Subset Of SNPs Research Articles

SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock

BackgroundCurrently, six commercial whole-genome SNP chips are available for cattle genotyping, produced by two different genotyping platforms. Technical issues need to be addressed to combine data that originates from the different platforms, or different versions of the same array generated by the manufacturer. For example: i) genome coordinates for SNPs may refer to different genome assemblies; ii) reference genome sequences are updated over time changing the positions, or even removing sequences which contain SNPs; iii) not all commercial SNP ID’s are searchable within public databases; iv) SNPs can be coded using different formats and referencing different strands (e.g. A/B or A/C/T/G alleles, referencing forward/reverse, top/bottom or plus/minus strand); v) Due to new information being discovered, higher density chips do not necessarily include all the SNPs present in the lower density chips; and, vi) SNP IDs may not be consistent across chips and platforms. Most researchers and breed associations manage SNP data in real-time and thus require tools to standardise data in a user-friendly manner.DescriptionHere we present SNPchiMp, a MySQL database linked to an open access web-based interface. Features of this interface include, but are not limited to, the following functions: 1) referencing the SNP mapping information to the latest genome assembly, 2) extraction of information contained in dbSNP for SNPs present in all commercially available bovine chips, and 3) identification of SNPs in common between two or more bovine chips (e.g. for SNP imputation from lower to higher density). In addition, SNPchiMp can retrieve this information on subsets of SNPs, accessing such data either via physical position on a supported assembly, or by a list of SNP IDs, rs or ss identifiers.ConclusionsThis tool combines many different sources of information, that otherwise are time consuming to obtain and difficult to integrate. The SNPchiMp not only provides the information in a user-friendly format, but also enables researchers to perform a large number of operations with a few clicks of the mouse. This significantly reduces the time needed to execute the large number of operations required to manage SNP data.

Hypothesis Testing in Normal Admixture Models to Detect Heterogeneous Genetic Signals

In this work we consider a three-component normal mixture model in which one component is known to have mean zero and the other two contaminating components have a nonnegative and a no positive mean respectively, while all three components share a common unknown variance parameter. One potential application of this model may be in prioritizing statistical scores obtained in biological experiments, including genetics data. Such a mixture model may be useful in describing the distribution of numerous Z test statistics corresponding to different genes or SNPs, such that a “significant” Z test statistic for a particular gene suggests its connection to a medical condition. More specifically, the inferences drawn from such a mixture model may be useful in a filtration algorithm to remove large subsets of genes or SNPs from consideration, thereby reducing the need for stringent and power-depleting multiplicity adjustments for controlling type I error rates on the remaining genes. We show how to test whether there is contamination in at least one direction (i.e., the mixture model truly requires at least two components) and, if so, how to test whether there is contamination in both directions (i.e., the mixture model truly requires all three components). We assess our testing procedures in simulation studies and illustrate them through application to LOD scores in a genome-wide linkage analysis from an autism study.

RNA sequencing reveals the complex regulatory network in the maize kernel

RNA sequencing can simultaneously identify exonic polymorphisms and quantitate gene expression. Here we report RNA sequencing of developing maize kernels from 368 inbred lines producing 25.8 billion reads and 3.6 million single-nucleotide polymorphisms. Both the MaizeSNP50 BeadChip and the Sequenom MassArray iPLEX platforms confirm a subset of high-quality SNPs. Of these SNPs, we have mapped 931,484 to gene regions with a mean density of 40.3 SNPs per gene. The genome-wide association study identifies 16,408 expression quantitative trait loci. A two-step approach defines 95.1% of the eQTLs to a 10-kb region, and 67.7% of them include a single gene. The establishment of relationships between eQTLs and their targets reveals a large-scale gene regulatory network, which include the regulation of 31 zein and 16 key kernel genes. These results contribute to our understanding of kernel development and to the improvement of maize yield and nutritional quality.

