Identification by denaturing high-performance liquid chromatography of numerous polymorphisms in a candidate region for multiple sclerosis susceptibility

Genetic association analysis of candidate regions where evidence of linkage has accumulated is becoming a key issue in the study of complex diseases. A high density of markers, at least one per centimorgan, is required to improve the chances of observing linkage disequilibrium with disease alleles. A recently available single nucleotide polymorphism (SNP) map designed to cover the whole genome provides an average density of one marker per 2 cM. In the present study we show that the number of markers can be approximately doubled in a selected region, thus reaching a density suitable for association studies, by applying a completely automated technique for polymorphism detection, denaturing high-performance liquid chromatography (DHPLC). A systematic search for SNPs was performed in the region 5ptel-q13, where weak but convergent evidence for linkage with multiple sclerosis has accumulated. Screening for polymorphisms was performed on 124 sequence tagged sites (STSs) in the 3'UTR ends of expressed sequence tags totaling about 30,000 bp. Thirty SNPs in 28 STSs were found with less than 10% overlap with the markers already detected in the same region. The data confirm the validity of the approach using DHPLC on expressed gene sequences tagged by a set of standard commercially available primers.     

ProbeMatchDB--a web database for finding equivalent probes across microarray platforms and species

SUMMARY: ProbeMatchDB is a web-based database designed to facilitate the search of EST/cDNA sequences or STS markers that can be used to represent the same gene across different microarray platforms and species. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms. AVAILABILITY: The database is accessible at http://brainarray.mhri.med.umich.edu/MARRAY/BC_ASP/brainarray.htm by clicking the 'Query ProbeMatchDB' link.     

Glutamate Receptor 6 Gene (<Emphasis Type="Italic">GluR6</Emphasis> or <Emphasis Type="Italic">GRIK2</Emphasis>) Polymorphisms in the Indian Population: A Genetic Association Study on Autism Spectrum Disorder

Autism is a neurodevelopmental disorder with early manifestation. It is a multifactorial disorder and several susceptible chromosomal regions for autism are identified through genome scan studies. The gene coding for glutamate receptor 6 (GluR6 or GRIK2) has been suggested as a candidate gene for autism based on its localization in the autism specific region on chromosome 6q21 and the involvement of receptor protein in cognitive functions like learning and memory. Despite its importance, so far no studies have been carried out on possible involvement of GluR6 with autism in the Indian population. Therefore in the present study, we have performed genetic analysis of three markers of GluR6 (SNP1: rs2227281, SNP2: rs2227283, SNP3: rs2235076) for possible association with autism through population, and family-based (TDT and HHRR) approaches. DSM-IV criteria and CARS/ADI-R have been utilized for diagnosis. Genotyping analysis for the SNPs has been carried out in 101 probands with autism spectrum disorder, 180 parents and 152 controls from different regions of India. Since the minor allele frequency of SNP3 was too low, the association studies have been carried out only for SNP1 and SNP2. Even though two earlier studies have shown association of these markers with autism, the present case–control and TDT, as well as HHRR analyses have not demonstrated any biased transmission of alleles or haplotypes to the affected offspring. Thus our results suggest that these markers of GluR6 are unlikely to be associated with autism in the Indian population.     

Genetic Association Studies: An Information Content Perspective

The availability of high-density single nucleotide polymorphisms (SNPs) data has made the human genetic association studies possible to identify common and rare variants underlying complex diseases in a genome-wide scale. A handful of novel genetic variants have been identified, which gives much hope and prospects for the future of genetic association studies. In this process, statistical and computational methods play key roles, among which information-based association tests have gained large popularity. This paper is intended to give a comprehensive review of the current literature in genetic association analysis casted in the framework of information theory. We focus our review on the following topics: (1) information theoretic approaches in genetic linkage and association studies; (2) entropy-based strategies for optimal SNP subset selection; and (3) the usage of theoretic information criteria in gene clustering and gene regulatory network construction.     

Cuckoo search epistasis: a new method for exploring significant genetic interactions

The advent of high-throughput sequencing technology has resulted in the ability to measure millions of single-nucleotide polymorphisms (SNPs) from thousands of individuals. Although these high-dimensional data have paved the way for better understanding of the genetic architecture of common diseases, they have also given rise to challenges in developing computational methods for learning epistatic relationships among genetic markers. We propose a new method, named cuckoo search epistasis (CSE) for identifying significant epistatic interactions in population-based association studies with a case–control design. This method combines a computationally efficient Bayesian scoring function with an evolutionary-based heuristic search algorithm, and can be efficiently applied to high-dimensional genome-wide SNP data. The experimental results from synthetic data sets show that CSE outperforms existing methods including multifactorial dimensionality reduction and Bayesian epistasis association mapping. In addition, on a real genome-wide data set related to Alzheimer''s disease, CSE identified SNPs that are consistent with previously reported results, and show the utility of CSE for application to genome-wide data.     

