼8 kb.–32 The 1et al[In most cancer genetic studies, genetic alterations in the tumor cells can be identified by comparing the genotypes of tumor cells with those for paired normal tissue. However, it is difficult or impossible to obtain paired normal tissue for many of the cancer cell lines, especially for those that were established a long time ago. Negrini et al reportedIt should be pointed out that HZs larger than expected may not be necessarily caused by LOH or chromosomal segment amplification. The presence of HZs with sizes significantly greater than expected random combination in the human population were first reported by Broman and Weber. The authMore intensive analysis using results from the Human HapMap Project was reported very recently. Their reThe presence of significantly more HZs in the MCF-7 cell line than in the human population indicates that it is very likely that the majority of these HZs are the results of LOH. Our results are further validated by comparing with those reported by others using other methods. Most of the regions of DNA loss or chromosomal deletion identified in the literatures were similar to what we identified in the present study. For example, LOHs of 1p , 1q (215.54 - 245.04 Mb), 6q (157.37 – 170.64 Mb), 13 (18.45 - 112.85 Mb), 15q (19.84 - 52.43 Mb), 16q (29.58 - 54.94 Mb and 57.24 – 66.71 Mb), 18 (nearly whole chromosome loss), and 21q (38.02 – 40.52 Mb) were reported by several studies using CGH41015 and ka2Comparing the results from CGH studies on MCF-7 reported by others, several HZs found from our study are of particular interest. Two HZs on 17q (27.06 - 49.35 Mb and 69.51 - 80.48 Mb) detected in our study were shown to be amplified by others using CGH. Althoug915Discrepancies were found between our results and those reported previously. HZs on 17p in the MCF-7 cell line was reported by other studies15 using 14414Unlike CGH that can be used to determine DNA copy number (loss or amplification) changes but can't discriminate copy number changes of different alleles, genotype analysis can be used to detect both LOH38 and am21In summary, since many cell lines such as MCF-7 are widely used in biomedical research, learning the genetic backgrounds of these lines and the genetic variation within each cell line from different sources is very important to understand experimental results on a genetic basis and for interpret experimental data correctly. Our system allows genetic profiling of widely used cell lines with a high marker density on a periodical basis to minor genetic changes occurring during passages and/or maintenance in different laboratories.


Macaca fascicularis (cynomolgus or longtail macaques) is the most commonly used non-human  primate in biomedical research. Little is known about the genomic variation in cynomolgus macaques or how the sequence variants compare to those of the well-studied related species, Macaca mulatta (rhesus macaque). Previously we identified single nucleotide polymorphisms (SNPs) in portions of 94 rhesus macaque genes and reported that Indian and Chinese rhesus had largely different SNPs. Here we identify SNPs from some of the same genomic regions of cynomolgus macaques  and compare them to the SNPs found in rhesus.M. fascicularis SNPs were overlapping in both species. The majority (70%) of the SNPs found in both Chinese and Indian rhesus macaque populations were also present in M. fascicularis. Of the SNPs previously found in a single rhesus population, 38% (Indian) and 44% (Chinese) were also identified in cynomolgus macaques. In an alternative approach, we genotyped 100 cynomolgus DNAs using a rhesus macaque SNP array representing 53 genes and found that 51% (29/57) of the rhesus SNPs were present in M. fascicularis. Comparisons of SNP profiles from cynomolgus macaques imported from breeding centers in China (where M. fascicularis are not native) showed they were similar to those from Indochina.We sequenced a portion of 10 genes in 20 cynomolgus macaques. We identified 69 SNPs in these regions, compared with 71 SNPs found in the same genomic regions of 20 Indian and Chinese rhesus macaques. Thirty six (52%) of the M. fascicularis and M. mulatta, suggesting that the relationship of these two species is closer than that suggested by morphological and mitochondrial DNA analysis alone. These findings indicate that SNP discovery efforts in either species will generate useful resources for both macaque species. Identification of SNPs that are unique to regional populations of cynomolgus macaques indicates that location-specific SNPs could be used to distinguish monkeys of uncertain origin. As an example, cynomolgus macaques obtained from 2 different breeding centers in China were shown to have Indochinese ancestry.This study demonstrates a surprisingly high conservation of SNPs between  M. fascicularis) is used widely in biomedical research, advancing the study of simian immunodeficiency virus (SIV) pathogenesis [M. fascicularis genome sequence, gene expression arrays and a SNP map are not yet available to advance complex trait analysis in cynomolgus macaques.The cynomolgus macaque  . The cynThe current range of the rhesus macaque extends from Eastern China through Western India and Pakistan. Within this territory, Indian and Chinese rhesus sub-populations have diverged into genetically distinct subpopulations, as assessed by both mtDNA analysis  and MHC M. fascicularis and M. mulatta co-exist in a limited geographic range in Mainland SE Asia. The precise evolutionary history of the macaques in the overlapping range is uncertain. For example, mtDNA sequence comparisons of both species cluster as discrete haplogroups [logroups ,28, conslogroups -31, and logroups .We recently reported the discovery and analysis of Chinese and Indian rhesus macaques SNPs in portions of 94 genes . Here weM. fascicularis and M. mulatta sequences, we did not identify any fixed base differences that distinguished the macaque species. The only variants detected were polymorphic. There were a total of 69 SNPs in the cynomolgus macaque DNAs, with individual genes having between 2 and 13 polymorphisms  and population distributions of Chinese and Indian rhesus SNPs (63:28). Comparing the s Figure  , as compared with Chinese rhesus (33)  of the SNPs in s Figure . All locs Figure . Compari6/69 of tM. fascicularis geographic populations: Indonesia (39), Mauritius (31), the Philippines (29), and Indochina (31) , Indonesia (20), Mauritius 23), the Philippines (16) and breeding centers in China (16) were genotyped using a SNP array containing previously identified rhesus macaque polymorphisms within the 3'ends of genes [, the Phi5, IndoneM. fascicularis, however