Genetically indistinguishable SNPs and their influence on inferring the location of disease-associated variants

Sporadic, Global Linkage Disequilibrium Between Unlinked Segregating Sites

Demographic, genetic, or stochastic factors can lead to perfect linkage disequilibrium (LD) between alleles at two loci without respect to the extent of their physical distance, a phenomenon that Lawrence et al. (2005a) refer to as "genetic indistinguishability." This phenomenon can complicate genotype-phenotype association testing by hindering the ability to localize causal alleles, but has not been thoroughly explored from a theoretical perspective or using large, dense whole-genome polymorphism data sets. We derive a simple theoretical model of the prevalence of genetic indistinguishability between unlinked loci and verify its accuracy via simulation. We show that sample size and minor allele frequency are the major determinants of the prevalence of perfect LD between unlinked loci but that demographic factors, such as deviations from random mating, can produce significant effects as well. Finally, we quantify this phenomenon in three model organisms and find thousands of pairs of moderate-frequency ([Formula: see text]) genetically indistinguishable variants in relatively large data sets. These results clarify a previously underexplored population genetic phenomenon with important implications for association studies and define conditions under which it is likely to manifest.

Identifying causal variants at loci with multiple signals of association

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/.

Determining causality and consequence of expression quantitative trait loci

Expression quantitative trait loci (eQTLs) are currently the most abundant and systematically-surveyed class of functional consequence for genetic variation. Recent genetic studies of gene expression have identified thousands of eQTLs in diverse tissue types for the majority of human genes. Application of this large eQTL catalog provides an important resource for understanding the molecular basis of common genetic diseases. However, only now has both the availability of individuals with full genomes and corresponding advances in functional genomics provided the opportunity to dissect eQTLs to identify causal regulatory variants. Resolving the properties of such causal regulatory variants is improving understanding of the molecular mechanisms that influence traits and guiding the development of new genome-scale approaches to variant interpretation. In this review, we provide an overview of current computational and experimental methods for identifying causal regulatory variants and predicting their phenotypic consequences.

From single-SNP to wide-locus: genome-wide association studies identifying functionally related genes and intragenic regions in small sample studies

BACKGROUND

Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts.

METHODS

Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence).

RESULTS

By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes.

CONCLUSION

With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.

Linkage-disequilibrium-based binning affects the interpretation of GWASs

Genome-wide association studies (GWASs) are critically dependent on detailed knowledge of the pattern of linkage disequilibrium (LD) in the human genome. GWASs generate lists of variants, usually SNPs, ranked according to the significance of their association to a trait. Downstream analyses generally focus on the gene or genes that are physically closest to these SNPs and ignore their LD profile with other SNPs. We have developed a flexible R package (LDsnpR) that efficiently assigns SNPs to genes on the basis of both their physical position and their pairwise LD with other SNPs. We used the positional-binning and LD-based-binning approaches to investigate whether including these "LD-based" SNPs would affect the interpretation of three published GWASs on bipolar affective disorder (BP) and of the imputed versions of two of these GWASs. We show how including LD can be important for interpreting and comparing GWASs. In the published, unimputed GWASs, LD-based binning effectively "recovered" 6.1%-8.3% of Ensembl-defined genes. It altered the ranks of the genes and resulted in nonnegligible differences between the lists of the top 2,000 genes emerging from the two binning approaches. It also improved the overall gene-based concordance between independent BP studies. In the imputed datasets, although the increases in coverage (>0.4%) and rank changes were more modest, even greater concordance between the studies was observed, attesting to the potential of LD-based binning on imputed data as well. Thus, ignoring LD can result in the misinterpretation of the GWAS findings and have an impact on subsequent genetic and functional studies.

