Navigating the HapMap

With the availability of the HapMap--a resource which describes common patterns of linkage disequilibrium (LD) in four different human population samples, we now have a powerful tool to help dissect the role of genetic variation in the biology of the genome. HapMap is entirely complimentary to the human genome map and so it is particularly fitting that it should be viewed in a full genomic context. However, characterization of high resolution LD across the genome can be a challenging task, owing in part to the sheer volume of data and the inherent dimensionality that its analysis entails. However, a number of tools are now available to make this task easier for researchers. This review will examine tools for viewing and analysing haplotype and LD data, enabling a number of tasks; including identification of optimal sets of haplotype tagging single nucleotide polymorphisms (SNPs); drawing links between associated SNPs and putative causal alleles; or simply viewing LD and haplotypes across a gene or region of interest. The data generated by the HapMap also has other important applications, informing, for example, on the demographic history and evidence of selection in human populations and on previously undetected regulatory relationships and gene networks. All of these properties make the HapMap no less an important resource than the human genome sequence itself and so this makes it essential viewing for all in the field of human biology.     

Using haplotype blocks to map human complex trait loci

Understanding of linkage disequilibrium (LD) in human populations could facilitate the discovery of genes that influence complex human diseases. The "HapMap" project is now underway to characterize patterns of LD in the human genome. A pilot study showed "haplotype blocks" in 51 regions scattered throughout the genome. These intriguing results raise important questions about the nature of recombination, and highlight practical issues of marker collection, the influence of statistical modelling on apparent block structure, and the levels of genotyping necessary for studies of common diseases. Knowledge of local disequilibrium patterns may help identify common polymorphisms involved in complex disease, but completely new analytical methods and experimental designs will be required to identify important rare variants.     

A method for detecting recent selection in the human genome from allele age estimates

Mutations that have recently increased in frequency by positive natural selection are an important component of naturally occurring variation that affects fitness. To identify such variants, we developed a method to test for recent selection by estimating the age of an allele from the extent of haplotype sharing at linked sites. Neutral coalescent simulations are then used to determine the likelihood of this age given the allele's observed frequency. We applied this method to a common disease allele, the hemochromatosis-associated HFE C282Y mutation. Our results allow us to reject neutral models incorporating plausible human demographic histories for HFE C282Y and one other young but common allele, indicating positive selection at HFE or a linked locus. This method will be useful for scanning the human genome for alleles under selection using the haplotype map now being constructed.     

Implications of inter-population linkage disequilibrium patterns on the approach to a disease association study in the human MHC class III

There is presently much interest in utilizing patterns of linkage disequilibrium (LD) to further genetic association studies. This is particularly pertinent in the class III region of the human major histocompatibility complex (MHC), which has been extensively studied as a disease susceptibility locus in a number of ethnic groups. To date, however, few studies of LD in the MHC have considered non-Caucasian populations. With the advent of large-scale haplotyping of the human genome, the question of utilizing LD patterns across populations has come to the fore. We have previously used LD mapping to direct an MHC class III association study in a UK Caucasian population. As an extension of this, we sought to determine to what extent the pattern of LD observed in that study could be used to conduct a similar study in a West African Gambian population. We found that broad patterns of LD were similar in the two populations, resulting in similar candidate region delineations, but at a higher resolution, marker-specific patterns of LD and population-dependent allele frequencies confounded the choice of regional tagging SNPs. Our results have implications for the applicability of large-scale haplotype maps such as the HapMap to complex regions like the MHC.Electronic Supplementary Material Supplementary material is available for this article at .     

Phenylalanine hydroxylase gene: novel missense mutation in exon 7 causing severe phenylketonuria.

By direct sequence analysis of 94 mutant phenylalanine hydroxylase alleles using polymerase chain reaction-based techniques, we identified a C to T transition in exon 7 of the human phenylalanine hydroxylase gene that is associated with RFLP haplotypes 1 and 4. A leucine for proline substitution at position 281 can be predicted from the nucleotide sequence of the mutant codon. Expression analysis in cultured mammalian cells after site-directed mutagenesis proved that the base substitution is a disease causing gene lesion. Dot-blot hybridization analysis using allele-specific oligonucleotides revealed that 25% of all mutant haplotype 1 alleles in the German population bear this mutation. In addition, this mutation could be detected on one mutant haplotype 4 allele. The fact that this mutation is associated with only 25% of all mutant haplotype 1 alleles suggests that multiple mutations may be associated with this haplotype. The occurrence of several different mutations would be in agreement with the clinical heterogeneity observed in the group of patients whose PKU alleles belong to haplotype 1.     