no heterozygous genotypes were detected among the 41 macaques screened from these populations. The genotypes from both the SNP assay and direct sequencing were concordant at this locus.The genotypes of two SNP loci, MAOA:116 and CCR1:641, are of particular interest. MAOA:116 alleles are largely fixed in the rhesus population, with the C-allele being found in Indian rhesus and the G-allele being found in Chinese rhesus . In the M. fascicularis from China.We also used the SNP array to compare the genotypes of cynomolgus macaques from known geographic ancestry to those of animals imported from two breeding centers in China. Of the 12 SNPs in this assay that were only detected among the 25 Indochinese macaques, 11 were also found in the 16 animals obtained from China. The average minor allele frequency of these 12 specific SNPs was 0.19 in the Indochinese animals and 0.16 in the M. fascicularis are also present in M. mulatta. The finding indicates that efforts to identify SNPs in either species will be beneficial in generating resources for both macaque species. The recent publication of the rhesus genome sequence has sparked interest in developing genome-wide SNP arrays for use in biomedical research [M. fascicularis DNAs, which based upon this analysis, would capture about 50% of the genomic SNPs in cynomolgus macaques.We found that approximately half of the SNPs identified in research . Such a M. fascicularis, suggesting that genetic differences between the populations could underlie unrecognized phenotypic differences between these animals. Since genetic heterogeneity can complicate the reproducibility of results in biomedical studies, it would be prudent to use animals from only a single population of M. fascicularis in a single research study. Towards this goal, population-specific SNPs could be used to verify the ancestry of a research animal when it is uncertain. As example, the population-specific SNPs from this study identified cynomolgus macaques from two breeding centers in China as being of Indochinese descent. An expanded SNP discovery effort would readily identify more population markers, making it possible to identify hybrid M. fascicularis as well.In this sampling, we identified 36 SNPs that appeared unique to a single geographic population of M. fascicularis overlap with those found in M. mulatta is remarkable, given that previous analysis showed that only 31% of SNPs in rhesus macaques are shared by both the Indian and Chinese subpopulations [M. fascicularis include ones that are private to Chinese or Indian rhesus, as well as those that are shared between Indian and Chinese populations. While there are more SNPs from the Chinese rhesus than Indian rhesus macaques present in cynomolgus macaques, there are about twice as many SNPs present in the Chinese rhesus population as whole. An evolutionary bottleneck that reduced the overall genetic diversity of the Indian rhesus macaque has been proposed [The finding that approximately half of the SNPs found in ulations ,11. The M. fascicularis and M. mulatta could represent that the most ancient SNPs in the fascicularis group, those that predate the divergence of rhesus and cynomolgus macaques. Consistent with this idea, the majority of SNPs found in both Indian and Chinese rhesus macaques were also found in M. fascicularis. Interestingly, although mtDNA analysis supports the divergence of these two species 1.8 MYA [There are several possible explanations for the high percentage of shared variants in these two macaque species. The SNPs identified in both  1.8 MYA ,33, the  1.8 MYA -31. Our M. mulatta and M. fascicularis SNPs. The Y-chromosome sequence studies of Tosi et al. suggest interspecies hybridization, though only within the current overlapping range of Chinese rhesus macaques and Indochinese cynomolgus macaques [M. fascicularis does not explain all of the overlapping SNPs, since there is also evidence of Indian-specific rhesus variants in M. fascicularis. Due to the geographic barriers that separate India and Indonesia, SNPs common to both of these populations are more likely a consequence of either sequence conservation or convergent evolution.It is possible that relatively recent gene flow between these two macaque species has contributed to the high rate of overlap between macaques ,31. HoweM. fascicularis and M. mulatta. Possible evidence of selective pressure can be found within this study. By both direct sequence comparisons and SNP array genotyping, we found that one SNP locus (CCR1:641) had a high minor allele frequency (0.41) in the macaques derived from Indochina and Chinese breeding center, and yet no heterozygous individuals were detected, a striking departure from Hardy-Weinberg equilibrium. This finding could be the consequence of inadequate sample size, or a technical issue that was resolved neither by direct sequencing nor by the SNP array. However it is also possible that a heterozygous genotype at CCR1:641, or at alleles tightly linked to this locus, is associated with decreased survival. This is not implausible, since CCR1 encodes the chemokine receptor 1 protein, which is involved in leukocyte recruitment in response to pathogenic infections [Selective pressure to maintain some of the sequence variants could have contributed to the retention of some SNPs in both fections .M. fascicularis. The allele present in > 99% of Indian rhesus (C) was also found in almost all of the M. fascicularis, with the only exceptions being a few individuals from the Indochinese population. This skewed presence of the Indian rhesus allele in the M. fascicularis animals is striking. The MAOA gene encodes monoamine oxidase A, a protein that is involved in the breakdown of neurotransmitters, including norepinephrine and serotonin. Some alleles of MAOA are thought to influence aggressive and impulsive behaviors in primates [MAOA alleles in varying macaque populations or environments.The MAOA:116 locus has fixed alleles in the Indian and Chinese rhesus macaque populations and was included in this study of primates . PerhapsM. fascicularis and M. mulatta, and thus there is no direct evidence of gene replacement between species. Based upon the morphological and anatomical differences between the macaques, one would expect some gene replacement to be present. Additional sequencing of larger regions of genomic DNA will be needed to resolve the rate of allele fixation between these two macaque species.There were no fixed alleles detected in this study that distinguish M. fascicularis are also found in M. mulatta. The high rate of overlap suggests that the evolutionary relationship of rhesus and cynomolgus macaques