Ancestry-shift refinement mapping of the C6orf97-ESR1 breast cancer susceptibility locus

We used an approach that we term ancestry-shift refinement mapping to investigate an association, originally discovered in a GWAS of a Chinese population, between rs2046210[T] and breast cancer susceptibility. The locus is on 6q25.1 in proximity to the C6orf97 and estrogen receptor α (ESR1) genes. We identified a panel of SNPs that are correlated with rs2046210 in Chinese, but not necessarily so in other ancestral populations, and genotyped them in breast cancer case∶control samples of Asian, European, and African origin, a total of 10,176 cases and 13,286 controls. We found that rs2046210[T] does not confer substantial risk of breast cancer in Europeans and Africans (OR = 1.04, P = 0.099, and OR = 0.98, P = 0.77, respectively). Rather, in those ancestries, an association signal arises from a group of less common SNPs typified by rs9397435. The rs9397435[G] allele was found to confer risk of breast cancer in European (OR = 1.15, P = 1.2×10⁻³), African (OR = 1.35, P = 0.014), and Asian (OR = 1.23, P = 2.9×10⁻⁴) population samples. Combined over all ancestries, the OR was 1.19 (P = 3.9×10⁻⁷), was without significant heterogeneity between ancestries (P_het = 0.36) and the SNP fully accounted for the association signal in each ancestry. Haplotypes bearing rs9397435[G] are well tagged by rs2046210[T] only in Asians. The rs9397435[G] allele showed associations with both estrogen receptor positive and estrogen receptor negative breast cancer. Using early-draft data from the 1,000 Genomes project, we found that the risk allele of a novel SNP (rs77275268), which is closely correlated with rs9397435, disrupts a partially methylated CpG sequence within a known CTCF binding site. These studies demonstrate that shifting the analysis among ancestral populations can provide valuable resolution in association mapping.

In genome-wide association studies of disease susceptibility, there is no particular expectation that a genotyped SNP showing an association is itself a pathogenic variant. Rather, it is more likely that a SNP giving a signal does so because it is in linkage disequilibrium (LD) with a pathogenic variant. When the analysis is shifted to a population of another ancestry, the tagging relationship between the genotyped SNP and the pathogenic variant may be disrupted, due to differing patterns of LD between populations. Thus, it is not straightforward to determine whether a susceptibility locus identified in one ancestral population is also associated with risk in another. Moreover, the differing patterns of LD between ancestral populations can be used to gain resolution in genetic mapping. We refer to this approach as ancestry-shift refinement mapping. Here, we apply it to a breast cancer risk variant near the estrogen receptor α gene that was initially described in a Chinese population. We show that the tagging relationship between the originally described SNP rs2046210 and the pathogenic variant(s) is not maintained in Europeans and Africans. We identify a SNP, rs9397435, that is associated with breast cancer risk in populations of Asian, European, and African ancestry.

Leveraging genetic variability across populations for the identification of causal variants

Genome-wide association studies have been performed extensively in the last few years, resulting in many new discoveries of genomic regions that are associated with complex traits. It is often the case that a SNP found to be associated with the condition is not the causal SNP, but a proxy to it as a result of linkage disequilibrium. For the identification of the actual causal SNP, fine-mapping follow-up is performed, either with the use of dense genotyping or by sequencing of the region. In either case, if the causal SNP is in high linkage disequilibrium with other SNPs, the fine-mapping procedure will require a very large sample size for the identification of the causal SNP. Here, we show that by leveraging genetic variability across populations, we significantly increase the localization success rate (LSR) for a causal SNP in a follow-up study that involves multiple populations as compared to a study that involves only one population. Thus, the average power for detection of the causal variant will be higher in a joint analysis than that in studies in which only one population is analyzed at a time. On the basis of this observation, we developed a framework to efficiently search for a follow-up study design: our framework searches for the best combination of populations from a pool of available populations to maximize the LSR for detection of a causal variant. This framework and its accompanying software can be used to considerably enhance the power of fine-mapping studies.

Genome wide high density SNP-based linkage analysis of childhood absence epilepsy identifies a susceptibility locus on chromosome 3p23-p14

Childhood absence epilepsy (CAE) is an idiopathic generalised epilepsy (IGE) characterised by typical absence seizures manifested by transitory loss of awareness with 2.5-4 Hz spike-wave complexes on ictal EEG. A genetic component to the aetiology is well recognised but the mechanism of inheritance and the genes involved are yet to be fully established. A genome wide single nucleotide polymorphism (SNP)-based high density linkage scan was carried out using 41 nuclear pedigrees with at least two affected members. Multipoint parametric and non-parametric linkage analyses were performed using MERLIN 1.1.1 and a susceptibility locus was identified on chromosome 3p23-p14 (Z(mean)=3.9, p<0.0001; HLOD=3.3, alpha=0.7). The linked region harbours the functional candidate genes TRAK1 and CACNA2D2. Fine-mapping using a tagSNP approach demonstrated disease association with variants in TRAK1.