Effective algorithms for tag SNP selection

Single nucleotide polymorphisms (SNPs), due to their abundance and low mutation rate, are very useful genetic markers for genetic association studies. However, the current genotyping technology cannot afford to genotype all common SNPs in all the genes. By making use of linkage disequilibrium, we can reduce the experiment cost by genotyping a subset of SNPs, called Tag SNPs, which have a strong association with the ungenotyped SNPs, while are as independent from each other as possible. The problem of selecting Tag SNPs is NP-complete; when there are large number of SNPs, in order to avoid extremely long computational time, most of the existing Tag SNP selection methods first partition the SNPs into blocks based on certain block definitions, then Tag SNPs are selected in each block by brute-force search. The size of the Tag SNP set obtained in this way may usually be reduced further due to the inter-dependency among blocks. This paper proposes two algorithms, TSSA and TSSD, to tackle the block-independent Tag SNP selection problem. TSSA is based on A* search algorithm, and TSSD is a heuristic algorithm. Experiments show that TSSA can find the optimal solutions for medium-sized problems in reasonable time, while TSSD can handle very large problems and report approximate solutions very close to the optimal ones.     

Generalized T2 test for genome association studies

Recent progress in the development of single-nucleotide polymorphism (SNP) maps within genes and across the genome provides a valuable tool for fine-mapping and has led to the suggestion of genomewide association studies to search for susceptibility loci for complex traits. Test statistics for genome association studies that consider a single marker at a time, ignoring the linkage disequilibrium between markers, are inefficient. In this study, we present a generalized T2 statistic for association studies of complex traits, which can utilize multiple SNP markers simultaneously and considers the effects of multiple disease-susceptibility loci. This generalized T2 statistic is a corollary to that originally developed for multivariate analysis and has a close relationship to discriminant analysis and common measure of genetic distance. We evaluate the power of the generalized T2 statistic and show that power to be greater than or equal to those of the traditional chi2 test of association and a similar haplotype-test statistic. Finally, examples are given to evaluate the performance of the proposed T2 statistic for association studies using simulated and real data.     

Commercially Available Outbred Mice for Genome-Wide Association Studies

Genome-wide association studies using commercially available outbred mice can detect genes involved in phenotypes of biomedical interest. Useful populations need high-frequency alleles to ensure high power to detect quantitative trait loci (QTLs), low linkage disequilibrium between markers to obtain accurate mapping resolution, and an absence of population structure to prevent false positive associations. We surveyed 66 colonies for inbreeding, genetic diversity, and linkage disequilibrium, and we demonstrate that some have haplotype blocks of less than 100 Kb, enabling gene-level mapping resolution. The same alleles contribute to variation in different colonies, so that when mapping progress stalls in one, another can be used in its stead. Colonies are genetically diverse: 45% of the total genetic variation is attributable to differences between colonies. However, quantitative differences in allele frequencies, rather than the existence of private alleles, are responsible for these population differences. The colonies derive from a limited pool of ancestral haplotypes resembling those found in inbred strains: over 95% of sequence variants segregating in outbred populations are found in inbred strains. Consequently it is possible to impute the sequence of any mouse from a dense SNP map combined with inbred strain sequence data, which opens up the possibility of cataloguing and testing all variants for association, a situation that has so far eluded studies in completely outbred populations. We demonstrate the colonies'' potential by identifying a deletion in the promoter of H2-Ea as the molecular change that strongly contributes to setting the ratio of CD4+ and CD8+ lymphocytes.     