may be closer than that suggested by previous morphological and mtDNA analysis. It also indicates that future SNP discovery efforts in either macaque species will generate information that will be useful for both species. Future efforts to identify cynomolgus SNPs would not only advance genetic research in this widely used animal model, but would also generate tools for verifying the origins of animals for studies where ancestry is important.We found that 52% of the SNPs identified in M. fascicularis DNAs used in this study were obtained from at least 2 sources for each geographic region: 25 from Indochina ; 20 from Indonesia ; 23 from Mauritius ; 16 from the Philippines  Manila, Luzon island; Jan Vacek, Located in ILOILO, on the Island of Panay.); 16 from China .The 10 gene regions chosen for direct DNA sequence analysis were selected from those previously analyzed in rhesus macaques [A custom SNP array was used to genotype 64 previously identified rhesus SNPs, using iPLEX reagents and protocols for multiplex PCR, single base primer extension and generation of mass spectra in accordance with the manufacturer's instructions . The multiplex reactions included 28, 17, 12 and 7 primer sets.All SNPs identified in this work were deposited in dbSNP and monkeySNP databases ,37.M. fascicularis DNAs and perspective on the project design. RG provided a diverse set of cynomolgus DNAs, as well as insightful review of the results. BF was responsible for the project design, the data analysis and the manuscript preparation. All authors read and approved this manuscript.BF and RCK conceived of this study. SLS preformed all DNA sequence and statistical analyses and contributed to the writing of the manuscript. RCK provided SNP identity and NCBI accession numbers. This file lists the SNPs identified in this study and the associated gene. The NCBI STS (reference sequence) accession numbers for each are shown.Click here for fileM. fascicularis and M. mulattoSNPs identified through sequencing in 10 genomic regions of . All SNPs identified in 10 genomic regions by sequencing of 20 M. fascicularis and 20 M. mulatta are shown at the top. Loci highlighted in green showed no variant allele in that population; yellow highlight indicates a SNP was detected, and red indicates that only the minor allele of the SNP variant was detected in the population.Click here for fileSNP assay gene summary. The fifty-three genes represented in the SNP genotype assay are listed, along with chromosome location. Those genes that were also analyzed by direct sequencing are noted.Click here for fileM. fascicularisSequence information for . The primer information and reference sequences for the ten gene regions sequenced in M. fascicularis are listed.Click here for file


We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r2) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to  Genome-wide association studies have led to the identification of susceptibility loci for a variety of human complex traits. What is still largely missing, however, is the understanding of the biological context in which these candidate variants act and of how they determine each trait. Given the localization of many GWAS loci outside coding regions and the important role of regulatory variation in shaping phenotypic variance, gene expression has been proposed as a plausible informative intermediate phenotype. Here we show that for a subset of the currently published GWAS this is indeed the case, by observing a significant excess of regulatory variants among disease loci. We propose an empirical methodology (regulatory trait concordance—RTC) able to integrate expression and disease data in order to detect causal regulatory effects. We show that the RTC outperforms simple correlation metrics under various simulated linkage disequilibrium (LD) scenarios. Our method is able to recover previously suspected causal regulatory effects from the literature and, as expected given the nature of the tested tissue, an overrepresentation of immunity-related candidates is observed. As the number of available tissues will increase, this prioritization approach will become even more useful in understanding the implication of regulatory variants in disease etiology. The biological interpretation of genome-wide association study (GWAS) signals As sample sizes increase, allowing the discovery of larger numbers of eQTLs of smaller effect size and as the expression experiments will be performed in a larger variety of tissues, we can envisage that almost every gene will have an associated eQTL under a certain condition. Consequently, the probability that any of these will map to a genomic region where a GWAS SNP also resides is very high. Therefore, it is important to emphasize that while it is very tempting to infer potential causal mechanisms based on such overlaps, this would be a naïve inference in the absence of additional supporting evidence for causality. In the long run, this will not only be an issue for gene expression, but also for any other cellular phenotype. Association studies for intermediate phenotypes with possible relevance to complex traits are underway and their results will overlap some of the GWAS signals. The biological meaning of these overlaps will again need to be evaluated in the context of the genome's correlation structure.It is not evident though how to model each genomic region with overlapping association signals in the absence of information about the history of the region. Accounting for the historical parameters of a region under the coalescent, while desirable, is computationally and practically not feasible since the human population history is too complex to properly model and small deviations or slightly incorrect assumptions could create false signals or reduce power.cis and trans and we also show that the correlation strength  between the GWAS SNP and the eQTL is not a sufficient predictor of regulatory mediated disease effects.In order to distinguish such accidental colocalizations cis) and distal (trans) effects as follows: variants within 1Mb on either side of the transcription start site (TSS) of a gene are considered to be acting in cis, while those at least 5 Mb downstream or upstream of the TSS or on a different chromosome are considered to be acting in trans.To identify likely causal effects (not variants since we do not have full sequencing data) associated with complex traits and diseases we took advantage of published association data catalogued in the NHGRI cis and trans effects to those of a random set of SNPs, representing the null. In a QQ plot  to 1000 sets of most significant p-values of 976 random SNPs each (expected). The 