Mapping genes for common diseases: the case for genetic (LD) maps

Genome-wide association of breast cancer: composite likelihood with imputed genotypes

We describe composite likelihood-based analysis of a genome-wide breast cancer case-control sample from the Cancer Genetic Markers of Susceptibility project. We determine 14 380 genome regions of fixed size on a linkage disequilibrium (LD) map, which delimit comparable levels of LD. Although the numbers of single-nucleotide polymorphisms (SNPs) are highly variable, each region contains an average of ∼35 SNPs and an average of ∼69 after imputation of missing genotypes. Composite likelihood association mapping yields a single P-value for each region, established by a permutation test, along with a maximum likelihood disease location, SE and information weight. For single SNP analysis, the nominal P-value for the most significant SNP (msSNP) requires substantial correction given the number of SNPs in the region. Therefore, imputing genotypes may not always be advantageous for the msSNP test, in contrast to composite likelihood. For the region containing FGFR2 (a known breast cancer gene) the largest χ(2) is obtained under composite likelihood with imputed genotypes (χ(2)(2) increases from 20.6 to 22.7), and compares with a single SNP-based χ(2)(2) of 19.9 after correction. Imputation of additional genotypes in this region reduces the size of the 95% confidence interval for location of the disease gene by ∼40%. Among the highest ranked regions, SNPs in the NTSR1 gene would be worthy of examination in additional samples. Meta-analysis, which combines weighted evidence from composite likelihood in different samples, and refines putative disease locations, is facilitated through defining fixed regions on an underlying LD map.

Pronounced inter- and intrachromosomal variation in linkage disequilibrium across the zebra finch genome

The extent of nonrandom association of alleles at two or more loci, termed linkage disequilibrium (LD), can reveal much about population demography, selection, and recombination rate, and is a key consideration when designing association mapping studies. Here, we describe a genome-wide analysis of LD in the zebra finch (Taeniopygia guttata) using 838 single nucleotide polymorphisms and present LD maps for all assembled chromosomes. We found that LD declined with physical distance approximately five times faster on the microchromosomes compared to macrochromosomes. The distribution of LD across individual macrochromosomes also varied in a distinct pattern. In the center of the macrochromosomes there were large blocks of markers, sometimes spanning tens of mega bases, in strong LD whereas on the ends of macrochromosomes LD declined more rapidly. Regions of high LD were not simply the result of suppressed recombination around the centromere and this pattern has not been observed previously in other taxa. We also found evidence that this pattern of LD has remained stable across many generations. The variability in LD between and within chromosomes has important implications for genome wide association studies in birds and for our understanding of the distribution of recombination events and the processes that govern them.

Approaches to the identification of susceptibility genes

Although previous studies have revealed a great deal about the genetic basis of susceptibility and resistance to parasite infection, there is now an opportunity to considerably enhance understanding through genome-wide association mapping. The application of association mapping to complex inheritance has recently become achievable given reduced costs, sophisticated genotyping platforms and powerful statistical methods which build upon increased knowledge of the linkage disequilibrium structure of the human genome. Linkage mapping and related approaches remain useful for the localization of the rarer genetic variants and candidate region association studies can be a very cost-effective route to progress. However, genome-wide association offers the greatest promise, despite the challenges posed by phenotype complexity, ensuring genotype coverage/quality and robust statistical analysis. The available approaches for mapping genes underlying susceptibility are reviewed here, emphasizing their relative merits and drawbacks and highlighting specific software tools and resources that enable successful mapping.

New technologies for ultra-high throughput genotyping in plants

Molecular genetic markers represent one of the most powerful tools for the analysis of plant genomes and the association of heritable traits with underlying genetic variation. Molecular marker technology has developed rapidly over the last decade, with the development of high-throughput genotyping methods. Two forms of sequence-based marker, simple sequence repeats (SSRs), also known as microsatellites and single nucleotide polymorphisms (SNPs) now predominate applications in modern plant genetic analysis, along the anonymous marker systems such as amplified fragment length polymorphisms (AFLPs) and diversity array technology (DArT). The reducing cost of DNA sequencing and increasing availability of large sequence data sets permits the mining of this data for large numbers of SSRs and SNPs. These may then be used in applications such as genetic linkage analysis and trait mapping, diversity analysis, association studies and marker-assisted selection. Here, we describe automated methods for the discovery of molecular markers and new technologies for high-throughput, low-cost molecular marker genotyping. Genotyping examples include multiplexing of SSRs using Multiplex-Ready marker technology (MRT); DArT genotyping; SNP genotyping using the Invader assay, the single base extension (SBE), oligonucleotide ligation assay (OLA) SNPlex system, and Illumina GoldenGate and Infinium methods.

