The optimal measure of linkage disequilibrium reduces error in association mapping of affection status

Dissecting the genetics of complex inheritance: linkage disequilibrium mapping provides insight into Crohn disease

Family studies for Crohn disease (CD) report extensive linkage on chromosome 16q and pinpoint NOD2 as a possible causative locus. However, linkage is also observed in families that do not bear the most frequent NOD2 causative mutations, but no other signals on 16q have been found so far in published genome-wide association studies. Our aim is to identify this missing genetic contribution. We apply a powerful genetic mapping approach to the Wellcome Trust Case-Control Consortium and the National Institute of Diabetes and Digestive and Kidney Diseases genome-wide association data on CD. This method takes into account the underlying structure of linkage disequilibrium (LD) by using genetic distances from LD maps and provides a location for the causal agent. We find genetic heterogeneity within the NOD2 locus and also show an independent and unsuspected involvement of the neighboring gene, CYLD. We find associations with the IRF8 region and the region containing CDH1 and CDH3, as well as substantial phenotypic and genetic heterogeneity for CD itself. The genes are known to be involved in inflammation and immune dysregulation. These findings provide insight into the genetics of CD and suggest promising directions for understanding disease heterogeneity. The application of this method thus paves the way for understanding complex inheritance in general, leading to the dissection of different pathways and ultimately, personalized treatment.

On the uses and applications of the most commonly used measures of linkage disequilibrium from the comparative analysis of their statistical properties

BACKGROUND/OBJECTIVE

The analysis of linkage disequilibrium is relevant for the exploration of the structure and evolution of genomes and for the gene mapping of quantitative characters and human diseases. The strength of linkage disequilibrium between diallelic loci is commonly measured by the coefficients D' and r. Recent studies suggest that r is more useful than D' as a general measure of the strength of disequilibrium because it provides much more precise (lower sampling variance) and accurate (lower bias) estimates of disequilibrium. We compared for the first time the statistical properties of D' and r taking into account their differences in range.

METHODS

The sampling properties of D' and r were evaluated by simulation under a variety of realistic population conditions and varying sample sizes using standardised statistics that allow for comparisons of the precision, accuracy and efficiency of estimates with different ranges.

RESULTS

Simulations revealed that estimates of r do not tend to be significantly more precise, accurate or efficient than those of D' when compared by means of standardised statistics.

CONCLUSION

The supposed advantage of r over D' based on direct comparisons of their sampling distributions is more apparent than real. The obtained results are useful to assess the uses and applications of these widely used disequilibrium measures.

A Bayesian spatial multimarker genetic random-effect model for fine-scale mapping

Multiple markers in linkage disequilibrium (LD) are usually used to localize the disease gene location. These markers may contribute to the disease etiology simultaneously. In contrast to the single-locus tests, we propose a genetic random effects model that accounts for the dependence between loci via their spatial structures. In this model, the locus-specific random effects measure not only the genetic disease risk, but also the correlations between markers. In other words, the model incorporates this relation in both mean and covariance structures, and the variance components play important roles. We consider two different settings for the spatial relations. The first is our proposal, relative distance function (RDF), which is intuitive in the sense that markers nearby are likely to correlate with each other. The second setting is a common exponential decay function (EDF). Under each setting, the inference of the genetic parameters is fully Bayesian with Markov chain Monte Carlo (MCMC) sampling. We demonstrate the validity and the utility of the proposed approach with two real datasets and simulation studies. The analyses show that the proposed model with either one of two spatial correlations performs better as compared with the single locus analysis. In addition, under the RDF model, a more precise estimate for the disease locus can be obtained even when the candidate markers are fairly dense. In all simulations, the inference under the true model provides unbiased estimates of the genetic parameters, and the model with the spatial correlation structure does lead to greater confidence interval coverage probabilities.