1000 random sets of 976 SNPs were sampled to have identical MAF distribution to the GWAS SNPs. In cis, we observe a much stronger regulatory signal in the GWAS data compared to random  = 4. In trans, we also detect a more significant regulatory signal for GWAS SNPs compared to random, however not as strong as in cis. This is to be expected given that the much greater statistical space we're exploring in trans limits the power to detect such effects. Nevertheless, despite their confinement to one tissue type - LCLs, these comparisons support the overall explanatory potential of regulatory variation for the biological effects of GWAS variants. As expected given the tissue nature, the phenotypes responsible for this enrichment are immunity related  and the total number of tested SNPs (NSNPs).Depending on the internal LD structure of the hotspot interval, the correlation between the GWAS SNP and the eQTL will vary, hence so will the P-values after and before correction. One way to assess the relevance of the GWAS SNP to the eQTL is to compare its correction impact to that of all other SNPs in the interval. For this purpose, we define a Regulatory Trait Concordance (RTC) Score for each gene-GWAS SNP combination as follows, taking into account the ranking of the correction with respect to all SNPs in the interval . Given this, the RTC Score will always be in the range  than the GWAS SNP  and the alternative hypothesis . For this purpose, we have simulated causal SNPs (cSNP), eQTLs and dSNPs  and the causal disease SNP (c-dSNP) are different .1 on the other hand, the RTC score is right skewed, with a clear enrichment for values close to 1 recovering the single causal SNP effect  between the eQTL and the dSNP is not on its own sufficient to predict whether they tag the same cSNP  is also not fully predictive of high RTC scores  discovered in the same tissue as the HapMap CEU eQTLs (LCLs) but derived from an independent set of samples: 75 individuals of Western European origin from the GenCord project cis eQTLs in both datasets. Furthermore, we conditioned the associated genes for the same interval to be identical in the two expression datasets, expecting thus a common functional variant. As a result of this filtering, we tested SNPs in 157 hotspot intervals, associated with differential expression levels of 154 genes. As expected from the H1 simulations, the RTC Score distribution after correcting for the GenCord eQTLs is right-skewed  where at least one significant cis eQTL at a 0.05 permutation P-value threshold also resides . Our method confirms prior results in the literature suggestive of disease effects mediated through expression  to more likely show an overlap with eQTLs. In order to evaluate the relevance of our results, we analyzed the distributions of the best RTC Scores per GWAS SNP stratified by the immunity relatedness of the complex trait they associate with . We obsecis-regulatory, using gene expression to determine its downstream targets, coupled with information about the biological pathways these targets act in could help interpret the primary GWAS effect. We investigated this hypothesis in the topmost 50 GWAS intervals ordered by their trans eQTL significance. For each interval, we apply the RTC Scoring scheme on the subset of genes in the whole genome with a notable effect in trans . These signals amount to a total of 552 genes. We obtain SNP-gene combinations passing the 0.9 Score threshold for 24 of the 50 tested intervals . Six of these intervals contain GWAS SNPs associated with immunity related traits  are actually historically correlated (D' = 1). Nevertheless, D' is not very informative either  for each individual genomic interval that assesses the likelihood that the eQTL and the GWAS SNP are tagging the same functional variant. By ranking the effect of the removal of the GWAS SNP in comparison to the outcome for any other SNP in the region and by accounting for the number of SNPs tested, we produce a score comparable across intervals. We evaluate the performance of the score in various simulated LD scenarios and we present its robustness by its expected uniform distribution when the eQTL and GWAS SNP are tagging different functional variants. In comparison, we investigate how well do current SNP correlation metrics  perform on their own. We show that the LD between the GWAS SNP and its colocalizing eQTL is not a good predictor of a shared functional effect. This is very important especially since most of the current replication and follow-up studies only focus on variants highly correlated (r2>0.8) with the initial discoveries. It is important to stress at this point that neither the eQTL nor the GWAS SNP is likely the causal variant. Therefore, what really matters is not the statistical correlation between two proxies but the correlation between each of the proxies and the causal variant, whose frequency is unknown. In any case, no obvious combination of LD measures can substitute the RTC scoring scheme and we thus conclude that many interesting candidate genes would be missed if one were to rely solely on correlation-based approaches.In this paper, we discover causal regulatory effects and their affected candidate genes in cis. Long-range trans effects are also present but less prevalent, possibly due to lower power to detect such associations. As expected given the tissue the expression data was measured in (LCLs), we observe a significant abundance of cis regulatory causal effects for immunity related traits. Our result reinforces the necessity to expand the tissue diversity In this paper, we also explore the explanatory potential of regulatory variation given the currently published GWAS data. We observe a significant overrepresentation of eQTLs among GWAS SNPs, especially affecting genes in cis and in addition, we show the utility of genome-wide expression data irrespective of the nature of the primary SNP effect by predicting clusters of genes affected in trans. The individual examination of the candidates prioritized with our approach will undoubtedly assist the biological interpretation of the ever-increasing list of GWAS signals. As associations with more intermediate cellular phenotypes will be reported, the integration of all these signals will be crucial for understanding the biology of complex traits.By applying the RTC method on the NHGRI GWAS SNPs, we are able to confirm previously suspected regulatory mediated disease effects and discover novel candidate genes affected by GWAS SNPs. We provide a list of follow-up candidate genes affected in RNA levels were measured in lymphoblastoid cell lines (LCLs) derived from the HapMap 3 individuals