Efficient techniques for genotype‐phenotype correlational analysis

BackgroundSingle Nucleotide Polymorphisms (SNPs) are sequence variations found in individuals at some specific points in the genomic sequence. As SNPs are highly conserved throughout evolution and within a population, the map of SNPs serves as an excellent genotypic marker. Conventional SNPs analysis mechanisms suffer from large run times, inefficient memory usage, and frequent overestimation. In this paper, we propose efficient, scalable, and reliable algorithms to select a small subset of SNPs from a large set of SNPs which can together be employed to perform phenotypic classification.MethodsOur algorithms exploit the techniques of gene selection and random projections to identify a meaningful subset of SNPs. To the best of our knowledge, these techniques have not been employed before in the context of genotype‐phenotype correlations. Random projections are used to project the input data into a lower dimensional space (closely preserving distances). Gene selection is then applied on the projected data to identify a subset of the most relevant SNPs.ResultsWe have compared the performance of our algorithms with one of the currently known best algorithms called Multifactor Dimensionality Reduction (MDR), and Principal Component Analysis (PCA) technique. Experimental results demonstrate that our algorithms are superior in terms of accuracy as well as run time.ConclusionsIn our proposed techniques, random projection is used to map data from a high dimensional space to a lower dimensional space, and thus overcomes the curse of dimensionality problem. From this space of reduced dimension, we select the best subset of attributes. It is a unique mechanism in the domain of SNPs analysis, and to the best of our knowledge it is not employed before. As revealed by our experimental results, our proposed techniques offer the potential of high accuracies while keeping the run times low.

Efficient haplotype block partitioning and tag SNP selection algorithms under various constraints.

Patterns of linkage disequilibrium plays a central role in genome-wide association studies aimed at identifying genetic variation responsible for common human diseases. These patterns in human chromosomes show a block-like structure, and regions of high linkage disequilibrium are called haplotype blocks. A small subset of SNPs, called tag SNPs, is sufficient to capture the haplotype patterns in each haplotype block. Previously developed algorithms completely partition a haplotype sample into blocks while attempting to minimize the number of tag SNPs. However, when resource limitations prevent genotyping all the tag SNPs, it is desirable to restrict their number. We propose two dynamic programming algorithms, incorporating many diversity evaluation functions, for haplotype block partitioning using a limited number of tag SNPs. We use the proposed algorithms to partition the chromosome 21 haplotype data. When the sample is fully partitioned into blocks by our algorithms, the 2,266 blocks and 3,260 tag SNPs are fewer than those identified by previous studies. We also demonstrate that our algorithms find the optimal solution by exploiting the nonmonotonic property of a common haplotype-evaluation function.

Presence of Multiple Independent Effects in Risk Loci of Common Complex Human Diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.

Alternative strategies for selecting subsets of predicting SNPs by LASSO-LARS procedure

BackgroundThe least absolute shrinkage and selection operator (LASSO) can be used to predict SNP effects. This operator has the desirable feature of including in the model only a subset of explanatory SNPs, which can be useful both in QTL detection and GWS studies. LASSO solutions can be obtained by the least angle regression (LARS) algorithm. The big issue with this procedure is to define the best constraint (t), i.e. the upper bound of the sum of absolute value of the SNP effects which roughly corresponds to the number of SNPs to be selected. Usai et al. (2009) dealt with this problem by a cross-validation approach and defined t as the average number of selected SNPs overall replications. Nevertheless, in small size populations, such estimator could give underestimated values of t. Here we propose two alternative ways to define t and compared them with the "classical" one.MethodsThe first (strategy 1), was based on 1,000 cross-validations carried out by randomly splitting the reference population (2,000 individuals with performance) into two halves. The value of t was the number of SNPs which occurred in more than 5% of replications. The second (strategy 2), which did not use cross-validations, was based on the minimization of the Cp-type selection criterion which depends on the number of selected SNPs and the expected residual variance.ResultsThe size of the subset of selected SNPs was 46, 189 and 64 for the classical approach, strategy 1 and 2 respectively. Classical and strategy 2 gave similar results and indicated quite clearly the regions were QTL with additive effects were located. Strategy 1 confirmed such regions and added further positions which gave a less clear scenario. Correlation between GEBVs estimated with the three strategies and TBVs in progenies without phenotypes were 0.9237, 0.9000 and 0.9240 for classical, strategy 1 and 2 respectively.ConclusionsThis suggests that the Cp-type selection criterion is a valid alternative to the cross-validations to define the best constraint for selecting subsets of predicting SNPs by LASSO-LARS procedure.