Genetic factors in autoimmune myasthenia gravis

Autoimmune myasthenia gravis (MG) is a multifactorial disease, markedly influenced by genetic factors, even though it shows limited heritability. The clinically typical form of autoimmune MG with thymus hyperplasia shows the most reproducible genetic associations, especially with the A1-B8-DR3 (8.1) haplotype of the major histocompatibility complex (MHC). However, because of strong linkage disequilibrium, the causative polymorphism in this region is not known yet. Increasing the density of genetic markers has nevertheless recently revealed the complex, but highly significant contribution of this essential genetic region in controlling the disease phenotype and the quantitative expression of serum autoantibodies. The advances of the human genome program, the development of genotyping and sequencing tools with increasing throughput, and the availability of powerful statistical methods now make feasible the dissection of a complex genetic region, such as the MHC and beyond, the systematic search throughout the genome for variants influencing disease predisposition. The identification of such functional variants should provide new clues to the pathogenesis of MG, as recently illustrated by the study of a promoter polymorphism of the CHRNA1 locus, influencing its thymic expression and central tolerance, or of a coding variant of the PTPN22 intracellular phosphatase.

Genetic architecture of transcript-level variation in humans

We report here the results of testing the pairwise association of 12,747 transcriptional gene-expression values with more than two million single-nucleotide polymorphisms (SNPs) in samples of European (CEPH from Utah; CEU) and African (Yoruba from Ibadan; YRI) ancestry. We found 4,677 and 5,125 significant associations between expression quantitative nucleotides (eQTNs) and transcript clusters in the CEU and the YRI samples, respectively. The physical distance between an eQTN and its associated transcript cluster was referred to as the intrapair distance. An association with 4 Mb or less intrapair distance was defined as local; otherwise, it was defined as distant. The enrichment analysis of functional categories shows that genes harboring the local eQTNs are enriched in the categories related to nucleosome and chromatin assembly; the genes harboring the distant eQTNs are enriched in the categories related to transmembrane signal transduction, suggesting that these biological pathways are likely to play a significant role in regulation of gene expression. We highlight in the EPHX1 gene a deleterious nonsynonymous SNP that is distantly associated with gene expression of ORMDL3, a susceptibility gene for asthma.

The PTPN22 promoter polymorphism -1123G>C association cannot be distinguished from the 1858C>T association in a Norwegian rheumatoid arthritis material

The protein tyrosine phosphatase nonreceptor 22 (PTPN22) gene has, during the last 2 years, been recognized as a susceptibility gene for numerous autoimmune diseases, including rheumatoid arthritis (RA) and type 1 diabetes. An association between the exonic 1858C>T single nucleotide polymorphism (SNP) and RA has repeatedly been replicated in several Caucasian populations. The SNP is not associated with autoimmune diseases in Asian populations, as the 1858T allele is almost absent. Recently, a promoter polymorphism -1123G>C was proposed to be associated with acute-onset type 1 diabetes in Japanese and Korean populations. Furthermore, in Caucasian populations, the presence of additional PTPN22 risk variants has been suggested, indicating that the 1858C>T risk variant cannot explain the entire disease association observed in the region. In this study, we wanted to jointly address and integrate these separate findings to further elucidate the association between the PTPN22 gene and RA in a Norwegian material of 861 RA patients and 559 healthy controls. Our results revealed that the strength of the association with the PTPN22 promoter polymorphism, -1123G>C, is analogous to that observed for 1858C>T. As the -1123G>C variant is also polymorphic in Asian populations, our data underpin the need to further explore the association between this variant and autoimmune diseases in different populations.

Polymorphisms in the cathepsin L2 (CTSL2) gene show association with type 1 diabetes and early-onset myasthenia gravis