using a whole-genome expression array  as previously described 2 transformed raw intensity values were normalized as follows: quantile normalization of sample replicates  followed by median normalization across all individuals.The logwww.genome.gov/26525384) and published by 02.03.2009 were downloaded. Of these, only the 976 unique common variants (MAF >5%) genotyped in the HapMap 3 CEU samples were kept for analysis.All SNPs from the catalogue of genome-wide association studies maintained by the National Human Genome Research Institute . For the cis analysis we selected the 130 hotspot intervals where at least one significant cis eQTL and a GWAS SNP colocalize while for the trans, we analyzed a subset of 50 of the total 784 unique intervals (where the 976 GWAS SNPs map to). These are the topmost intervals ordered by their most significant trans eQTL .We mapped all common autosomal CEU HapMap 3 SNPs  to recombination hotspot intervals as defined by McVean et.al. cis and trans GWAS analysis, the best P-value associations per SNP were stored. The set of the most significant P-values of the 976 GWAS SNPs was compared to 1000 sets of most significant P-values of 976 random SNPs. The 1000 random sets of 976 SNPs each were conditioned to have the same MAF distribution as the 976 GWAS set.For both the The QQ plot showing the abundance of regulatory signal in GWAS data is the median QQ plot of 1000  comparisons. It shows the distribution of the −log10 quantile values of the GWAS best associations (observed) versus the median of the corresponding 1000 −log10 quantile values from each of the 1000 random SNP sets (expected). In order to assess the significance of the observed versus expected median QQ plot, we superimpose the upper limit of the 95% confidence interval. This is calculated from the sorted 0.95 quantiles of 10000 pairs of 976 random SNPs each.GWAS SNP) the impact on the eQTL  of the GWAS SNP correction to that of correcting for all other SNPs in the same interval. By taking into account the total number of SNPs in the interval (NSNPs), we can compare this ranking across different genes and intervals. For this purpose we define the regulatory trait concordance (RTC) Score ranked below ranging from 0 to 1, with values closer to 1 indicating causal regulatory effects.We assess the likelihood of a shared functional effect between a GWAS SNP and an eQTL by quantifying the change in the statistical significance of the eQTL after correcting for the genetic effect of the GWAS SNP. We redo the SRC association of the eQTL genotype with the residuals from the standard LR of the “corrected-for” SNP against normalized expression values. We account for the LD structure in each hotspot interval separately by ranking  and the alternative hypothesis .We investigate the properties of the RTC score with respect to different correlation metrics under the null hypothesis (Hcis eQTLs (315 genes at 10−3 permutation threshold) to create a list of causal SNPs (cSNP). For the H0, we call these cSNPs causal eQTL SNPs (c-eQTL). For each c-eQTL, we sample a different causal disease SNP (c-dSNP) from the same interval, with the requirement that its MAF comes from a distribution identical to that of the 976 NHGRI GWAS SNPs. Subsequently, we sample up to five eQTL-dSNP pairs per interval where the eQTLs and dSNPs are the topmost correlated (r2) SNPs with the c-eQTL and the c-dSNP respectively. After sampling, we exclude cases where the eQTL and dSNP are identical, as these contradict the H0..c-eQTL-c-dSNP-eQTL-dSNP quartets mapping to 287 unique hotspot intervals were sampled and tested under H0.We use the HapMap3 CEU 1, we sample up to five eQTL-dSNP pairs for each hotspot interval harbouring a cSNP as follows: the eQTLs are chosen as the top most significant SNPs per eQTL gene - excluding the cSNP; the dSNPs are randomly sampled from the same hotspot interval such that the r2 between each of them and the cSNP is in the range . At any stage of the 5-step iteration per cSNP, the dSNP must be different from the cSNP and the eQTLs sampled up to that point. cSNP-eQTL-dSNP trios mapping to 290 unique hotspot intervals throughout the genome were sampled and tested under the H1.Under the H2) of all pairwise SNP combinations per interval to calculate the median r2, an estimate of the LD extent per region.We use the LD values  detected in LCLs derived from 75 unrelated individuals of Western European origin from the GenCord project To perform a control experiment where the trait is gene expression, we used Dataset S1Cis analysis RTC results. Candidate cis regulatory effects as ranked by the RTC. No score filtering.(0.08 MB XLS)Click here for additional data file.Dataset S2Trans analysis RTC results. Candidate trans regulatory effects as ranked by the RTC in 50 genomic intervals. No score filtering.(0.30 MB XLS)Click here for additional data file.


Allelic variation is the cornerstone of genetically determined differences in gene expression, gene product structure, physiology, and behavior. However, allelic variation, particularly cryptic (unknown or not annotated) variation, is problematic for follow up analyses. Polymorphisms result in a high incidence of false positive and false negative results in hybridization based analyses and hinder the identification of the true variation underlying genetically determined differences in physiology and behavior. Given the proliferation of mouse genetic models  and the wealth of gene expression microarray and phenotypic studies using genetic models, the impact of naturally-occurring polymorphisms on these data is critical. With the advent of next-generation, high-throughput sequencing, we are now in a position to determine to what extent polymorphisms are currently cryptic in such models and their impact on downstream analyses.We sequenced the two most commonly used inbred mouse strains, DBA/2J and C57BL/6J, across a region of chromosome 1 (171.6 – 174.6 megabases) using two next generation high-throughput sequencing platforms: Applied Biosystems (SOLiD) and Illumina . Using the same templates on both platforms, we compared realignments and single nucleotide polymorphism (SNP) detection with an 80 fold average read depth across platforms and samples. While public datasets currently annotate 4,527 SNPs between the two strains in this interval, thorough high-throughput sequencing identified a total of 11,824 SNPs in the interval, including 7,663 new SNPs. Furthermore, we confirmed 40 missense SNPs and discovered 36 new missense SNPs.Comparisons utilizing even two of the best characterized mouse genetic models, DBA/2J and C57BL/6J, indicate that more than half of naturally-occurring SNPs