Linear models for breeding values prediction in haplotype-assisted selection - an analysis of QTL-MAS Workshop 2011 Data

BackgroundThe aim of this study was to estimate haplotype effects and then to predict breeding values using linear models. The haplotype based analysis enables avoidance of loosing information due to linkage disequilibrium between single markers. There are also less explanatory variables in the linear model which makes the estimation more reliable.MethodsDifferent methods and criteria for marker and haplotype selection were considered. First, markers with MAF lower than 5% where excluded from the data set. Then, SNPs in complete linkage disequilibrium where selected. Next step was to construct haplotypes and to estimate their frequencies basing on selected SNPs. The haplotypes with a frequency lower than 1% were not considered in further analysis. Chosen haplotypes were used as the explanatory variables in the linear models for breeding values prediction. Linear models with fixed and random haplotype effects as well as animal model were tested.ResultsThe number of markers was limited to 1206, 1189, 1249, 1288 and 1167 for chromosome 1, 2, 3, 4 and 5, respectively due to MAF criterion. In total 409 subsets of SNPs with r2=1 were found. 1476 haplotypes with different lengths were inferred. The frequencies of 817 haplotypes were higher than 1% - 184 for the first chromosome, 172 for the second, 131 for the third, 146 for the forth and 184 haplotypes for the fifth chromosome. The haplotype effects estimated using random models were comparable and more precise in prediction for individuals with unknown phenotypes. A few haplotypes with large effects were found when their effects were defined as fixed in the linear model . The correlations of the predicted breeding values with true breeding values were not that high. This could be brought about by selection criteria imposed on the genotype data which led to substantial reduction of number of markers.ConclusionsAlthough not many markers were considered in the study, the results obtained show that the implemented approach can be considered as quite promising. The haplotype approach let to avoid high dimensional models as compared with single SNPs models.

Analysis workflow for the identification allelic variants associated with rare disorders using whole genome sequencing approach

Motivation Recent advances in sequencing technologies allowed for unprecedented possibilities and applications for clinical data analysis. In particular, Whole Genome sequencing at reasonable coverage has turned into a cost-effective technology to characterize the genetic framework of rare diseases. Here we present an example of a case of whole genome sequencing analysis in a case of a rare recessive disease. Our purpose is to identify allelic variants associated with the described syndrome using a whole genome sequencing approach. Methods As an example, we utilized 100 bp paired-end sequencing of a single human genomic sample from a rare recessive disorder using the Illumina HiSeq 2000 sequencer. Read tags were aligned to the hg19 reference genome using BWA [1]. The Genome Analysis ToolKit was used to pipeline the downstream analysis: local realignment around indels, quality score recalibration, SNP and indel calling and Variant Quality Score Recalibration. We filtered SNV with a confidence lower than 0.1%. We used the Seattle SNP Annotation tools [3] in order to annotate the SNP on the reference genome. We performed some preliminary statistics in order to identify a threshold that allowed us to identify a reliable subset of SNPs to use for our purpose. Using the sub set of known SNPs as guide we identify the value of the SNP quality, for which we are confident regarding our data. Using a quality threshold of 50 we identified a subset of SNPs that we analysed with the HomozygosityMapper web tools [4]. Results The sequencing data used amounted to 400 million read pairs, achieving a 20x average coverage of the genome (sufficient for reliable homozygous SNP calls). We filtered out 2 million SNPs with a read depth of at least 5 and max 250. The average quality above 400k SNPs is 313 (max 9371, min 30). We found about 100k novel SNPs (10% of which were homozygous). Given the assumption of recessive inheritance in the example under investigation, we focused on homozygous stretches, and identified 6 stretches of homozigosity, two of which were also confirmed by previous microsatellite linkage data. This workflow can be easily automatized and used for different type of re-sequencing projects in the context of recessive rare disorders. Reference Li and Durbin (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26 (5):589-95 Mckenna et al. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome research 20 (9):1297-1303 http://snp.gs.washington.edu/SeattleSeqAnnotation134/ Seelow et al. (2009) HomozygosityMapper—an interactive approach to homozygosity mapping. Nucleic Acids Res 37 (suppl 2):W593-W599

Abstract 5503: A simple correction for population stratification in random forest analysis