Single nucleotide polymorphisms (SNPs) that map to gaps in the human SNP map

An international effort is underway to generate a comprehensive haplotype map (HapMap) of the human genome represented by an estimated 300000 to 1 million ‘tag’ single nucleotide polymorphisms (SNPs). Our analysis indicates that the current human SNP map is not sufficiently dense to support the HapMap project. For example, 24.6% of the genome currently lacks SNPs at the minimal density and spacing that would be required to construct even a conservative tag SNP map containing 300 000 SNPs. In an effort to improve the human SNP map, we identified 140 696 additional SNP candidates using a new bioinformatics pipeline. Over 51 000 of these SNPs mapped to the largest gaps in the human SNP map, leading to significant improvements in these regions. Our SNPs will be immediately useful for the HapMap project, and will allow for the inclusion of many additional genomic intervals in the final HapMap. Nevertheless, our results also indicate that additional SNP discovery projects will be required both to define the haplotype architecture of the human genome and to construct comprehensive tag SNP maps that will be useful for genetic linkage studies in humans.     

Complex high-resolution linkage disequilibrium and haplotype patterns of single-nucleotide polymorphisms in 2.5 Mb of sequence on human chromosome 21.

One approach to identify potentially important segments of the human genome is to search for DNA regions with nonrandom patterns of human sequence variation. Previous studies have investigated these patterns primarily in and around candidate gene regions. Here, we determined patterns of DNA sequence variation in 2.5 Mb of finished sequence from five regions on human chromosome 21. By sequencing 13 individual chromosomes, we identified 1460 single-nucleotide polymorphisms (SNPs) and obtained unambiguous haplotypes for all chromosomes. For all five chromosomal regions, we observed segments with high linkage disequilibrium (LD), extending from 1.7 to>81 kb (average 21.7 kb), disrupted by segments of similar or larger size with no significant LD between SNPs. At least 25% of the contig sequences consisted of segments with high LD between SNPs. Each of these segments was characterized by a restricted number of observed haplotypes,with the major haplotype found in over 60% of all chromosomes. In contrast, the interspersed segments with low LD showed significantly more haplotype patterns. The position and extent of the segments of high LD with restricted haplotype variability did not coincide with the location of coding sequences. Our results indicate that LD and haplotype patterns need to be investigated with closely spaced SNPs throughout the human genome, independent of the location of coding sequences, to reliably identify regions with significant LD useful for disease association studies.     

Evolutionary genetics of the human Rh blood group system

The evolutionary history of variation in the human Rh blood group system, determined by variants in the RHD and RHCE genes, has long been an unresolved puzzle in human genetics. Prior to medical treatments and interventions developed in the last century, the D-positive (RhD positive) children of D-negative (RhD negative) women were at risk for hemolytic disease of the newborn, if the mother produced anti-D antibodies following sensitization to the blood of a previous D-positive child. Given the deleterious fitness consequences of this disease, the appreciable frequencies in European populations of the responsible RHD gene deletion variant (for example, 0.43 in our study) seem surprising. In this study, we used new molecular and genomic data generated from four HapMap population samples to test the idea that positive selection for an as-of-yet unknown fitness benefit of the RHD deletion may have offset the otherwise negative fitness effects of hemolytic disease of the newborn. We found no evidence that positive natural selection affected the frequency of the RHD deletion. Thus, the initial rise to intermediate frequency of the RHD deletion in European populations may simply be explained by genetic drift/founder effect, or by an older or more complex sweep that we are insufficiently powered to detect. However, our simulations recapitulate previous findings that selection on the RHD deletion is frequency dependent and weak or absent near 0.5. Therefore, once such a frequency was achieved, it could have been maintained by a relatively small amount of genetic drift. We unexpectedly observed evidence for positive selection on the C allele of RHCE in non-African populations (on chromosomes with intact copies of the RHD gene) in the form of an unusually high F( ST ) value and the high frequency of a single haplotype carrying the C allele. RhCE function is not well understood, but the C/c antigenic variant is clinically relevant and can result in hemolytic disease of the newborn, albeit much less commonly and severely than that related to the D-negative blood type. Therefore, the potential fitness benefits of the RHCE C allele are currently unknown but merit further exploration.     