remain cryptic. The magnitude of this problem is compounded when using more divergent or poorly annotated genetic models. This warrants full genomic sequencing of the mouse strains used as genetic models. With the recent completion of the Perlegen/NIEHS mouse resequencing , over teThe onset of next-generation HTS enabled us to obtain full sequence coverage of a region of chromosome 1 in the D2 and B6 mouse strains. There are several platforms for massively parallel DNA sequencing currently on the market , and simThe present analyses were limited to a region of chromosome 1 from 171.6 – 174.6 megabases (Mb). This interval was selected for four reasons. First, it is representative of the genome in that it spans discrete regions of high and low SNP densities. Second, it is a gene dense region containing 79 protein coding genes, 2 retrotransposed genes, and 6 noncoding RNA genes. Third, it harbors numerous quantitative trait loci (QTLs) affecting a wide variety of physiological and behavioral phenotypes . And fiWe report that comparisons utilizing two well-annotated mouse genetic models, D2 and B6, predict that more than half of naturally-occurring SNPs remain unknown or not annotated. These cryptic SNPs lead to a high incidence of gene expression microarray false-positive and false-negative results and lead to failures in identifying allelically-variant genes that can underlie QTL phenotypic effects.Using 32 PCR probes spanning the 171.6 – 174.6 Mb interval of chromosome 1, we screened a commercially available D2 strain bacterial artificial chromosome (BAC) library, MM_DBa. End-sequencing of the BACs identified by these PCR probes allowed us to assemble a minimal overlapping contig of 27 D2 BACs  of those mapping to the chromosome 1 target interval. SOLiD generated 32.7 million reads of pooled B6 BACs with 10.5 million (32%) mapping to the chromosome 1 interval. Genome Analyzer generated 14.5 million D2 reads with 9.6 million (66%) mapping to the chromosome 1 interval, and 14.0 million B6 reads with 6.2 million (44%) mapping to the chromosome 1 region. Reads that did not align to the chromosome 1 interval were mostly BAC vector sequence (which was not removed when preparing the DNA), adapters from sequencing chemistry, and bacterial contamination. These are template specific problems and do not reflect upon differences between the sequencing platforms. Additional purification steps would have resulted in fewer unaligned sequences; however, the aligned sequences provided sufficient high read depth coverage, so the exclusion of the unaligned sequences did not adversely affect the present analyses.Deep sequencing was achieved, with average read depths using the Genome Analyzer of 75 and 51 for D2 and B6, respectively, and 108 and 85, respectively, using SOLiD, for reads aligning to the chromosome 1 interval , we nonetheless identified a small number of discrepancies in the realignments to the reference sequence. Upon assembly, the Applied Biosystems and Illumina realignments of the B6 sequence reads differed from the B6 reference sequence for 41 and 60 residues Table . SubsequIn order to compare the SOLiD and Genome Analyzer platforms under optimal conditions for each platform and to compare in a manner the majority of end-users are likely to employ, the manufacturers used parameters determined to be optimal for SNP calling on their particular platform. The Illumina SNP calling method (Maq) relies upon quality scores and applies a filter based on these qualities, read depth, and neighboring SNPs in order to discriminate between a SNP and a base-calling error. Applied Biosystems calls SNPs based on two-base encoding in color-space allowing for more sensitive discrimination between SNPs and base-calling errors. Illumina's method produced fewer no calls (Ns) than Applied Biosystems' method based upon realignments performed by each vendor Table . No nuclCurrently, the Mouse Phenome Database (MPD), which includes dbSNP and Perlegen data among other resources, offers the most inclusive SNP queries for mouse strains, including comparisons of the D2 and B6 strains (see Methods for further explanation of SNP Databases). While, MPD currently annotates 4,527 D2 vs. B6 SNPs in the chromosome 1 interval (171.6–174.6 Mb), custom HTS identified 11,824 SNPs Figure  for the The SOLiD and Genome Anlayzer datasets were merged for subsequent comparisons. Our results confirmed 4,161 (92%) of the D2 vs. B6 SNPs currently annotated in the MPD public dataset within the chromosome 1 interval, while 236 (5%) of the SNPs reported in MPD for this interval were determined to be false-positive SNPs. The 130 remaining either lie in gaps in our realignments or had ambiguous  calls in the sequence data. Our results identified numerous SNPs not previously annotated in MPD. In fact, our results identified 7,663 new SNPs, more than doubling the number of D2 vs. B6 SNPs found in this chromosome 1 interval.This chromosome 1 interval spans a clear haplotype break, resulting in a SNP sparse region 171.6–172.9 Mb) and a more distal SNP dense region (172.9 – 174.6 Mb) , Fcgr3 (A81S), Itln1 (Y133F). The other two had ambiguous coverage in our alignments and could not be evaluated: Fcgr3 (G72W) and Itln1 (N162K).We assessed non-synonymous SNPs that result in amino acid changes in proteins because of their clear impact on protein activity. Based on our custom HTS, we found 76 missense (non-synonymous coding) PARC SNPs that changed an amino acid residue between the D2 and B6 strains. 36 of these were new missense SNPs  for the Affymetrix Exon 1.0 ST gene expression microarray platform. For chromosome 1 (171.6 – 174.6 Mb), there are 8201 probes and 2126 probesets on the Affymetrix Mouse Exon 1.0 ST array, and we identified 861 probes that spanned at least one SNP encompassing 480 probesets or 23% of the probesets that interrogate this interval (unpublished data). Thus, compared to publicly available D2 vs. B6 SNPs, custom HTS identified 60% more probesets that span SNPs.We report that comparisons utilizing even two of the most commonly used mouse genetic models, D2 and B6, predict that more than half of naturally-occurring SNPs remain unknown or not annotated. This is particularly striking given that the present comparison is between the B6 strain, upon which the mouse reference is based , and theWe compare two methods for next-generation HTS. By sequencing the same templates on both Applied Biosystems' SOLiD and Illumina's Genome Analyzer sequencing platforms, we determined that the platforms offer comparable results at a high read depth. More SNPs were called by Illumina/Maq than by Applied Biosystems, but because the vendors used