Mining for SNPs and SSRs using SNPServer, dbSNP and SSR taxonomy tree

Molecular genetic markers represent one of the most powerful tools for the analysis of genomes and the association of heritable traits with underlying genetic variation. The development of high-throughput methods for the detection of single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) has led to a revolution in their use as molecular markers. The availability of large sequence data sets permits mining for these molecular markers, which may then be used for applications such as genetic trait mapping, diversity analysis and marker assisted selection in agriculture. Here we describe web-based automated methods for the discovery of SSRs using SSR taxonomy tree, the discovery of SNPs from sequence data using SNPServer and the identification of validated SNPs from within the dbSNP database. SSR taxonomy tree identifies pre-determined SSR amplification primers for virtually all species represented within the GenBank database. SNPServer uses a redundancy based approach to identify SNPs within DNA sequences. Following submission of a sequence of interest, SNPServer uses BLAST to identify similar sequences, CAP3 to cluster and assemble these sequences and then the SNP discovery software autoSNP to detect SNPs and insertion/deletion (indel) polymorphisms. The NCBI dbSNP database is a catalogue of molecular variation, hosting validated SNPs for several species within a public-domain archive.

Linkage disequilibrium maps and location databases

Effective application of association mapping for complex traits requires characterization of linkage disequilibrium (LD) patterns that reflect the dominant process of recombination and its duration in addition to the more subtle influences of mutation, selection, and genetic drift. Maps expressed in linkage disequilibrium units (LDUs) reflect the influences of these factors with the use of a modified version of Malecot's isolation-by-distance model. As a result, LDU maps are analogous to linkage maps in so far as their provision of an additive metric that is related to recombination and facilitates association-mapping studies. However, unlike linkage maps, LDUs also reflect the partly cumulative effects of multiple historical bottlenecks that account for substantial variations in LD patterns between populations. This chapter provides an overview of the data requirements and methodology used to construct LDU maps, their applications outside association mapping, and their integration into location databases.

Mechanistic role of a disease-associated genetic variant within the ADAM33 asthma susceptibility gene

BACKGROUND

ADAM33 has been identified as an asthma-associated gene in an out-bred population. Genetic studies suggested that the functional role of this metalloprotease was in airway remodeling. However, the mechanistic roles of the disease-associated SNPs have yet to be elucidated especially in the context of the pathophysiology of asthma. One disease-associated SNP, BC+1, which resides in intron BC toward the 5' end of ADAM33, is highly associated with the disease.

METHODS

The region surrounding this genetic variant was cloned into a model system to determine if there is a regulatory element within this intron that influences transcription.

RESULTS

The BC+1 protective allele did not impose any affect on the transcription of the reporter gene. However, the at-risk allele enforced such a repressive affect on the promoter that no protein product from the reporter gene was detected. These results indicated that there exists within intron BC a regulatory element that acts as a repressor for gene expression. Moreover, since SNP BC+1 is a common genetic variant, this region may interact with other undefined regulatory elements within ADAM33 to provide a rheostat effect, which modulates pre-mRNA processing. Thus, SNP BC+1 may have an important role in the modulation of ADAM33 gene expression.

CONCLUSION

These data provide for the first time a functional role for a disease-associated SNP in ADAM33 and begin to shed light on the deregulation of this gene in the pathophysiology of asthma.