Construction of a SNP and SSR linkage map in autotetraploid blueberry using genotyping by sequencing

The construction of the first genetic map in autotetraploid blueberry has been made possible by the development of new SNP markers developed using genotyping by sequencing in a mapping population created from a cross between two key highbush blueberry cultivars, Draper × Jewel (Vaccinium corymbosum). The novel SNP markers were supplemented with existing SSR markers to enable the alignment of parental maps.  In total, 1794 single nucleotide polymorphic (SNP) markers and 233 simple sequence repeat (SSR) markers exhibited segregation patterns consistent with a random chromosomal segregation model for meiosis in an autotetraploid. Of these, 700 SNPs and 85 SSRs were utilized for construction of the ‘Draper’ genetic map, and 450 SNPs and 86 SSRs for the ‘Jewel’ map.  The ‘Draper’ map comprises 12  linkage groups (LG), associated with the haploid chromosome number for blueberry, and totals 1621 cM while the ‘Jewel’ map comprises 20 linkage groups totalling 1610 cM. Tentative alignments of the two parental maps have been made on the basis of shared SSR alleles and linkages to double-simplex markers segregating in both parents. Tentative alignments of the two parental maps have been made on the basis of shared SSR alleles and linkages to double-simplex markers segregating in both parents.     

New generation pharmacogenomic tools: a SNP linkage disequilibrium Map,validated SNP assay resource,and high-throughput instrumentation system for large-scale genetic studies

Since public and private efforts announced the first draft of the human genome last year, researchers have reported great numbers of single nucleotide polymorphisms (SNPs). We believe that the availability of well-mapped, quality SNP markers constitutes the gateway to a revolution in genetics and personalized medicine that will lead to better diagnosis and treatment of common complex disorders. A new generation of tools and public SNP resources for pharmacogenomic and genetic studies--specifically for candidate-gene, candidate-region, and whole-genome association studies--will form part of the new scientific landscape. This will only be possible through the greater accessibility of SNP resources and superior high-throughput instrumentation-assay systems that enable affordable, highly productive large-scale genetic studies. We are contributing to this effort by developing a high-quality linkage disequilibrium SNP marker map and an accompanying set of ready-to-use, validated SNP assays across every gene in the human genome. This effort incorporates both the public sequence and SNP data sources, and Celera Genomics' human genome assembly and enormous resource ofphysically mapped SNPs (approximately 4,000,000 unique records). This article discusses our approach and methodology for designing the map, choosing quality SNPs, designing and validating these assays, and obtaining population frequency ofthe polymorphisms. We also discuss an advanced, high-performance SNP assay chemisty--a new generation of the TaqMan probe-based, 5' nuclease assay-and high-throughput instrumentation-software system for large-scale genotyping. We provide the new SNP map and validation information, validated SNP assays and reagents, and instrumentation systems as a novel resource for genetic discoveries.     

Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms

Characterization of genetic diversity is of great value to assist breeders in parental line selection and breeding system design. We screened 770 maize inbred lines with 1,034 single nucleotide polymorphism (SNP) markers and identified 449 high-quality markers with no germplasm-specific biasing effects. Pairwise comparisons across three distinct sets of germplasm, CIMMYT (394), China (282), and Brazil (94), showed that the elite lines from these diverse breeding pools have been developed with only limited utilization of genetic diversity existing in the center of origin. Temperate and tropical/subtropical germplasm clearly clustered into two separate groups. The temperate germplasm could be further divided into six groups consistent with known heterotic patterns. The greatest genetic divergence was observed between temperate and tropical/subtropical lines, followed by the divergence between yellow and white kernel lines, whereas the least divergence was observed between dent and flint lines. Long-term selection for hybrid performance has contributed to significant allele differentiation between heterotic groups at 20% of the SNP loci. There appeared to be substantial levels of genetic variation between different breeding pools as revealed by missing and unique alleles. Two SNPs developed from the same candidate gene were associated with the divergence between two opposite Chinese heterotic groups. Associated allele frequency change at two SNPs and their allele missing in Brazilian germplasm indicated a linkage disequilibrium block of 142 kb. These results confirm the power of SNP markers for diversity analysis and provide a feasible approach to unique allele discovery and use in maize breeding programs.     

SNP Discovery and Linkage Map Construction in Cultivated Tomato

Few intraspecific genetic linkage maps have been reported for cultivated tomato, mainly because genetic diversity within Solanum lycopersicum is much less than that between tomato species. Single nucleotide polymorphisms (SNPs), the most abundant source of genomic variation, are the most promising source of polymorphisms for the construction of linkage maps for closely related intraspecific lines. In this study, we developed SNP markers based on expressed sequence tags for the construction of intraspecific linkage maps in tomato. Out of the 5607 SNP positions detected through in silico analysis, 1536 were selected for high-throughput genotyping of two mapping populations derived from crosses between ‘Micro-Tom’ and either ‘Ailsa Craig’ or ‘M82’. A total of 1137 markers, including 793 out of the 1338 successfully genotyped SNPs, along with 344 simple sequence repeat and intronic polymorphism markers, were mapped onto two linkage maps, which covered 1467.8 and 1422.7 cM, respectively. The SNP markers developed were then screened against cultivated tomato lines in order to estimate the transferability of these SNPs to other breeding materials. The molecular markers and linkage maps represent a milestone in the genomics and genetics, and are the first step toward molecular breeding of cultivated tomato. Information on the DNA markers, linkage maps, and SNP genotypes for these tomato lines is available at http://www.kazusa.or.jp/tomato/.     