independent mapping and SNP calling approaches, differential results are likely due to differences in the analysis pipelines, as no nucleotide bias in the frequency of the calls made by the Genome Analyzer and SOLiD was apparent.Mouse models are an invaluable tool for identifying allelic variation that contributes to genetically determined differences in physiology and behavior. However, allelic variation is problematic for follow-up analyses, and removing technical bias resulting from naturally-occurring sequence variation is critical. Previously, we illustrated the impact of SNPs on gene expression microarray analyses  and arguUsing 32 chemiluminescent labeled PCR probes designed across a 3 Mb region of chromosome 1  as per standard protocol . BrieflyEquimolarly pooled BAC DNA samples were sent to Applied Biosystems as per their requirements: 49.7 ug of B6 BACs and 69.3 ug of D2 BACs. Applied Biosystems prepared two fragment libraries for sequencing from each pool of genomic DNA via standard methods on their SOLiD platform . In shorin silico using CLC Sequence Viewer 5 [Non-synonymous SNPs were computed using the Ensembl CDS sequence from UCSC for each transcript. Any SNP falling within coding region and the exon boundaries were used to convert the B6 allele into the D2 allele predicted by the realignments. The coding sequences were then reassembled and translated Viewer 5 . The amiStriatal tissue from D2 (n = 8) and B6 (n = 8) males and females was dissected and total RNA isolated using standard Trizol (Invitrogen) protocol. 3 μg of total RNA was pooled (n = 8) for each strain and sent to Illumina for library construction and standard transcriptome sequencing. The realignment was performed using ELAND with 2 errors allowed in the first 32 bases of a read. Because we had incomplete read coverage for lowly expressed genes, strain-specific sequence results were analyzed for highly expressed genes only. So this dataset offers confirmation of some of the SNPs detected in our DNA sequencing, but did not achieve complete transcriptome coverage.1. Mouse Phenome database (MPD). The MPD SNP collection [llection  contains2. dbSNP Build 128. dbSNP is maintained at NCBI [ at NCBI  and incl3. Ensembl variation 53. The Ensembl SNP dataset is queried using Biomart [ Biomart  and primAll of the PARC SNPs discovered from both Applied Biosystems' SOLiD and Illumina's Genome Analyzer sequencing pipelines have been deposited in dbSNP at NCBI under the Handle PARC_SEQ as Computational SNPs (ss#119994841-120015816). D2 BAC end sequence has been submitted to GenBank as GSS (genome survey sequences). Raw sequencing data for SOLiD sequencing has been submitted to GenBank in the SRA (short read archive) without intensity data. Raw sequencing data for the Illumina sequencing is available upon request; however this data was generated before SRA standards were established, and the specific raw files needed for SRF format are no longer available.The authors declare that they have no competing interests.All authors assisted with the concept and design of the study. All of the BAC library screening and sample preparation was conducted by NW, as well as primary manuscript writing. DB analyzed the SOLiD and Illumina sequencing results and assessed the protein coding changes. TL and MM conducted the overlay of the SNPs onto annotated sequences and probes for SNP masks. PD and NW analyzed the SNP lists and masks and deposited data. RS, CH, SKM, RH, and KJB contributed to discussions and manuscript edits. All authors read and approved the final manuscript.D2 BAC probe primer sequences. Custom PCR primer pairs that were used to generate PCR probes to screen MM_DBa BAC library across chromosome 1 region of interest. All primers are listed 5'-3' orientation.Click here for file


After more than 200 genome-wide association studies, there have been some successful identifications of a single novel locus. Thus, the identification of single-nucleotide polymorphisms (SNP) with interaction effects is of interest. Using the Genetic Analysis Workshop 16 data from the North American Rheumatoid Arthritis Consortium, we propose an approach to screen for SNP-SNP interaction using a two-stage method and an approach for detecting gene-gene interactions using principal components. We selected a set of 17 rheumatoid arthritis candidate genes to assess both approaches. Our approach using principal components holds promise in detecting gene-gene interactions. However, further study is needed to evaluate the power and the feasibility for a whole genome-wide association analysis using the principal components approach. It is common in candidate-gene or genome-wide association studies to perform single-gene association analysis. However, after more than 200 genome-wide association studies (GWAS), there have been fewer novel loci identified than expected , possiblp < 0.001) were excluded from all analyses, leading to a final set of 135 SNPs. Before analysis, the computer program MACH [All of our analyses utilized genotype data of the 868 cases and 1194 controls in the NARAC data set. Analyses were carried out on a set of 17 candidate genes, selected on the basis of a literature search. The candidate genes used in these analyses are listed in Table glk be the number of minor alleles at SNP k for lth subject, l = 1, ..., N, k = 1, ..., K. 2) Calculate the correlation matrix R, where Rij = cor and gi and gj represent the genotypes of all subjects for SNP i and SNP j, respectively. 3) Decompose R by singular value decomposition: R = AΛAT 4) Determine the factor loading by GA, where G is the standardized N × K matrix of genotypes. The standardized genotypes are calculated as: This approach was proposed by Gauderman et al.  to test R and the computer program Haploview [Then, we use PCs that explain at least 80% of the variation as the gene representation to perform a gene-gene interaction analysis, by applying logistic regression to test for interaction between every combination of two PCs. Once significant PC interactions are identified, PC loadings may be used to determine the influence of a specific SNP on the PCs because the loading represents the correlation of a SNP with a component. For better visualization of the gene-PCs and their SNPs position with the LD block plots, we created a graphical display using our own function in the statistical package Murcray et al.  proposedg1, g2) based on the approximate method to screen for epistasis implemented in PLINK [g1 and g2 as 0, 1, or 2, representing the number of minor alleles. A χ2 with 1 degree of freedom is used to test the association between each pair of SNPs. Pairs of SNPs are selected for