Identification and replication of three novel myopia common susceptibility gene loci on chromosome 3q26 using linkage and linkage disequilibrium mapping

Refractive error is a highly heritable quantitative trait responsible for considerable morbidity. Following an initial genome-wide linkage study using microsatellite markers, we confirmed evidence for linkage to chromosome 3q26 and then conducted fine-scale association mapping using high-resolution linkage disequilibrium unit (LDU) maps. We used a preliminary discovery marker set across the 30-Mb region with an average SNP density of 1 SNP/15 kb (Map 1). Map 1 was divided into 51 LDU windows and additional SNPs were genotyped for six regions (Map 2) that showed preliminary evidence of multi-marker association using composite likelihood. A total of 575 cases and controls selected from the tails of the trait distribution were genotyped for the discovery sample. Malecot model estimates indicate three loci with putative common functional variants centred on MFN1 (180,566 kb; 95% confidence interval 180,505–180, 655 kb), approximately 156 kb upstream from alternate-splicing SOX2OT (182,595 kb; 95% CI 182,533–182,688 kb) and PSARL (184,386 kb; 95% CI 184,356–184,411 kb), with the loci showing modest to strong evidence of association for the Map 2 discovery samples (p<10⁻⁷, p<10⁻¹⁰, and p = 0.01, respectively). Using an unselected independent sample of 1,430 individuals, results replicated for the MFN1 (p = 0.006), SOX2OT (p = 0.0002), and PSARL (p = 0.0005) gene regions. MFN1 and PSARL both interact with OPA1 to regulate mitochondrial fusion and the inhibition of mitochondrial-led apoptosis, respectively. That two mitochondrial regulatory processes in the retina are implicated in the aetiology of myopia is surprising and is likely to provide novel insight into the molecular genetic basis of common myopia.

Successful gene mapping strategies for common disease continue to require careful consideration of basic study design with the advent of genome-wide association studies. Here, we take advantage of prior information that the heritability of the quantitative trait myopia in the general population is high and shows evidence of replicated linkage to chromosome 3q26. Based on this, we conducted a fine map linkage disequilibrium association study for the region, using a high-resolution genetic map derived from population-based HapMap Phase II data. For analysis, we used efficient multi-locus tests of association using single nucleotide polymorphism markers genotyped for our sample data and placed on the genetic map measured in linkage disequilibrium units. We followed up preliminary evidence of association for the discovery samples with further genotyping in the same samples to improve the model location estimates for the common functional variants we identified. Three locations were replicated using an independent sample. Two of the identified genes are likely to play an unexpected role in myopia with both pivotal in the healthy housekeeping metabolism of retinal mitochondria. Both proteins interact with OPA1, with nonsynonymous OPA1 mutations causing the unrelated Mendelian disease Autosomal Dominant Optic Atrophy (ADOA) by triggering mitochondrial-led retinal ganglia cell apoptosis.

Allelic association: linkage disequilibrium structure and gene mapping

The linkage disequilibrium (LD) structure of the human genome is now well understood and characterised for a number of human populations. The LD structure underpins the design and execution of candidate gene and genome-wide association mapping studies. Successful association mapping studies completed to date provide vital new insights into the genetic influences on common diseases, such as diabetes, some cancers and heart disease. The LD structure also presents new avenues of research into the genetic history of human populations, the effects of natural selection and the impact of recombination on the genomic landscape. This review introduces this exciting and complex field by encompassing this range of topics.

Genetic association mapping via evolution-based clustering of haplotypes

Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error.

Linkage disequilibrium--understanding the evolutionary past and mapping the medical future

Linkage disequilibrium--the nonrandom association of alleles at different loci--is a sensitive indicator of the population genetic forces that structure a genome. Because of the explosive growth of methods for assessing genetic variation at a fine scale, evolutionary biologists and human geneticists are increasingly exploiting linkage disequilibrium in order to understand past evolutionary and demographic events, to map genes that are associated with quantitative characters and inherited diseases, and to understand the joint evolution of linked sets of genes. This article introduces linkage disequilibrium, reviews the population genetic processes that affect it and describes some of its uses. At present, linkage disequilibrium is used much more extensively in the study of humans than in non-humans, but that is changing as technological advances make extensive genomic studies feasible in other species.