Reliable allele detection using SNP-based PCR primers containing Locked Nucleic Acid: application in genetic mapping

BACKGROUND

The diploid, Solanum caripense, a wild relative of potato and tomato, possesses valuable resistance to potato late blight and we are interested in the genetic base of this resistance. Due to extremely low levels of genetic variation within the S. caripense genome it proved impossible to generate a dense genetic map and to assign individual Solanum chromosomes through the use of conventional chromosome-specific SSR, RFLP, AFLP, as well as gene- or locus-specific markers. The ease of detection of DNA polymorphisms depends on both frequency and form of sequence variation. The narrow genetic background of close relatives and inbreds complicates the detection of persisting, reduced polymorphism and is a challenge to the development of reliable molecular markers. Nonetheless, monomorphic DNA fragments representing not directly usable conventional markers can contain considerable variation at the level of single nucleotide polymorphisms (SNPs). This can be used for the design of allele-specific molecular markers. The reproducible detection of allele-specific markers based on SNPs has been a technical challenge.

RESULTS

We present a fast and cost-effective protocol for the detection of allele-specific SNPs by applying Sequence Polymorphism-Derived (SPD) markers. These markers proved highly efficient for fingerprinting of individuals possessing a homogeneous genetic background. SPD markers are obtained from within non-informative, conventional molecular marker fragments that are screened for SNPs to design allele-specific PCR primers. The method makes use of primers containing a single, 3'-terminal Locked Nucleic Acid (LNA) base. We demonstrate the applicability of the technique by successful genetic mapping of allele-specific SNP markers derived from monomorphic Conserved Ortholog Set II (COSII) markers mapped to Solanum chromosomes, in S. caripense. By using SPD markers it was possible for the first time to map the S. caripense alleles of 16 chromosome-specific COSII markers and to assign eight of the twelve linkage groups to consensus Solanum chromosomes.

CONCLUSION

The method based on individual allelic variants allows for a level-of-magnitude higher resolution of genetic variation than conventional marker techniques. We show that the majority of monomorphic molecular marker fragments from organisms with reduced heterozygosity levels still contain SNPs that are sufficient to trace individual alleles.

Automated discovery of single nucleotide polymorphism and simple sequence repeat molecular genetic markers

Molecular genetic markers represent one of the most powerful tools for the analysis of genomes. Molecular marker technology has developed rapidly over the last decade, and two forms of sequence-based markers, simple sequence repeats (SSRs), also known as microsatellites, and single nucleotide polymorphisms (SNPs), now predominate applications in modern genetic analysis. The availability of large sequence data sets permits mining for SSRs and SNPs, which may then be applied to genetic trait mapping and marker-assisted selection. Here, we describe Web-based automated methods for the discovery of these SSRs and SNPs from sequence data. SSRPrimer enables the real-time discovery of SSRs within submitted DNA sequences, with the concomitant design of PCR primers for SSR amplification. Alternatively, users may browse the SSR Taxonomy Tree to identify predetermined SSR amplification primers for any species represented within the GenBank database. SNPServer uses a redundancy-based approach to identify SNPs within DNA sequence data. Following submission of a sequence of interest, SNPServer uses BLAST to identify similar sequences, CAP3 to cluster and assemble these sequences, and then the SNP discovery software autoSNP to detect SNPs and insertion/deletion (indel) polymorphisms.

Linkage disequilibrium and association mapping: an introduction

The basis for recent developments on the characterization of the linkage-disequilibrium structure of the genome and the application of association mapping to genes for common human diseases is described. Patterns of linkage disequilibrium are now understood, for a number of human populations, in unprecedented detail. This information not only provides a vital resource for the design and execution of powerful association-mapping studies, but opens new avenues of research into the genetic history of human populations and the effects of natural selection, mutation, and recombination on the genomic landscape.