analysis in Step 2 if they exceed a given significance threshold, p <α*. In our case, we selected α* = 0.05.For each pair of SNPs, we perform a test of association between the two SNPs  and controls (D = 0). An interaction is considered significant when the p-value of interaction  is less than or equal to α/M, where α = 0.05.where Q values [p-value < 5.5 × 10-6 /2 with K = 135 SNPs) compared with the two-stage approach with p-value in the first stage < 0.05 and a p-value in the second stage < 3.2 × 10-5 .Figure Q values . Figure Q-value only. The strongest interactions were observed within the HLA region with TNF-PC3 and VEGFA-PC1 and HLA-C-PC1 and TNF-PC2 . Outside the HLA region we observed two moderate interaction effects involving STAT4-PC5 and C5-PC4, and TNF-PC2 and STAT4-PC5 . Figure STAT4 and C5, and the linkage disequilibrium (LD) blocks within these genes. The STAT4-PC5 interaction contains four SNPs with absolute value of loadings ≥ 0.5 and they represent their own block. The C5-PC4 interaction contains three SNPs with loadings ≥ 0.5, where the two SNPs (rs10760131 and rs10985112) with the two highest loadings, 0.9 and 0.8 respectively, belong to the same block. On the other hand, the two-step approach detected only interactions within the HLA region. The strongest interactions were between SNPs of VEGFA with HLA-C, LTA with HLA-B, and with HLA-C, and HLA-B, with HLA-C. Code to perform these analyses is available from the authors by request.The PC approach detected several PC interaction effects when using We extended two approaches previously used for gene-level tests or gene-environment interaction analysis to screen for gene-gene interactions in 17 candidate genes for RA using the GAW16 NARAC data. In the PC approach we calculated the SNP loadings for each PC and viewed them in the context of the gene LD structure generated using Haploview Figure . This coα* in Stage 1. Recently, a similar two-step method was proposed and shown to be more powerful than a one-step approach [We could not identify any significant interactions using the two-step approach. There are several possibilities, including the elimination of SNPs with low allele frequency, and the choice of approach . FurtherUsing PCs is a promising approach to screen for potential interactions. As shown in our results, it can detect interactions not observed based on SNP-SNP interactions assessed using either a single-step or a two-step approach. Furthermore, the method used to correct for multiple comparison also plays an important role.GAW16: Genetic Analysis Workshop 16; GWAS: Genome-wide association studies; LD: Linkage disequilibrium; NARAC: North American Rheumatoid Arthritis Consortium; PC: Principal components; SNP: Single-nucleotide polymorphismThe authors declare that they have no competing interests.JL carried out the candidate genes selection, programming, and performed the statistical analysis. RT participated in the candidate genes selection and programming. JMB participated in the design of the study and helped to draft the manuscript. MdA conceived of the study, participated in its design and coordination, and drafted the manuscript. All authors read and approved the final manuscript.


Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western Europe , we selected tagging SNPs in five chromosomal regions using ldSelect, Tagger, and TagSNPs. We found that SNP subsets did not substantially overlap, and that the use of trio data did not greatly impact SNP selection. We then tested associations between HapMap genotypes and expression phenotypes on 28 CEU individuals as part of Genetic Analysis Workshop 15. Relative to the use of all SNPs , most subset methods were able to detect single-SNP and haplotype associations. Generally, pairwise selection approaches worked extremely well, relative to use of all SNPs, with marked reductions in the number of SNPs required. Haplotype-based approaches, which had identified smaller SNP subsets, missed associations in some regions. We conclude that the optimal tagging SNP method depends on the true model of the genetic association ; unfortunately, this is often unknown at the time of SNP selection. Additional evaluations using empirical and simulated data are needed. Development and application of methods using linkage-disequilibrium (LD) for single-nucleotide polymorphism (SNP) selection has empowered genetic epidemiologic studies. Tagging SNP selection methods capitalize on the high levels of LD in much of the genome and aim to capture all of the common variation. SNP redundancy can be reduced, allowing for improved information/coverage within the constraints of a fixed budget. Three classes of tagging SNP methods have the following aims: 1) correlate each SNP of interest with a genotyped SNP (pairwise methods), 2) correlate each SNP of interest with a genotyped SNP or a combination of genotyped SNPs (multimarker methods), or 3) explain each haplotype of interest using a set of genotyped SNPs (haplotype-based methods). Investigators commonly select tagging SNPs using data from public projects  to [-log]. Generally, haplotype-based selection methods and methods in TagSNPs "missed" more single-SNP and haplotype associations than other methods  may lead to more missed signals, particularly in haplotype association testing. The program TagSNPs did not offer particular advantages over ldSelect or Tagger in terms of number of SNPs chosen or associations detected. Regardless of the method used, typing additional markers in areas of signal may improve signal strength and localization.The current work suggests that empirical assessment of a larger data set and simulated data addressing a range of genetic models would allow for more precise comparison of approaches. Consideration of coverage, rather than signal strength, and examination of our assumption that signals detected in each region were due to a common underlying genetic cause could further inform comparisons. Additional issues include cost efficiency, transferability of tagging SNPs, and the role of bioinformatics.The optimal tagging SNP method to use will depend on the true genetic model of the association. Pairwise or multimarker methods are optimal if the discovery SNP set contains the causal SNP , while haplotype-based methods are optimal if the discovery SNP set defines a haplotype carrying the causal allele. Unfortunately, it is seldom known during the SNP selection phase of studies whether a SNP or a haplotype defines an association. Thus, critical assessment of the utility of available SNP selection methods under a variety of conditions is essential.The author(s) declare that they have no competing interests.