A comparison of methods to detect recombination hotspots

OBJECTIVE

A number of linkage disequilibrium (LD)-based methods have been developed to describe recombination and infer hotspots. We determine the correspondence between LDMAP and LDhat, and between LDMAP and LDhot by comparison with linkage maps and hotspots that have been verified by sperm typing.

METHODS

Regression and variance analyses were used to compare LDMAP and LDhat with linkage maps. The location and intensity of hotspots inferred by LDMAP and LDhot were compared with fifteen verified hotspots.

RESULTS

Despite different methodologies and assumptions, LDMAP, LDhat, and linkage maps are highly concordant. Closer inspection shows that LDMAP corresponds more closely with linkage maps across the genome and on sixteen chromosomes compared with LDhat. LDhot identified fourteen and ten of the verified hotspots using high and low density maps. In comparison, LDMAP identified all fifteen hotspots at high and low density. However, some significant discrepancies between sperm and LD-based recombination rates remain.

CONCLUSIONS

Combining information from linkage and LDMAP to construct sex-specific high resolution linkage maps suggests that some of these discrepancies may be due to female recombination while others may relate to the age of hotspots. LDMAP based estimates between approximately 68,000 and approximately 112,000 hotspots in the genome with mean widths less than 4 kb.

Hierarchical Bayes prioritization of marker associations from a genome-wide association scan for further investigation

We describe a hierarchical regression modeling approach to selection of a subset of markers from the first stage of a genomewide association scan to carry forward to subsequent stages for testing on an independent set of subjects. Rather than simply selecting a subset of most significant marker-disease associations at some cutoff chosen to maximize the cost efficiency of a multistage design, we propose a prior model for the true noncentrality parameters of these associations composed of a large mass at zero and a continuous distribution of nonzero values. The prior probability of nonzero values and their prior means can be functions of various covariates characterizing each marker, such as their location relative to genes or evolutionary conserved regions, or prior linkage or association data. We propose to take the top ranked posterior expectations of the noncentrality parameters for confirmation in later stages of a genomewide scan. The statistical performance of this approach is compared with the traditional p-value ranking by simulation studies. We show that the ranking by posterior expectations performs better at selecting the true positive association than a simple ranking of p-values if at least some of the prior covariates have predictive value.

Closely linked cis-acting modifier of expansion of the CGG repeat in high risk FMR1 haplotypes

In its expanded form, the fragile X triplet repeat at Xq27.3 gives rise to the most common form of inherited mental retardation, fragile X syndrome. This high population frequency persists despite strong selective pressure against mutation-bearing chromosomes. Males carrying the full mutation rarely reproduce and females heterozygous for the premutation allele are at risk of premature ovarian failure. Our diagnostic facility and previous research have provided a large databank of X chromosomes that have been tested for the FRAXA allele. Using this resource, we have conducted a detailed genetic association study of the FRAXA region to determine any cis-acting factors that predispose to expansion of the CGG triplet repeat. We have genotyped SNP variants across a 650-kb tract centered on FRAXA in a sample of 877 expanded and normal X chromosomes. These chromosomes were selected to be representative of the haplotypic diversity encountered in our population. We found expansion status to be strongly associated with a ∼50-kb region proximal to the fragile site. Subsequent detailed analyses of this region revealed no specific genetic determinants for the whole population. However, stratification of chromosomes by risk subgroups enabled us to identify a common SNP variant which cosegregates with the subset of D group haplotypes at highest risk of expansion (, p=0.00002). We have verified that this SNP acts as a marker of repeat expansion in three independent samples. Hum Mutat 28(12), 1216–1224, 2007. © 2007 Wiley-Liss, Inc.

The promise of composite likelihood methods for addressing computationally intensive challenges

High-dimensional genetic data, due to its complex correlation structure, poses an enormous challenge to standard likelihood-based methods for making statistical inference. As an approximation, composite likelihood has proved to be a successful strategy for some genetic applications. It has the potential to see even wider application and much research is needed. We first give a brief description of composite likelihood. The advantage of this method and potential challenges in inference are noted. Next, its applications in genetic studies are reviewed, specifically in estimating population genetics parameters such as recombination rate, and in multi-locus linkage disequilibrium mapping of disease genes with some discussion about future research directions.