Selecting single-nucleotide polymorphisms for association studies with SNPbrowser software

The design of genetic association studies using single-nucleotide polymorphisms (SNPs) requires the selection of subsets of the variants providing high statistical power at a reasonable cost. SNPs must be selected to maximize the probability that a causative mutation is in linkage disequilibrium (LD) with at least one marker genotyped in the study. The HapMap Project performed a genome-wide survey of genetic variation with over 3 million SNPs typed in four populations, providing a rich resource to inform the design of association studies. A number of strategies have been proposed for the selection of SNPs based on observed LD, including construction of metric LD maps and the selection of haplotype-tagging SNPs. Power calculations are important at the study design stage to ensure successful results. Integrating these methods and annotations can be challenging: the algorithms required to implement these methods are complex to deploy, and all the necessary data and annotations are deposited in disparate databases. Here, we review the typical workflows for the selection of markers for association studies utilizing the SNPbrowser software, a freely available, stand-alone application that incorporates the HapMap database together with gene and SNP annotations. Selected SNPs are screened for their conversion potential to genotyping platforms, expediting the set up of genetic studies with an increased probability of success.

Power and Sample Size Calculations for Genetic Case/Control Studies Using Gene-Centric SNP Maps: Application to Human Chromosomes 6, 21, and 22 in Three Populations

Power and sample size calculations are critical parts of any research design for genetic association. We present a method that utilizes haplotype frequency information and average marker-marker linkage disequilibrium on SNPs typed in and around all genes on a chromosome. The test statistic used is the classic likelihood ratio test applied to haplotypes in case/control populations. Haplotype frequencies are computed through specification of genetic model parameters. Power is determined by computation of the test's non-centrality parameter. Power per gene is computed as a weighted average of the power assuming each haplotype is associated with the trait. We apply our method to genotype data from dense SNP maps across three entire chromosomes (6, 21, and 22) for three different human populations (African-American, Caucasian, Chinese), three different models of disease (additive, dominant, and multiplicative) and two trait allele frequencies (rare, common). We perform a regression analysis using these factors, average marker-marker disequilibrium, and the haplotype diversity across the gene region to determine which factors most significantly affect average power for a gene in our data. Also, as a 'proof of principle' calculation, we perform power and sample size calculations for all genes within 100 kb of the PSORS1 locus (chromosome 6) for a previously published association study of psoriasis. Results of our regression analysis indicate that four highly significant factors that determine average power to detect association are: disease model, average marker-marker disequilibrium, haplotype diversity, and the trait allele frequency. These findings may have important implications for the design of well-powered candidate gene association studies. Our power and sample size calculations for the PSORS1 gene appear consistent with published findings, namely that there is substantial power (>0.99) for most genes within 100 kb of the PSORS1 locus at the 0.01 significance level.

SNPServer: a real-time SNP discovery tool

SNPServer is a real-time flexible tool for the discovery of SNPs (single nucleotide polymorphisms) within DNA sequence data. The program uses BLAST, to identify related sequences, and CAP3, to cluster and align these sequences. The alignments are parsed to the SNP discovery software autoSNP, a program that detects SNPs and insertion/deletion polymorphisms (indels). Alternatively, lists of related sequences or pre-assembled sequences may be entered for SNP discovery. SNPServer and autoSNP use redundancy to differentiate between candidate SNPs and sequence errors. For each candidate SNP, two measures of confidence are calculated, the redundancy of the polymorphism at a SNP locus and the co-segregation of the candidate SNP with other SNPs in the alignment. SNPServer is available at .

Assessment of two flexible and compatible SNP genotyping platforms: TaqMan SNP Genotyping Assays and the SNPlex Genotyping System

In this review we describe the principles, protocols, and applications of two commercially available SNP genotyping platforms, the TaqMan SNP Genotyping Assays and the SNPlex Genotyping System. Combined, these two technologies meet the requirements of multiple SNP applications in genetics research and pharmacogenetics. We also describe a set of SNP selection tools and validated assay resources which we developed to accelerate the cycle of experimentation on these platforms. Criteria for selecting the more appropriate of these two genotyping technologies are presented: the genetic architecture of the trait of interest, the throughput required, and the number of SNPs and samples needed for a successful study. Overall, the TaqMan assay format is suitable for low- to mid-throughput applications in which a high assay conversion rate, simple assay workflow, and low cost of automation are desirable. The SNPlex Genotyping System, on the other hand, is well suited for SNP applications in which throughput and cost-efficiency are essential, e.g., applications requiring either the testing of large numbers of SNPs and samples, or the flexibility to select various SNP subsets.