Abstract Introduction Random forest (RF) has been increasingly applied in Genome-wide Association (GWA) studies because of its advantage in handling high dimensional genetic data. RF creates importance measures for SNPs which are helpful for feature selections. However, if the confounding effect of population structure, including population stratification and admixture, is not appropriately corrected, RF tends to give greater importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results. Methods We propose to correct for the confounding effect of population structure by including the EIGENSTRAT approach in the RF analysis. The axes of variation are firstly extracted from a large number of structure inference SNPs. Instead of including the axes of variation in the RF model directly, the phenotype and genotypes are regressed on these axes, respectively. The residuals for phenotype and genotyoes are then used as the outcome and predictors in RF analysis. We conduct simulation studies to investigate whether the proposed method works well in RF analysis. We simulate 4 scenarios. Scenario 1 to 3 are designed to have 2, 3 and 4 underlying subpopulations, respectively. In each of the 3 scenarios, two levels (moderate and extreme) of population stratification are simulated by varying the proportions of individuals in cases and controls from the different subpopulations. In scenario 4, we consider an admixed population formed by two ancestral populations. For each setting in the different scenarios, we generate 1,000 datasets. Each dataset consists of 10,000 SNPs for PS inference, 90 disease-unrelated random SNPs, 9 disease-unrelated differentiated SNPs and 1 causal SNP with RR=1.5. Results Our extensive simulations show that the correction for PS improves the ability of identifying the causal SNP and decreases the ranks of differentiated SNPs when compared with RF analysis without PS correction. We apply the proposed method to analyze a subset of SNPs from a GWA dataset on NSCLC. The dataset is analyzed by RF either without or with PS correction. Lists of the top 1000 SNPs from the 2 analyses are mapped to genes using the “20kb rule”. GeneGO disease (by biomarkers) analysis is performed for each of the 2 gene lists. In both analysis, “Tobacco Use Disorder” ranks the 1st. Two lung-related diseases, “Adenocarcinoma, Bronchiolo-Alveolar” and “Lung Diseases, Obstructive,” rank the 12th and 18th in the RF with PS correction analysis, whereas they rank the 69th and 104th when being analyzed by RF without PS correction. Conclusion The confounding effect of population structure can be easily corrected by including the EIGENSTRAT procedure in RF analysis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5503. doi:1538-7445.AM2012-5503

Genome-Wide SNP Detection, Validation, and Development of an 8K SNP Array for Apple

As high-throughput genetic marker screening systems are essential for a range of genetics studies and plant breeding applications, the International RosBREED SNP Consortium (IRSC) has utilized the Illumina Infinium® II system to develop a medium- to high-throughput SNP screening tool for genome-wide evaluation of allelic variation in apple (Malus×domestica) breeding germplasm. For genome-wide SNP discovery, 27 apple cultivars were chosen to represent worldwide breeding germplasm and re-sequenced at low coverage with the Illumina Genome Analyzer II. Following alignment of these sequences to the whole genome sequence of ‘Golden Delicious’, SNPs were identified using SoapSNP. A total of 2,113,120 SNPs were detected, corresponding to one SNP to every 288 bp of the genome. The Illumina GoldenGate® assay was then used to validate a subset of 144 SNPs with a range of characteristics, using a set of 160 apple accessions. This validation assay enabled fine-tuning of the final subset of SNPs for the Illumina Infinium® II system. The set of stringent filtering criteria developed allowed choice of a set of SNPs that not only exhibited an even distribution across the apple genome and a range of minor allele frequencies to ensure utility across germplasm, but also were located in putative exonic regions to maximize genotyping success rate. A total of 7867 apple SNPs was established for the IRSC apple 8K SNP array v1, of which 5554 were polymorphic after evaluation in segregating families and a germplasm collection. This publicly available genomics resource will provide an unprecedented resolution of SNP haplotypes, which will enable marker-locus-trait association discovery, description of the genetic architecture of quantitative traits, investigation of genetic variation (neutral and functional), and genomic selection in apple.

Combining SNP discovery from next-generation sequencing data with bulked segregant analysis (BSA) to fine-map genes in polyploid wheat