Haplotype-association analysis

Association methods based on linkage disequilibrium (LD) offer a promising approach for detecting genetic variations that are responsible for complex human diseases. Although methods based on individual single nucleotide polymorphisms (SNPs) may lead to significant findings, methods based on haplotypes comprising multiple SNPs on the same inherited chromosome may provide additional power for mapping disease genes and also provide insight on factors influencing the dependency among genetic markers. Such insights may provide information essential for understanding human evolution and also for identifying cis-interactions between two or more causal variants. Because obtaining haplotype information directly from experiments can be cost prohibitive in most studies, especially in large scale studies, haplotype analysis presents many unique challenges. In this chapter, we focus on two main issues: haplotype inference and haplotype-association analysis. We first provide a detailed review of methods for haplotype inference using unrelated individuals as well as related individuals from pedigrees. We then cover a number of statistical methods that employ haplotype information in association analysis. In addition, we discuss the advantages and limitations of different methods.

Mapping a gene for rheumatoid arthritis on chromosome 18q21

Although single chi-square analysis of the North American Rheumatoid Arthritis Consortium (NARAC) data identifies many single-nucleotide polymorphisms (SNPs) with p-values less than 0.05, none remain significant after Bonferroni correction. In contrast, CHROMSCAN evades heavy Bonferroni correction and auto-correlation between SNPs by using composite likelihood to model association across all markers in a region and permutation to assess significance. Analysis by CHROMSCAN identifies a 36-kb interval that includes the most significant SNP (msSNP) observed in a 10-Mb target suggested by linkage. Unexpectedly, stratification by gender and age of onset shows that association evidence comes almost entirely from females with age of onset less than 40. Combining evidence from a meta-analysis of linkage studies and three subsets of the NARAC data provides significant evidence for a determinant of rheumatoid arthritis in a 36-kb interval and illustrates the principle that estimates of location and its information are more powerful than estimates of p-values alone.

Patterns of linkage disequilibrium between SNPs in a Sardinian population isolate and the selection of markers for association studies

OBJECTIVE

In isolated populations, 'background' linkage disequilibrium (LD) has been shown to extend over large genetic distances. This and their reduced environmental and genetic heterogeneity has stimulated interest in their potential for association mapping. We compared LD unit map distances with pair-wise measurements of LD in a dense single nucleotide polymorphism (SNP) set.

METHODS

We genotyped 771 SNPs in an 8 Mb segment of chromosome 22 on 101 individuals from the isolated village of Talana, Sardinia, and compared with outbred European populations.

RESULTS

Heterozygosity was remarkably similar in both populations. In contrast, the extent of LD observed was quite different. The decay of LD with distance is slower in the isolate. The differences in LD map lengths suggest that useful LD extends up to three times farther in the Sardinian population; smaller differences are seen with pairwise LD metrics. While LD map length slightly decreases with average relatedness, cryptic relatedness does not explain the decrease in LD map length. Haplotypes, block boundaries, and patterns of LD are similar in both populations, suggesting a shared distribution of recombination hotspots.

CONCLUSIONS

About 15% fewer haplotype tagging SNPs need to be genotyped in the isolate, and possibly 70% fewer if selecting SNPs evenly spaced on the metric LD map.

The application of the HapMap to diabetic nephropathy and other causes of chronic renal failure

The human nuclear genome consists of approximately 3 billion nucleotides. Human beings are 99% similar in DNA sequence to each other, but natural genetic variation in approximately 1% of the DNA sequence is responsible for interindividual differences, including determining who will develop disease and who will remain healthy. The pace and timing of disease initiation also is regulated by exposure to individual-level environmental factors and other random causes. Therefore, an examination of the DNA sequences of individuals with and without diabetic nephropathy, or, more broadly, chronic renal failure, can predict which sequence differences vary with disease (or health). The technology is not yet economical enough to analyze large numbers of individuals down to each nucleotide, but standardized dense genotyping sets for interrogating 1 marker for every 5,000, 10,000, or 15,000 nucleotides now are affordable even in large samples. The swiftness with which disease-gene associations can be mined has improved radically as a result of the availability of discovery human genetic variation data from large-scale public and private initiatives, such as those provided by the International Haplotype Map Consortium and Perlegen Sciences, Inc. (Mountain View, CA). These projects have captured many of the common genetic variants (>1%) in the genome. This information has been buttressed with improvements in large-scale genotyping technologies and statistical methods for data analysis. In summary, the renal community is now poised for discovery of genes for chronic renal failure using these resources.