Type 1 diabetes (T1D) is an autoimmune disease characterized by loss of beta cells in the pancreas. The CTSL2 gene encodes the cysteine protease cathepsin V involved in antigen presentation in human cortical thymic epithelial cells, and involvement of the protease in autoimmunity has been suggested. This study aimed to evaluate CTSL2 as a candidate gene for T1D, and test whether the gene predisposes more generally to autoimmune diseases. Four polymorphisms aiming at tagging the CTSL2 locus were genotyped in 421 T1D families, and subsequently in 861 rheumatoid arthritis patients, 530 juvenile idiopathic arthritis patients, and 559 controls of Norwegian origin. Additionally, DNA from 83 German myasthenia gravis (MG) patients and 244 controls were investigated. A polymorphism, rs16919034, situated downstream of CTSL2 was associated with T1D (60.8%T, p = 0.008; p(c) = 0.03). An association with early-onset MG (45% in cases vs 36.6% in controls; p = 0.03) was observed for another polymorphism (rs4361859) situated upstream of the gene, but within the same linkage disequilibrium block. No association was observed in rheumatoid arthritis or juvenile idiopathic arthritis. Our findings suggest that the CTSL2 gene is associated with T1D and with early-onset MG.

Cis sequence effects on gene expression

Background

Sequence and transcriptional variability within and between individuals are typically studied independently. The joint analysis of sequence and gene expression variation (genetical genomics) provides insight into the role of linked sequence variation in the regulation of gene expression. We investigated the role of sequence variation in cis on gene expression (cis sequence effects) in a group of genes commonly studied in cancer research in lymphoblastoid cell lines. We estimated the proportion of genes exhibiting cis sequence effects and the proportion of gene expression variation explained by cis sequence effects using three different analytical approaches, and compared our results to the literature.

Results

We generated gene expression profiling data at N = 697 candidate genes from N = 30 lymphoblastoid cell lines for this study and used available candidate gene resequencing data at N = 552 candidate genes to identify N = 30 candidate genes with sufficient variance in both datasets for the investigation of cis sequence effects. We used two additive models and the haplotype phylogeny scanning approach of Templeton (Tree Scanning) to evaluate association between individual SNPs, all SNPs at a gene, and diplotypes, with log-transformed gene expression. SNPs and diplotypes at eight candidate genes exhibited statistically significant (p < 0.05) association with gene expression. Using the literature as a "gold standard" to compare 14 genes with data from both this study and the literature, we observed 80% and 85% concordance for genes exhibiting and not exhibiting significant cis sequence effects in our study, respectively.

Conclusion

Based on analysis of our results and the extant literature, one in four genes exhibits significant cis sequence effects, and for these genes, about 30% of gene expression variation is accounted for by cis sequence variation. Despite diverse experimental approaches, the presence or absence of significant cis sequence effects is largely supported by previously published studies.

DTNBP1 (Dystrobrevin binding protein 1) and schizophrenia: association evidence in the 3' end of the gene

OBJECTIVES

Dysbindin (DTNBP1) has been identified as a susceptibility gene for schizophrenia (SZ) through a positional approach. However, a variety of single nucleotide polymorphisms (SNPs) and haplotypes, in different parts of the gene, have been reported to be associated in different samples, and a precise molecular mechanism of disease remains to be defined. We have performed an association study with two well-characterized family samples not previously investigated at the DTNBP1 locus.

METHODS

We examined 646 subjects in 136 families with SZ, largely of European ancestry (EA), genotyping 26 SNPs in DTNBP1.

RESULTS

Three correlated markers (rs875462, rs760666, and rs7758659) at the 3' region of DTNBP1 showed evidence for association to SZ (p = 0.004), observed in both the EA (p = 0.031) and the African American (AA) subset (p = 0.045) with the same over-transmitted allele. The most significant haplotype in our study was rs7758659-rs3213207 (global p = 0.0015), with rs3213207 being the most frequently reported associated marker in previous studies. A non-conservative missense variant (Pro272Ser) in the 3' region of DTNBP1 that may impair DTNBP1 function was more common in SZ probands (8.2%) than in founders (5%) and in dbSNP (2.1%), but did not reach statistical significance.

CONCLUSION

Our results provide evidence for an association of SZ with SNPs at the 3' end of DTNBP1 in the samples studied.

Comparative Assessment of the Association Information Captured by SNP Tagging

Exploiting the association between single nucleotide polymorphisms (SNP) can potentially reduce the costs of association mapping of common disease genes. Different methods have been proposed for defining subsets of SNPs as proxies (or tagSNPs) for other SNPs, some of which rely upon a model of haplotype blocks. Other approaches only consider the pair-wise correlation between markers as a basis for selecting tagSNPs. Yet another, recently proposed model-based method takes marker heterozygosity and genetic distance into account in order to maximize the expected utility of a marker set to map frequent, but unobserved genetic variants. We compared these tagging approaches with regard to their ability to correlate tagSNPs and bi-allelic, potentially disease-causing genetic variants. We used the CEU sample of chromosome 19 from the HapMap project for an initial comparison, and demonstrated a comparable performance of both approaches but a difference in terms of tagSNPs selected and variants captured. In any case, we conclude that a considerable loss of information appears to be inherent to any type of SNP tagging, even when dense marker sets are available for SNP selection.