BackgroundNext generation sequencing (NGS) technologies are providing new ways to accelerate fine-mapping and gene isolation in many species. To date, the majority of these efforts have focused on diploid organisms with readily available whole genome sequence information. In this study, as a proof of concept, we tested the use of NGS for SNP discovery in tetraploid wheat lines differing for the previously cloned grain protein content (GPC) gene GPC-B1. Bulked segregant analysis (BSA) was used to define a subset of putative SNPs within the candidate gene region, which were then used to fine-map GPC-B1.ResultsWe used Illumina paired end technology to sequence mRNA (RNAseq) from near isogenic lines differing across a ~30-cM interval including the GPC-B1 locus. After discriminating for SNPs between the two homoeologous wheat genomes and additional quality filtering, we identified inter-varietal SNPs in wheat unigenes between the parental lines. The relative frequency of these SNPs was examined by RNAseq in two bulked samples made up of homozygous recombinant lines differing for their GPC phenotype. SNPs that were enriched at least 3-fold in the corresponding pool (6.5% of all SNPs) were further evaluated. Marker assays were designed for a subset of the enriched SNPs and mapped using DNA from individuals of each bulk. Thirty nine new SNP markers, corresponding to 67% of the validated SNPs, mapped across a 12.2-cM interval including GPC-B1. This translated to 1 SNP marker per 0.31 cM defining the GPC-B1 gene to within 13-18 genes in syntenic cereal genomes and to a 0.4 cM interval in wheat.ConclusionsThis study exemplifies the use of RNAseq for SNP discovery in polyploid species and supports the use of BSA as an effective way to target SNPs to specific genetic intervals to fine-map genes in unsequenced genomes.

Matrix eQTL: ultra fast eQTL analysis via large matrix operations

Expression quantitative trait loci (eQTL) mapping aims to determine genomic regions that regulate gene transcription. Expression QTL is used to study the regulatory structure of normal tissues and to search for genetic factors in complex diseases such as cancer, diabetes, and cystic fibrosis. A modern eQTL dataset contains millions of SNPs and thousands of transcripts measured for hundreds of samples. This makes the analysis computationally complex as it involves independent testing for association for every transcript-SNP pair. The heavy computational burden makes eQTL analysis less popular, often forces analysts to restrict their attention to just a subset of transcripts and SNPs. As larger genotype and gene expression datasets become available, the demand for fast tools for eQTL analysis increases. We present a new method for fast eQTL analysis via linear models, called Matrix eQTL. Matrix eQTL can model and test for association using both linear regression and ANOVA models. The models can include covariates to account for such factors as population structure, gender, and clinical variables. It also supports testing of heteroscedastic models and models with correlated errors. In our experiment on large datasets Matrix eQTL was thousands of times faster than the existing popular software for QTL/eQTL analysis. Matrix eQTL is implemented as both Matlab and R packages and thus can easily be run on Windows, Mac OS, and Linux systems. The software is freely available at the following address: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL

Characterisation of QTL-linked and genome-wide restriction site-associated DNA (RAD) markers in farmed Atlantic salmon.

BackgroundRestriction site-associated DNA sequencing (RAD-Seq) is a genome complexity reduction technique that facilitates large-scale marker discovery and genotyping by sequencing. Recent applications of RAD-Seq have included linkage and QTL mapping with a particular focus on non-model species. In the current study, we have applied RAD-Seq to two Atlantic salmon families from a commercial breeding program. The offspring from these families were classified into resistant or susceptible based on survival/mortality in an Infectious Pancreatic Necrosis (IPN) challenge experiment, and putative homozygous resistant or susceptible genotype at a major IPN-resistance QTL. From each family, the genomic DNA of the two heterozygous parents and seven offspring of each IPN phenotype and genotype was digested with the SbfI enzyme and sequenced in multiplexed pools.ResultsSequence was obtained from approximately 70,000 RAD loci in both families and a filtered set of 6,712 segregating SNPs were identified. Analyses of genome-wide RAD marker segregation patterns in the two families suggested SNP discovery on all 29 Atlantic salmon chromosome pairs, and highlighted the dearth of male recombination. The use of pedigreed samples allowed us to distinguish segregating SNPs from putative paralogous sequence variants resulting from the relatively recent genome duplication of salmonid species. Of the segregating SNPs, 50 were linked to the QTL. A subset of these QTL-linked SNPs were converted to a high-throughput assay and genotyped across large commercial populations of IPNV-challenged salmon fry. Several SNPs showed highly significant linkage and association with resistance to IPN, and population linkage-disequilibrium-based SNP tests for resistance were identified.ConclusionsWe used RAD-Seq to successfully identify and characterise high-density genetic markers in pedigreed aquaculture Atlantic salmon. These results underline the effectiveness of RAD-Seq as a tool for rapid and efficient generation of QTL-targeted and genome-wide marker data in a large complex genome, and its possible utility in farmed animal selection programs.