Evaluation of the SNP tagging approach in an independent population sample—array-based SNP discovery in Sami

Significant efforts have been made to determine the correlation structure of common SNPs in the human genome. One method has been to identify the sets of tagSNPs that capture most of the genetic variation. Here, we evaluate the transferability of tagSNPs between populations using a population sample of Sami, the indigenous people of Scandinavia. Array-based SNP discovery in a 4.4 Mb region of 28 phased copies of chromosome 21 uncovered 5,132 segregating sites, 3,188 of which had a minimum minor allele frequency (mMAF) of 0.1. Due to the population structure and consequently high LD, the number of tagSNPs needed to capture all SNP variation in Sami is much lower than that for the HapMap populations. TagSNPs identified from the HapMap data perform only slightly better in the Sami than choosing tagSNPs at random from the same set of common SNPs. Surprisingly, tagSNPs defined from the HapMap data did not perform better than selecting the same number of SNPs at random from all SNPs discovered in Sami. Nearly half (46%) of the Sami SNPs with a mMAF of 0.1 are not present in the HapMap dataset. Among sites overlapping between Sami and HapMap populations, 18% are not tagged by the European American (CEU) HapMap tagSNPs, while 43% of the SNPs that are unique to Sami are not tagged by the CEU tagSNPs. These results point to serious limitations in the transferability of common tagSNPs to capture random sequence variation, even between closely related populations, such as CEU and Sami. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Crohn's disease risk alleles on the NOD2 locus have been maintained by natural selection on standing variation

Risk alleles for complex diseases are widely spread throughout human populations. However, little is known about the geographic distribution and frequencies of risk alleles, which may contribute to differences in disease susceptibility and prevalence among populations. Here, we focus on Crohn's disease (CD) as a model for the evolutionary study of complex disease alleles. Recent genome-wide association studies and classical linkage analyses have identified more than 70 susceptible genomic regions for CD in Europeans, but only a few have been confirmed in non-European populations. Our analysis of eight European-specific susceptibility genes using HapMap data shows that at the NOD2 locus the CD-risk alleles are linked with a haplotype specific to CEU at a frequency that is significantly higher compared with the entire genome. We subsequently examined nine global populations and found that the CD-risk alleles spread through hitchhiking with a high-frequency haplotype (H1) exclusive to Europeans. To examine the neutrality of NOD2, we performed phylogenetic network analyses, coalescent simulation, protein structural prediction, characterization of mutation patterns, and estimations of population growth and time to most recent common ancestor (TMRCA). We found that while H1 was significantly prevalent in European populations, the H1 TMRCA predated human migration out of Africa. H1 is likely to have undergone negative selection because 1) the root of H1 genealogy is defined by a preexisting amino acid substitution that causes serious conformational changes to the NOD2 protein, 2) the haplotype has almost become extinct in Africa, and 3) the haplotype has not been affected by the recent European expansion reflected in the other haplotypes. Nevertheless, H1 has survived in European populations, suggesting that the haplotype is advantageous to this group. We propose that several CD-risk alleles, which destabilize and disrupt the NOD2 protein, have been maintained by natural selection on standing variation because the deleterious haplotype of NOD2 is advantageous in diploid individuals due to heterozygote advantage and/or intergenic interactions.     

Sequencing and analysis of an Irish human genome

Background

Recent studies generating complete human sequences from Asian, African and European subgroups have revealed population-specific variation and disease susceptibility loci. Here, choosing a DNA sample from a population of interest due to its relative geographical isolation and genetic impact on further populations, we extend the above studies through the generation of 11-fold coverage of the first Irish human genome sequence.