Relationship between five GLUT1 gene single nucleotide polymorphisms and diabetic nephropathy: a systematic review and meta-analysis

So far, case-control studies on the association between glucose transporter 1 (GLUT1) gene single nucleotide polymorphisms (SNPs) and diabetic nephropathy (DN) have generated considerable controversy. To clarify the linkage of GLUT1 SNPs on the risk of DN, a systematic review and meta-analysis was performed. A comprehensive literature search of electronic databases was conducted to obtain relative studies. Nine case-control studies were included. Significant differences were found between XbaI SNP (rs841853) and increased risk of DN in all genetic models. Subgroup analyses for Caucasians population and DN from both type 1 and type 2 diabetes also revealed positive results. For Enh2-1 SNP (rs841847), Enh2-2 SNP (rs841848) and HaeIII SNP (rs1385129), obvious linkages were demonstrated in recessive model. However, analysis for the association between HpyCH4V SNP (rs710218) and the susceptibility of DN showed no significance. Likewise, negative outcome was also found in the assessment for the influence of XbaI or Enh2-2 SNP on the pathogenesis progress of DN. The evidence currently available shows that XbaI, Enh2 and HaeIII SNPs, but not HpyCH4V SNP, in GLUT1 gene may be genetic susceptibility to DN. However, data does not support the association between either XbaI or Enh2-2 SNP and the severity of DN.     

Gene mapping in the wild with SNPs: guidelines and future directions

One of the biggest challenges facing evolutionary biologists is to identify and understand loci that explain fitness variation in natural populations. This review describes how genetic (linkage) mapping with single nucleotide polymorphism (SNP) markers can lead to great progress in this area. Strategies for SNP discovery and SNP genotyping are described and an overview of how to model SNP genotype information in mapping studies is presented. Finally, the opportunity afforded by new generation sequencing and typing technologies to map fitness genes by genome-wide association studies is discussed.     

Derived SNP alleles are used more frequently than ancestral alleles as risk-associated variants in common human diseases

Evolutionary aspects of the genetic architecture of common human diseases remain enigmatic. The results of more than 200 genome-wide association studies published to date were compiled in a catalog (). We used cataloged data to determine whether derived (mutant) alleles are associated with higher risk of human disease more frequently than ancestral alleles. We placed all allelic variants into ten categories of population frequency (0%-100%) in 10% increments. We then analyzed the relationship between allelic frequency, evolutionary status of the polymorphic site (ancestral versus derived), and disease risk status (risk versus protection). Given the same population frequency, derived alleles are more likely to be risk associated than ancestral alleles, as are rarer alleles. The common interpretation of this association is that negative selection prevents fixation of the risk variants. However, disease stratification as early or late onset suggests that weak selection against risk-associated alleles is unlikely a major factor shaping genetic architecture of common diseases. Our results clearly suggest that the duration of existence of an allele in a population is more important. Alleles existing longer tend to show weaker linkage disequilibrium with neighboring alleles, including the causal alleles, and are less likely to tag a SNP-disease association.     

Gene SNPs and mutations in clinical genetic testing: haplotype-based testing and analysis

Haplotype-based analysis using high-density single nucleotide polymorphism (SNP) markers have gained increasing attention in evaluating candidate genes in various clinical situations. For example, haplotype information is useful for predicting the severity and prognosis of certain genetic disorders. The intragenic cis-interactions between the common polymorphisms and the pathogenic mutations of prion protein (PRNP) and cystic fibrosis transmembrane conductance regulator (CFTR) genes greatly influence the phenotypes and the disease penetrance of hereditary Creutzfeldt-Jakob disease and cystic fibrosis. Merits of haplotype study are more evident in the fine mapping of complex diseases and in identifying genetic variations that influence individual's response to drugs. Knowledge-based approaches and/or linkage analyses using SNP tagged haplotypes are effective tools in detecting genetic associations. For example, haplotype studies in the inflammatory bowel disease susceptibility loci revealed diverse cis and trans gene-gene interactions, which can affect the clinical outcomes. Although currently, we have very limited knowledge on haplotype-phenotypic characterizations of most genes, these examples demonstrate that increased understanding of the clinically relevant haplotypes will provide better results in the diagnosis and possibly in the treatment of both monogenic and polygenic diseases.     