Linkage and association analysis of CACNG3 in childhood absence epilepsy

Childhood absence epilepsy (CAE) is an idiopathic generalised epilepsy characterised by absence seizures manifested by transitory loss of awareness with 2.5-4 Hz spike-wave complexes on ictal EEG. A genetic component to aetiology is established but the mechanism of inheritance and the genes involved are not fully defined. Available evidence suggests that genes encoding brain expressed voltage-gated calcium channels, including CACNG3 on chromosome 16p12-p13.1, may represent susceptibility loci for CAE. The aim of this work was to further evaluate CACNG3 as a susceptibility locus by linkage and association analysis. Assuming locus heterogeneity, a significant HLOD score (HLOD = 3.54, alpha = 0.62) was obtained for markers encompassing CACNG3 in 65 nuclear families with a proband with CAE. The maximum non-parametric linkage score was 2.87 (P < 0.002). Re-sequencing of the coding exons in 59 patients did not identify any putative causal variants. A linkage disequilibrium (LD) map of CACNG3 was constructed using 23 single nucleotide polymorphisms (SNPs). Transmission disequilibrium was sought using individual SNPs and SNP-based haplotypes with the pedigree disequilibrium test in 217 CAE trios and the 65 nuclear pedigrees. Evidence for transmission disequilibrium (P < or = 0.01) was found for SNPs within a approximately 35 kb region of high LD encompassing the 5'UTR, exon 1 and part of intron 1 of CACNG3. Re-sequencing of this interval was undertaken in 24 affected individuals. Seventy-two variants were identified: 45 upstream; two 5'UTR; and 25 intronic SNPs. No coding sequence variants were identified, although four variants are predicted to affect exonic splicing. This evidence supports CACNG3 as a susceptibility locus in a subset of CAE patients.

ssSNPer: identifying statistically similar SNPs to aid interpretation of genetic association studies

ssSNPer is a novel user-friendly web interface that provides easy determination of the number and location of untested HapMap SNPs, in the region surrounding a tested HapMap SNP, which are statistically similar and would thus produce comparable and perhaps more significant association results. Identification of ssSNPs can have crucial implications for the interpretation of the initial association results and the design of follow-up studies.

AVAILABILITY

http://fraser.qimr.edu.au/general/daleN/ssSNPer/

Matrix-assisted laser desorption/ionisation, time-of-flight mass spectrometry in genomics research

The beginning of this millennium has seen dramatic advances in genomic research. Milestones such as the complete sequencing of the human genome and of many other species were achieved and complemented by the systematic discovery of variation at the single nucleotide (SNP) and whole segment (copy number polymorphism) level. Currently most genomics research efforts are concentrated on the production of whole genome functional annotations, as well as on mapping the epigenome by identifying the methylation status of CpGs, mainly in CpG islands, in different tissues. These recent advances have a major impact on the way genetic research is conducted and have accelerated the discovery of genetic factors contributing to disease. Technology was the critical driving force behind genomics projects: both the combination of Sanger sequencing with high-throughput capillary electrophoresis and the rapid advances in microarray technologies were keys to success. MALDI-TOF MS–based genome analysis represents a relative newcomer in this field. Can it establish itself as a long-term contributor to genetics research, or is it only suitable for niche areas and for laboratories with a passion for mass spectrometry? In this review, we will highlight the potential of MALDI-TOF MS–based tools for resequencing and for epigenetics research applications, as well as for classical complex genetic studies, allele quantification, and quantitative gene expression analysis. We will also identify the current limitations of this approach and attempt to place it in the context of other genome analysis technologies.