Results

Using sequence data from a branch of the European ancestral tree as yet unsequenced, we identify variants that may be specific to this population. Through comparisons with HapMap and previous genetic association studies, we identified novel disease-associated variants, including a novel nonsense variant putatively associated with inflammatory bowel disease. We describe a novel method for improving SNP calling accuracy at low genome coverage using haplotype information. This analysis has implications for future re-sequencing studies and validates the imputation of Irish haplotypes using data from the current Human Genome Diversity Cell Line Panel (HGDP-CEPH). Finally, we identify gene duplication events as constituting significant targets of recent positive selection in the human lineage.

Conclusions

Our findings show that there remains utility in generating whole genome sequences to illustrate both general principles and reveal specific instances of human biology. With increasing access to low cost sequencing we would predict that even armed with the resources of a small research group a number of similar initiatives geared towards answering specific biological questions will emerge.     

The balance between heritable and environmental aetiology of human disease

The Human Genome Project and the ensuing International HapMap Project were largely motivated by human health issues. But the distance from a DNA sequence variation to a novel disease gene is considerable; for complex diseases, closing this gap hinges on the premise that they arise mainly from heritable causes. Using cancer as an example of complex disease, we examine the scientific evidence for the hypothesis that human diseases result from interactions between genetic variants and the environment.     

Genome-wide associations of gene expression variation in humans

The exploration of quantitative variation in human populations has become one of the major priorities for medical genetics. The successful identification of variants that contribute to complex traits is highly dependent on reliable assays and genetic maps. We have performed a genome-wide quantitative trait analysis of 630 genes in 60 unrelated Utah residents with ancestry from Northern and Western Europe using the publicly available phase I data of the International HapMap project. The genes are located in regions of the human genome with elevated functional annotation and disease interest including the ENCODE regions spanning 1% of the genome, Chromosome 21 and Chromosome 20q12–13.2. We apply three different methods of multiple test correction, including Bonferroni, false discovery rate, and permutations. For the 374 expressed genes, we find many regions with statistically significant association of single nucleotide polymorphisms (SNPs) with expression variation in lymphoblastoid cell lines after correcting for multiple tests. Based on our analyses, the signal proximal (cis-) to the genes of interest is more abundant and more stable than distal and trans across statistical methodologies. Our results suggest that regulatory polymorphism is widespread in the human genome and show that the 5-kb (phase I) HapMap has sufficient density to enable linkage disequilibrium mapping in humans. Such studies will significantly enhance our ability to annotate the non-coding part of the genome and interpret functional variation. In addition, we demonstrate that the HapMap cell lines themselves may serve as a useful resource for quantitative measurements at the cellular level.     

Adaptive evolution of UGT2B17 copy-number variation

The human UGT2B17 gene varies in copy number from zero to two per individual and also differs in mean number between populations from Africa, Europe, and East Asia. We show that such a high degree of geographical variation is unusual and investigate its evolutionary history. This required first reinterpreting the reference sequence in this region of the genome, which is misassembled from the two different alleles separated by an artifactual gap. A corrected assembly identifies the polymorphism as a 117 kb deletion arising by nonallelic homologous recombination between ~4.9 kb segmental duplications and allows the deletion breakpoint to be identified. We resequenced ~12 kb of DNA spanning the breakpoint in 91 humans from three HapMap and one extended HapMap populations and one chimpanzee. Diversity was unusually high and the time to the most recent common ancestor was estimated at ~2.4 or ~3.0 million years by two different methods, with evidence of balancing selection in Europe. In contrast, diversity was low in East Asia where a single haplotype predominated, suggesting positive selection for the deletion in this part of the world.     

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical “complete” chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated.     

The effect of single-nucleotide polymorphism marker selection on patterns of haplotype blocks and haplotype frequency estimates

The definition of haplotype blocks of single-nucleotide polymorphisms (SNPs) has been proposed so that the haplotypes can be used as markers in association studies and to efficiently describe human genetic variation. The International Haplotype Map (HapMap) project to construct a comprehensive catalog of haplotypic variation in humans is underway. However, a number of factors have already been shown to influence the definition of blocks, including the population studied and the sample SNP density. Here, we examine the effect that marker selection has on the definition of blocks and the pattern of haplotypes by using comparable but complementary SNP sets and a number of block definition methods in various genomic regions and populations that were provided by the Encyclopedia of DNA Elements (ENCODE) project. We find that the chosen SNP set has a profound effect on the block-covered sequence and block borders, even at high marker densities. Our results question the very concept of discrete haplotype blocks and the possibility of generalizing block findings from the HapMap project. We comparatively apply the block-free tagging-SNP approach and discuss both the haplotype approach and the tagging-SNP approach as means to efficiently catalog genetic variation.     