Effects of single SNPs, haplotypes, and whole-genome LD maps on accuracy of association mapping

We describe an association mapping approach that utilizes linkage disequilibrium (LD) maps in LD units (LDU). This method uses composite likelihood to combine information from all single marker tests, and applies a model with a parameter for the location of the causal polymorphism. Previous analyses of the poor drug metabolizer phenotype provided evidence of the substantial utility of LDU maps for disease gene association mapping. Using LDU locations for the 27 single nucleotide polymorphisms (SNPs) flanking the CYP2D6 gene on chromosome 22, the most common functional polymorphism within the gene was located at 15 kb from its true location. Here, we examine the performance of this mapping approach by exploiting the high-density LDU map constructed from the HapMap data. Expressing the locations of the 27 SNPs in LDU from the HapMap LDU map, analysis yielded an estimated location that is only 0.3 kb away from the CYP2D6 gene. This supports the use of the high marker density HapMap-derived LDU map for association mapping even though it is derived from a much smaller number of individuals compared to the CYP2D6 sample. We also examine the performance of 2-SNP haplotypes. Using the same modelling procedures and composite likelihood as for single SNPs, the haplotype data provided much poorer localization compared to single SNP analysis. Haplotypes generate more autocorrelation through multiple inclusions of the same SNPs, which could inflate significance in association studies. The results of the present study demonstrate the great potential of the genome HapMap LDU maps for high-resolution mapping of complex phenotypes.

A spatial probit model for fine-scale mapping of disease genes

We present a novel statistical method for linkage disequilibrium (LD) mapping of disease susceptibility loci in case-control studies. Such studies exploit the statistical correlation or LD that exist between variants physically close along the genome to identify those that correlate with disease status and might thus be close to a causative mutation, generally assumed unobserved. LD structure, however, varies markedly over short distances because of variation in local recombination rates, mutation and genetic drift among other factors. We propose a Bayesian multivariate probit model that flexibly accounts for the local spatial correlation between markers. In a case-control setting, we use a retrospective model that properly reflects the sampling scheme and identify regions where single- or multi-locus marker frequencies differ across cases and controls. We formally quantify these differences using information-theoretic distance measures while the fully Bayesian approach naturally accommodates unphased or missing genotype data. We demonstrate our approach on simulated data and on real data from the CYP2D6 region that has a confirmed role in drug metabolism.

Fine-scale linkage disequilibrium mapping of age-related macular degeneration in the complement factor H gene region

AIM

To present results from a nested association study of the complement factor H (CFH) gene region using a novel methodology that uses a high-resolution genetic linkage disequilibrium map to estimate a point location for a causal mutation.

METHOD

Age-related macular degeneration (AMD) case-control data from a genomewide single-nucleotide polymorphism (SNP) panel were used to identify the target interval to be genotyped at higher density in a second independent panel. The pattern of linkage disequilibrium (LD) and segmental duplications across this region are described in detail.

RESULT

Data were consistent with other studies in that strong association between the Y402H variant and AMD is observed. However, composite likelihood analysis, which combines association data from all SNPs in the region, and uses genetic locations on a high-resolution LD map, gave a point location for a causal variant between exons 1 and 2 of the CFH gene.

CONCLUSION

The findings are consistent with evidence that, in addition to the widely described Y402H variant, there is at least one and, most probably, several other mutations in the CFH gene which determine disease manifestation in AMD. A genetic model in which multiple mutations contribute to a varying degree to disease aetiology has been previously well described in ophthalmic genetics, and is typified by the COL2A1 and ABCA4 genes.