Abstract PL07-01: Linkage disequilibrium-based analysis of DNA repair and GST genes in malignant gliomas: A unique tool in defining the association between genetic diversity and survival

Abstract Inherited genetics are increasingly being identified as a significant factor that contributes to both treatment outcome and treatment-associated toxicities in cancer patients undergoing therapy. The specific SNPs and genes involved are, however, still poorly understood. In this study, we exploited the unique advantages of a linkage disequilibrium-based approach to examine the relationship between genetic diversity in regions of DNA repair and GST genes with survival in adults with anaplastic astrocytoma and glioblastoma multiforme treated on temozolomide and carmustine-based protocols. We genotyped a subset of 1,267 tagging SNPs within or near haplotype blocks of 27 genes in 301 patients. The SNPs were selected to be in high LD with and, thus, to be predictive of the diversity of the target region containing the gene and 20 kb flanking 3′ and 5′ of it. Genotyping was conducted on a high-throughput customized Affymetrix platform. Associations between genotypes and survival was performed using the Fisher's exact test, after testing for Hardy-Weinberg conformity and gender mismatches. The results identified several tSNPs in and around the loci of 11 genes (EXO1, DDB2, LIG1, MGMT, MSH2, MSH4, PAN3, RAD52, TDG, XRCC1, and XRCC5) to be highly associated with survival. Several other SNPs showing a statistical tendency to be associative. Although, a number of SNPs/genes was associated with survival in both anaplastic astrocytoma and GBM, a subset of genes and SNPs were associative in GBMs but not in AAs and vice versa. We then examined 28 SNPs in all eight members of the GST family (A1, M1, M3, P1, O1, S1, T and Z) allowing the identification of seven specific SNPs that were highly associated with treatment outcome/survival. The identification of highly polymorphic regions associated with the loci of DNA repair and GST genes as determinants of alkylating agent-based therapy in malignant gliomas is consistent with the established role of enhanced DNA repair and drug metabolism in tumor resistance to these agents. Ongoing studies are directed at these associative SNPs to gain insight into the biology underlying their association with treatment outcome. Supported by grants RO1 CA 153050, RO1 CA127872, RO1 CA 112519 and P30-CA114236 from the NIH, USA. Citation Information: Cancer Epidemiol Biomarkers Prev 2011;20(10 Suppl):PL07-01.

Apolipoprotein M Gene (APOM) Polymorphism Modifies Metabolic and Disease Traits in Type 2 Diabetes

This study aimed at substantiating the associations of the apolipoproein M gene (APOM) with type 2 diabetes (T2D) as well as with metabolic traits in Hong Kong Chinese. In addition, APOM gene function was further characterized to elucidate its activity in cholesterol metabolism. Seventeen APOM SNPs documented in the NCBI database were genotyped. Five SNPs were confirmed in our study cohort of 1234 T2D and 606 control participants. Three of the five SNPs rs707921(C+1871A), rs707922(G+1837T) and rs805264(G+203A) were in linkage disequilibrium (LD). We chose rs707922 to tag this LD region for down stream association analyses and characterized the function of this SNP at molecular level. No association between APOM and T2D susceptibility was detected in our Hong Kong Chinese cohort. Interestingly, the C allele of rs805297 was significantly associated with T2D duration of longer than 10 years (OR = 1.245, p = 0.015). The rs707922 TT genotype was significantly associated with elevated plasma total- and LDL- cholesterol levels (p = 0.006 and p = 0.009, respectively) in T2D patients. Molecular analyses of rs707922 lead to the discoveries of a novel transcript APOM5 as well as the cryptic nature of exon 5 of the gene. Ectopic expression of APOM5 transcript confirmed rs707922 allele-dependent activity of the transcript in modifying cholesterol homeostasis in vitro. In conclusion, the results here did not support APOM as a T2D susceptibility gene in Hong Kong Chinese. However, in T2D patients, a subset of APOM SNPs was associated with disease duration and metabolic traits. Further molecular analysis proved the functional activity of rs707922 in APOM expression and in regulation of cellular cholesterol content.

Identification, utilisation and mapping of novel transcriptome-based markers from blackcurrant (Ribes nigrum).