A flexible Bayesian framework for modeling haplotype association with disease, allowing for dominance effects of the underlying causative variants

Multilocus analysis of single-nucleotide-polymorphism (SNP) haplotypes may provide evidence of association with disease, even when the individual loci themselves do not. Haplotype-based methods are expected to outperform single-SNP analyses because (i) common genetic variation can be structured into haplotypes within blocks of strong linkage disequilibrium and (ii) the functional properties of a protein are determined by the linear sequence of amino acids corresponding to DNA variation on a haplotype. Here, I propose a flexible Bayesian framework for modeling haplotype association with disease in population-based studies of candidate genes or small candidate regions. I employ a Bayesian partition model to describe the correlation between marker-SNP haplotypes and causal variants at the underlying functional polymorphism(s). Under this model, haplotypes are clustered according to their similarity, in terms of marker-SNP allele matches, which is used as a proxy for recent shared ancestry. Haplotypes within a cluster are then assigned the same probability of carrying a causal variant at the functional polymorphism(s). In this way, I can account for the dominance effect of causal variants, here corresponding to any deviation from a multiplicative contribution to disease risk. The results of a detailed simulation study demonstrate that there is minimal cost associated with modeling these dominance effects, with substantial gains in power over haplotype-based methods that do not incorporate clustering and that assume a multiplicative model of disease risks.     

Molecular evolution and genetics of the Saitohin gene and tau haplotype in Alzheimer's disease and argyrophilic grain disease

A single nucleotide polymorphism that results in an amino acid change (Q7R) has been identified in the Saitohin (STH) gene and was initially found to be over-represented in the homozygous state in subjects with late-onset Alzheimer's disease (AD). More extensive studies provide limited support for the association with AD, but confirm an association of the Q allele with progressive supranuclear palsy and argyrophilic grain disease. A homologous sequence was found in the appropriate location of the rat and mouse tau genes, but there was no open reading frame allowing STH expression in these species, suggesting relatively recent evolution of this gene. In some non-human primates, the STH gene was identified, and this was found to differ from the human gene at two of 128 amino acids. All primates in which the STH gene was identified were homozygous for the R allele of STH, suggesting this is the ancestral allele. This observation was surprising, in that the Q allele is more common in human populations, and raises the possibility that natural selection has operated to favor individuals carrying this allele. The STH polymorphism is part of the tau gene haplotype, of which two major variants exist in human populations, the Q being part of the H1 haplotype and the R part of the H2 haplotype. More detailed studies confirm the H2 haplotype to be the ancestral tau gene. This situation is reminiscent of the evolution of the apolipoprotein (ApoE) gene, another locus that is potentially important for the risk of development of AD.     

Modeling genetic imprinting effects of DNA sequences with multilocus polymorphism data

Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA sequence variation in the human genome and they have recently emerged as valuable genetic markers for revealing the genetic architecture of complex traits in terms of nucleotide combination and sequence. Here, we extend an algorithmic model for the haplotype analysis of SNPs to estimate the effects of genetic imprinting expressed at the DNA sequence level. The model provides a general procedure for identifying the number and types of optimal DNA sequence variants that are expressed differently due to their parental origin. The model is used to analyze a genetic data set collected from a pain genetics project. We find that DNA haplotype GAC from three SNPs, OPRKG36T (with two alleles G and T), OPRKA843G (with alleles A and G), and OPRKC846T (with alleles C and T), at the kappa-opioid receptor, triggers a significant effect on pain sensitivity, but with expression significantly depending on the parent from which it is inherited (p = 0.008). With a tremendous advance in SNP identification and automated screening, the model founded on haplotype discovery and statistical inference may provide a useful tool for genetic analysis of any quantitative trait with complex inheritance.