Depression in suicidal males: genetic risk variants in the CRHR1 gene

Dysregulation in the stress response of the hypothalamic–pituitary–adrenal axis, involving the corticotrophin-releasing hormone and its main receptor (CRHR1), is considered to play a major role in depression and suicidal behavior. To comprehensively map the genetic variation in CRHR1 in relation to suicidality and depression, as a follow-up to our initial report on SNP rs4792887, we analyzed six new single nucleotide polymorphisms (SNPs), in an extended sample of family trios ( n  = 672) with suicide attempter offspring, by using family-based association tests. The minor T-allele of exonic SNP rs12936511, not previously studied in the context of psychiatric disorders and suicidal behaviors, was significantly transmitted to suicidal males with increased Beck Depression Inventory (BDI) scores ( n  = 347; P  = 0.0028). We found additional evidence of association and linkage with increased BDI scores among suicidal males with an additional SNP, located proximally to the index SNP rs4792887, as well as with two distal SNPs, which were correlated with index SNP rs4792887. Analysis of haplotypes showed that each of the risk alleles segregated onto three separate haplotypes, whereas a fourth 'nonrisk' haplotype ('CGC') contained none of the risk alleles and was preferentially transmitted to suicidal males with lowered BDI scores ( P  = 0.0007). The BDI scores among all suicidal males, who carried a homozygous combination of any of the three risk haplotypes (non-CGC/non-CGC; n  = 160), were significantly increased ( P  = 0.000089) compared with suicidal male CGC carriers ( n  = 181). Thus, while the characteristics of the suicide female attempters remained undetermined, the male suicidal offspring had increased depression intensity related to main genetic effects by exonic SNP rs12936511 and homozygous non-CGC haplotypes.     

Anchoring of canine linkage groups with chromosome-specific markers

A high-resolution genetic map with polymorphic markers spaced frequently throughout the genome is a key resource for identifying genes that control specific traits or diseases. The lack of rigorous selection against genetic disorders has resulted in many breeds of dog suffering from a very high frequency of genetic diseases, which tend to be breed-specific and usually inherited as autosomal recessive or apparently complex genetic traits. Many of these closely resemble human genetic disorders in their clinical and pathologic features and are likely to be caused by mutations in homologous genes. To identify loci important in canine disease genes, as well as traits associated with morphological and behavioral variation, we are developing a genetic map of the canine genome. Here we report on an updated version of the canine linkage map, which includes 341 mapped markers distributed over the X and 37 autosomal linkage groups. The average distance between markers on the map is 9.0 cM, and the linkage groups provide estimated coverage of over 95% of the genome. Fourteen linkage groups contain either gene-associated or anonymous markers localized to cosmids that have been assigned to specific canine chromosomes by FISH. These 14 linkage groups contain 150 microsatellite markers and allow us to assign 40% of the linkage groups to specific canine chromosomes. This new version of the map is of sufficient density and characterization to initiate mapping of traits of interest. Received: 23 February 1999 / Accepted: 28 April 1999     

Score tests of genetic association in the presence of linkage based on the additive genetic gamma frailty model

Nuclear families with multiple affected sibs are often collected for genetic linkage analysis of complex diseases. Once linkage evidence is established, dense markers are often typed in the linked region for genetic association analysis based on linkage disequilibrium (LD). Detection of association in the presence of linkage localizes disease genes more accurately than the methods that rely on linkage alone. However, test of association due to LD in the linked region needs to account for dependency of the allele transmissions to different sibs within a family. In this paper, we define a joint model for genetic linkage and association and derive the corresponding joint survival function of age of onset for the sibs within a sibship. The joint survival function is a function of both the inheritance vector and the genotypes at the candidate marker locus. Based on this joint survival function, we derive score tests for genetic association. The proposed methods utilize the phenotype data of all the sibs and have the advantages of family-based designs which can avoid the potential spurious association caused by population admixture. In addition, the methods can account for variable age of onset or age at censoring and possible covariate effects, and therefore provide important tools for modelling disease heterogeneity. Simulation studies and application to the data sets from the 12th Genetic Analysis Workshop indicate that the proposed methods have correct type 1 error rates and increased power over other existing methods for testing allelic association.