Genome scanning by composite likelihood

Ambitious programs have recently been advocated or launched to create genomewide databases for meta-analysis of association between DNA markers and phenotypes of medical and/or social concern. A necessary but not sufficient condition for success in association mapping is that the data give accurate estimates of both genomic location and its standard error, which are provided for multifactorial phenotypes by composite likelihood. That class includes the Malecot model, which we here apply with an illustrative example. This preliminary analysis leads to five inferences: permutation of cases and controls provides a test of association free of autocorrelation; two hypotheses give similar estimates, but one is consistently more accurate; estimation of the false-discovery rate is extended to causal genes in a small proportion of regions; the minimal data for successful meta-analysis are inferred; and power is robust for all genomic factors except minor-allele frequency. An extension to meta-analysis is proposed. Other approaches to genome scanning and meta-analysis should, if possible, be similarly extended so that their operating characteristics can be compared.

Linkage disequilibrium maps and disease-association mapping

Over the last few years, association mapping of disease genes has developed into one of the most dynamic research areas of human genetics. It focuses on identifying functional polymorphisms that predispose to complex diseases. Population-based approaches are concerned with exploiting linkage disequilibrium (LD) between single-nucleotide polymorphism (SNPs) and disease-predisposing loci. The utility of SNPs in association mapping is now well established and the interest in this field has been escalated by the discovery of millions of SNPs across the genome. This chapter reviews an association-mapping method that utilizes metric LD maps in LD units and employs a composite likelihood approach to combine information from all single SNP tests. It applies a model that incorporates a parameter for the location of the causal polymorphism. A proof-of-principle application of this method to a small region is given and its potential properties to large-scale datasets are discussed.

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.

LDMAP: the construction of high-resolution linkage disequilibrium maps of the human genome

The precise characterization of the linkage disequilibrium (LD) landscape from high-density single-nucleotide polymorphism (SNP) data underpins the association mapping of diseases and other studies. We describe the algorithm and implementation of a powerful approach for constructing LD genetic maps with meaningful map distances. The computational problems posed by the enormous number of SNPs typed in the HapMap data are addressed by developing segmental map construction with the potential for parallelization, which we are developing. There is remarkably little loss of information (1-2%) through this approach, but the computation times are dramatically reduced (more than fourfold for sequential map assembly). These developments enable the construction of very high-density genome-wide LD maps using data from more than 3 million SNPs in HapMap. We anticipate that a whole-genome LD map will be useful for disease gene mapping, genomic research, and population genetics.

Exploiting large scale computing to construct high resolution linkage disequilibrium maps of the human genome

Linkage disequilibrium (LD) maps increase power and precision in association mapping, define optimal marker spacing and identify recombination hot-spots and regions influenced by natural selection. Phase II of HapMap provides approximately 2.8-fold more single nucleotide polymorphisms (SNPs) than phase I for constructing higher resolution maps. LDMAP-cluster, is a parallel program for rapid map construction in a Linux environment used here to construct genome-wide LD maps with >8.2 million SNPs from the phase II data.

AVAILABILITY

The LD maps, LDMAP-cluster and documentation are available from: http://www.som.soton.ac.uk/research/geneticsdiv/epidemiology/LDMAP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Refined Association Mapping for a Quantitative Trait: Weight in the H19-IGF2-INS-TH Region

Previous analyses have provided evidence for one or more loci affecting body weight in the H19-IGF2-INS-TH region on chromosome 11p15. To identify the location of a possible causal locus or loci we applied association analysis by composite likelihood to a large cohort under the Malecot model for body weight. A random sample of 2731 men in the UK were typed for eleven single nucleotide polymorphisms (SNPs) in IGF2, two SNPs in H19, one SNP in INS and one microsatellite marker in the TH genes. Using F tests appropriate to small marker sets, the superiority of regression over correlation was confirmed. All the evidence for association came from IGF2, with P= 0.007 for height-adjusted weight and P= 0.019 for weight additionally adjusted for smoking and alcohol drinking. Although the estimated point location for the suspected causal variant was close to IGF2 ApaI, the 95% confidence and support intervals covered most of IGF2 but none of the other loci. Identification of the causal SNP or SNPs within IGF2 will require typing of more variants in this region.