The influence of recombination on human genetic diversity

In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale. Despite considerable debate, it is not clear whether these factors are causally linked or, if they are, whether this is driven by the repeated action of adaptive evolution or molecular processes such as double-strand break formation and mismatch repair. We introduce three innovations to the analysis of recombination and diversity: fine-scale genetic maps estimated from genotype experiments that identify recombination hotspots at the kilobase scale, analysis of an entire human chromosome, and the use of wavelet techniques to identify correlations acting at different scales. We show that recombination influences genetic diversity only at the level of recombination hotspots. Hotspots are also associated with local increases in GC content and the relative frequency of GC-increasing mutations but have no effect on substitution rates. Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition. To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations. However, that hotspots have no influence on substitution rates suggests that they are too ephemeral on an evolutionary time scale to have a strong influence on broader scale patterns of base composition and long-term molecular evolution.

Patterns of genetic variation in the human genome provide a history of the evolutionary forces that have shaped our species. The role of one factor, recombination, in shaping variation is much debated. The observation is that regions of the genome with high recombination also have high levels of genetic variation, but this pattern can be interpreted as evidence for either repeated, widespread adaptive evolution or correlation through neutral factors such as base composition. To resolve this issue, the authors constructed a genetic map of human Chromosome 20 that has a resolution more than three orders in magnitude greater than previous maps. By comparing the location of recombination hotspots with patterns of genetic variation, evolution, and base composition, the authors show that recombination has only a very local influence on diversity, which suggests that molecular mechanisms, such as mismatch-associated repair or double-strand break formation, not adaptive evolution, drives the association.

MIDAS: software for analysis and visualisation of interallelic disequilibrium between multiallelic markers

Background

Various software tools are available for the display of pairwise linkage disequilibrium across multiple single nucleotide polymorphisms. The HapMap project also presents these graphics within their website. However, these approaches are limited in their use of data from multiallelic markers and provide limited information in a graphical form.

Results

We have developed a software package (MIDAS – Multiallelic Interallelic Disequilibrium Analysis Software) for the estimation and graphical display of interallelic linkage disequilibrium. Linkage disequilibrium is analysed for each allelic combination (of one allele from each of two loci), between all pairwise combinations of any type of multiallelic loci in a contig (or any set) of many loci (including single nucleotide polymorphisms, microsatellites, minisatellites and haplotypes). Data are presented graphically in a novel and informative way, and can also be exported in tabular form for other analyses. This approach facilitates visualisation of patterns of linkage disequilibrium across genomic regions, analysis of the relationships between different alleles of multiallelic markers and inferences about patterns of evolution and selection.

Conclusion

MIDAS is a linkage disequilibrium analysis program with a comprehensive graphical user interface providing novel views of patterns of linkage disequilibrium between all types of multiallelic and biallelic markers.

Availability

Available from and

Contribution of clinical correlates and 13 C-reactive protein gene polymorphisms to interindividual variability in serum C-reactive protein level

BACKGROUND

Serum C-reactive protein (CRP) level is a heritable complex trait that predicts incident cardiovascular disease. We investigated the clinical and genetic sources of interindividual variability in serum CRP.

METHODS AND RESULTS

We studied serum CRP in 3301 Framingham Heart Study (FHS) participants (mean age 61 years, 53% women). Twelve clinical covariates explained 26% of the variability in CRP level, with body mass index alone explaining 15% (P<0.0001) of the variance. To investigate the influence of genetic variation at the CRP gene on CRP levels, we first constructed a dense linkage disequilibrium map for common single-nucleotide polymorphisms (SNPs) spanning the CRP locus (1 SNP every 850 bases, 26 kilobase [kb] genomic region). Thirteen CRP SNPs were genotyped in 1640 unrelated FHS participants with measured CRP levels. After adjustment for clinical covariates, 9 of 13 SNPs were associated with CRP level (P<0.05). To account for correlation among SNPs, we conducted forward stepwise selection among all 13 SNPs; a triallelic SNP (rs3091244) remained associated with CRP level (stepwise P<0.0001). The triallelic SNP (C-->T-->A; allele frequencies 62%, 31%, and 7%), located in the promoter sequence, explained 1.4% of total serum CRP variation; haplotypes harboring the minor T and A alleles of this SNP were associated with higher CRP level (haplotype P=0.0002 and 0.004).

CONCLUSIONS

In our community-based sample, clinical variables explained 26% of the interindividual variation in CRP, whereas a common triallelic CRP SNP contributed modestly. Studies of larger samples are warranted to assess the association of genetic variation in CRP and risk of cardiovascular disease.

Prospects and pitfalls in whole genome association studies

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype-phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.