BackgroundDeep-level second generation sequencing (2GS) technologies are now being applied to non-model species as a viable and favourable alternative to Sanger sequencing. Large-scale SNP discovery was undertaken in blackcurrant (Ribes nigrum L.) using transcriptome-based 2GS 454 sequencing on the parental genotypes of a reference mapping population, to generate large numbers of novel markers for the construction of a high-density linkage map.ResultsOver 700,000 reads were produced, from which a total of 7,000 SNPs were found. A subset of polymorphic SNPs was selected to develop a 384-SNP OPA assay using the Illumina BeadXpress platform. Additionally, the data enabled identification of 3,000 novel EST-SSRs. The selected SNPs and SSRs were validated across diverse Ribes germplasm, including mapping populations and other selected Ribes species.SNP-based maps were developed from two blackcurrant mapping populations, incorporating 48% and 27% of assayed SNPs respectively. A relatively high proportion of visually monomorphic SNPs were investigated further by quantitative trait mapping of theta score outputs from BeadStudio analysis, and this enabled additional SNPs to be placed on the two maps.ConclusionsThe use of 2GS technology for the development of markers is superior to previously described methods, in both numbers of markers and biological informativeness of those markers. Whilst the numbers of reads and assembled contigs were comparable to similar sized studies of other non-model species, here a high proportion of novel genes were discovered across a wide range of putative function and localisation. The potential utility of markers developed using the 2GS approach in downstream breeding applications is discussed.

The Bayesian lasso for genome-wide association studies

Despite their success in identifying genes that affect complex disease or traits, current genome-wide association studies (GWASs) based on a single SNP analysis are too simple to elucidate a comprehensive picture of the genetic architecture of phenotypes. A simultaneous analysis of a large number of SNPs, although statistically challenging, especially with a small number of samples, is crucial for genetic modeling. We propose a two-stage procedure for multi-SNP modeling and analysis in GWASs, by first producing a 'preconditioned' response variable using a supervised principle component analysis and then formulating Bayesian lasso to select a subset of significant SNPs. The Bayesian lasso is implemented with a hierarchical model, in which scale mixtures of normal are used as prior distributions for the genetic effects and exponential priors are considered for their variances, and then solved by using the Markov chain Monte Carlo (MCMC) algorithm. Our approach obviates the choice of the lasso parameter by imposing a diffuse hyperprior on it and estimating it along with other parameters and is particularly powerful for selecting the most relevant SNPs for GWASs, where the number of predictors exceeds the number of observations. The new approach was examined through a simulation study. By using the approach to analyze a real dataset from the Framingham Heart Study, we detected several significant genes that are associated with body mass index (BMI). Our findings support the previous results about BMI-related SNPs and, meanwhile, gain new insights into the genetic control of this trait. The computer code for the approach developed is available at Penn State Center for Statistical Genetics web site, http://statgen.psu.edu.

Pathway‐based analysis for genome‐wide association studies using supervised principal components

Many complex diseases are influenced by genetic variations in multiple genes, each with only a small marginal effect on disease susceptibility. Pathway analysis, which identifies biological pathways associated with disease outcome, has become increasingly popular for genome-wide association studies (GWAS). In addition to combining weak signals from a number of SNPs in the same pathway, results from pathway analysis also shed light on the biological processes underlying disease. We propose a new pathway-based analysis method for GWAS, the supervised principal component analysis (SPCA) model. In the proposed SPCA model, a selected subset of SNPs most associated with disease outcome is used to estimate the latent variable for a pathway. The estimated latent variable for each pathway is an optimal linear combination of a selected subset of SNPs; therefore, the proposed SPCA model provides the ability to borrow strength across the SNPs in a pathway. In addition to identifying pathways associated with disease outcome, SPCA also carries out additional within-category selection to identify the most important SNPs within each gene set. The proposed model operates in a well-established statistical framework and can handle design information such as covariate adjustment and matching information in GWAS. We compare the proposed method with currently available methods using data with realistic linkage disequilibrium structures, and we illustrate the SPCA method using the Wellcome Trust Case-Control Consortium Crohn Disease (CD) data set.

SKM-SNP: SNP markers detection method

SKM-SNP, SNP markers detection program, is proposed to identify a set of relevant SNPs for the association between a disease and multiple marker genotypes. We employ a subspace categorical clustering algorithm to compute a weight for each SNP in the group of patient samples and the group of normal samples, and use the weights to identify the subsets of relevant SNPs that categorize these two groups. The experiments on both Schizophrenia and Parkinson Disease data sets containing genome-wide SNPs are reported to demonstrate the program. Results indicate that our method can find some relevant SNPs that categorize the disease samples. The online SKM-SNP program is available at http://www.math.hkbu.edu.hk/~mng/SKM-SNP/SKM-SNP.html.