Bayesian method for gene detection and mapping, using a case and control design and DNA pooling

Association mapping studies aim to determine the genetic basis of a trait. A common experimental design uses a sample of unrelated individuals classified into 2 groups, for example cases and controls. If the trait has a complex genetic basis, consisting of many quantitative trait loci (QTLs), each group needs to be large. Each group must be genotyped at marker loci covering the region of interest; for dense coverage of a large candidate region, or a whole-genome scan, the number of markers will be very large. The total amount of genotyping required for such a study is formidable. A laboratory effort efficient technique called DNA pooling could reduce the amount of genotyping required, but the data generated are less informative and require novel methods for efficient analysis. In this paper, a Bayesian statistical analysis of the classic model of McPeek and Strahs is proposed. In contrast to previous work on this model, I assume that data are collected using DNA pooling, so individual genotypes are not directly observed, and also account for experimental errors. A complete analysis can be performed using analytical integration, a propagation algorithm for a hidden Markov model, and quadrature. The method developed here is both statistically and computationally efficient. It allows simultaneous detection and mapping of a QTL, in a large-scale association mapping study, using data from pooled DNA. The method is shown to perform well on data sets simulated under a realistic coalescent-with-recombination model, and is shown to outperform classical single-point methods. The method is illustrated on data consisting of 27 markers in an 880-kb region around the CYP2D6 gene.

Bayesian graphical models for genomewide association studies

As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single-locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single-nucleotide polymorphisms. Analysis is quick (<5 min), and we are able to localize the causative site to a very short interval.

Association mapping with single-feature polymorphisms

We develop methods for exploiting "single-feature polymorphism" data, generated by hybridizing genomic DNA to oligonucleotide expression arrays. Our methods enable the use of such data, which can be regarded as very high density, but imperfect, polymorphism data, for genomewide association or linkage disequilibrium mapping. We use a simulation-based power study to conclude that our methods should have good power for organisms like Arabidopsis thaliana, in which linkage disequilibrium is extensive, the reason being that the noisiness of single-feature polymorphism data is more than compensated for by their great number. Finally, we show how power depends on the accuracy with which single-feature polymorphisms are called.

Genetics of diabetic retinopathy

Diabetes is rapidly increasing in frequency with an attendant toll of complications, including diabetic retinopathy. Although the underlying mechanisms remain elusive, genetic susceptibility is key to both types 1 and 2 diabetes and is increasingly recognized for its contribution to diabetic complications. In this article we review the evidence connecting genetic susceptibility to diabetic retinopathy. Elucidating the susceptibility genes and pathways should permit strategies to slow and reverse the troubling trends for the population, families, and individuals.

Fifty years of genetic epidemiology, with special reference to Japan

Genetic epidemiology deals with etiology, distribution, and control of disease in groups of relatives and with inherited causes of disease in populations. It took its first steps before its recognition as a discipline, and did not reach its present scope until the Human Genome Project succeeded. The intimate relationship between genetics and epidemiology was discussed by Neel and Schull (1954), just a year after Watson and Crick reported the DNA double helix, and 2 years before human cytogenetics and the Japan Society of Human Genetics were founded. It is convenient to divide the next half-century into three phases. The first of these (1956-1979) was before DNA polymorphisms were typed, and so the focus was on segregation and linkage of major genes, cytogenetics, population studies, and biochemical genetics. The next phase (1980-2001) progressively identified DNA polymorphisms and their application to complex inheritance. The last phase began with a reliable sequence of the human genome (2002), followed by exploration of genomic diversity. Linkage continues to be useful to study recombination and to map major genes, but association mapping gives much greater resolution and enables studies of complex inheritance. The generation now entering human genetics will have collaborative opportunities undreamed of a few years ago, without the independence that led to great advances during the past half-century.

A map of the human genome in linkage disequilibrium units

Two genetic maps with additive distances contribute information about recombination patterns, recombinogenic sequences, and discovery of genes affecting a particular phenotype. Recombination is measured in morgans (w) over a single generation in a linkage map but may cover thousands of generations in a linkage disequilibrium (LD) map measured in LD units (LDU). We used a subset of single nucleotide polymorphisms from the HapMap Project to create a genome-wide map in LDU. Recombination accounts for 96.8% of the LDU variance in chromosome arms and 92.4% in their deciles. However, deeper analysis shows that LDU/w, an estimate of the effective bottleneck time (t), is significantly variable among chromosome arms because (i) the linkage map is approximated from the Haldane function, then adjusted toward the Kosambi function that is more accurate but still exaggerates w for all chromosomes, especially shorter ones; (ii) the non-pseudoautosomal region of the X chromosome is subject to hemizygous selection; and (iii) at resolution less than approximately 40,000 markers per w, there are indeterminacies (holes) in the LD map reflecting intervals of very high recombination. Selection and stochastic variation in small regions must have effects, which remain to be investigated by comparisons among populations. These considerations suggest an optimal strategy to eliminate holes quickly, greatly enhance the resolution of sex-specific linkage maps, and maximize the gain in association mapping by using LD maps.

Linkage disequilibrium maps and association mapping

The causal chain between a gene and its effect on disease susceptibility cannot be understood until the effect has been localized in the DNA sequence. Recently, polymorphisms incorporated in the HapMap Project have made linkage disequilibrium (LD) the most powerful tool for localization. The genetics of LD, the maps and databases that it provides, and their use for association mapping, as well as alternative methods for gene localization, are briefly described.

Polymorphisms in a disintegrin and metalloprotease 33 (ADAM33) predict impaired early-life lung function

RATIONALE

Asthma commonly originates in early life in association with impaired lung function, which tracks to adulthood.

OBJECTIVES

Within the context of a prospective birth cohort study, we investigated the association between single nucleotide polymorphisms (SNPs) in a disintegrin and metalloprotease 33 (ADAM33) gene and early-life lung function.

METHODS

Children were genotyped for 17 SNPs in ADAM33. Lung function at age 3 (n = 285) and 5 years (n = 470) was assessed using plethysmographic measurement of specific airway resistance (sRaw). At age 5, we also measured FEV(1). SNPs were analyzed individually using logistic regression, followed by linkage disequilibrium mapping to identify the causal locus.

MAIN RESULTS

Carriers of the rare allele of F+1 SNP had reduced lung function at age 3 years (p = 0.003). When the recessive model was considered, four SNPs (F+1, S1, ST+5, V4) showed association with sRaw at age 5 years (p < 0.04). Using linkage disequilibrium mapping, we found evidence of a significant causal location between BC+1 and F1 SNPs, at the 5' end of the gene. Four SNPs were associated with lower FEV(1) (F+1, M+1, T1, and T2; p < or = 0.04). The risk of transient early wheezing more than doubled among children homozygous for the A allele of F+1 (odds ratio, 2.39; 95% confidence intervals, 1.18-4.86; p = 0.02), but there was no association between any SNP and allergic sensitization or physician-diagnosed asthma.

CONCLUSIONS

Polymorphisms in ADAM33 predict impaired early-life lung function. The functionally relevant polymorphism is likely to be at the 5' end of the gene.

The linkage disequilibrium maps of three human chromosomes across four populations reflect their demographic history and a common underlying recombination pattern

The extent and patterns of linkage disequilibrium (LD) determine the feasibility of association studies to map genes that underlie complex traits. Here we present a comparison of the patterns of LD across four major human populations (African-American, Caucasian, Chinese, and Japanese) with a high-resolution single-nucleotide polymorphism (SNP) map covering almost the entire length of chromosomes 6, 21, and 22. We constructed metric LD maps formulated such that the units measure the extent of useful LD for association mapping. LD reaches almost twice as far in chromosome 6 as in chromosomes 21 or 22, in agreement with their differences in recombination rates. By all measures used, out-of-Africa populations showed over a third more LD than African-Americans, highlighting the role of the population's demography in shaping the patterns of LD. Despite those differences, the long-range contour of the LD maps is remarkably similar across the four populations, presumably reflecting common localization of recombination hot spots. Our results have practical implications for the rational design and selection of SNPs for disease association studies.