Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium

Insulin Promoter Factor 1 variation is associated with type 2 diabetes in African Americans

Background

Defective insulin secretion is a key defect in the pathogenesis of type 2 diabetes (T2DM). The β-cell specific transcription factor, insulin promoter factor 1 gene (IPF1), is essential to pancreatic development and the maintenance of β-cell mass. We hypothesized that regulatory or coding variants in IPF1 contribute to defective insulin secretion and thus T2DM.

Methods

We screened 71 Caucasian and 69 African American individuals for genetic variants in the promoter region, three highly conserved upstream regulatory sequences (PH1, PH2 and PH3), the human β-cell specific enhancer, and the two exons with adjacent introns. We tested for an association of each variant with T2DM Caucasians (192 cases and 192 controls) and African Americans (341 cases and 186 controls).

Results

We identified 8 variants in the two populations, including a 3 bp insertion in exon 2 (InsCCG243) in African Americans that resulted in an in-frame proline insertion in the transactivation domain. No variant was associated with T2DM in Caucasians, but polymorphisms at -3766 in the human β-cell enhancer, at -2877 bp in the PH1 domain, and at -108 bp in the promoter region were associated with T2DM in African American subjects (p < 0.01), both individually and as haplotypes (p = 0.01 correcting by permutation test). No SNP altered a binding site for the expected β-cell transcription factors. The rare alleles of InsCCG243 in exon 2 showed a trend to over-representation among African American diabetic subjects (p < 0.1), but this trend was not significant on permutation test.

Conculsion

The common alleles of regulatory variants in the 5' enhancer and promoter regions of the IPF1 gene increase susceptibility to type 2 diabetes among African American individuals, likely as a result of gene-gene or gene-environment interactions. In contrast, IPF1 is not a cause of type 2 diabetes in Caucasians. A previously described InsCCG243 variant may contribute to diabetes susceptibility in African American individuals, but is of low penetrance.

What makes a good genetic association study?

Genetic association studies are central to efforts to identify and characterise genomic variants underlying susceptibility to multifactorial disease. However, obtaining robust replication of initial association findings has proved difficult. Much of this inconsistency can be attributed to inadequacies in study design, implementation, and interpretation--inadequately powered sample groups are a major concern. Several additional factors affect the quality of any given association study, with appropriate sample-recruitment strategy, logical variant selection, minimum genotyping error, relevant data analysis, and valid interpretation all essential to generation of robust findings. Replication has a vital role in showing that associations that are identified reflect interesting biological processes rather than methodological quirks. For an unbiased view of the evidence for and against any particular association, study quality, rather than significance value, needs to play the dominant part.

Multiplexed variation scanning for 1,000 amplicons in hundreds of patients using mismatch repair detection (MRD) on tag arrays

Identification of the genetic basis of common disease may require comprehensive sequence analysis of coding regions and regulatory elements in patients and controls to find genetic effects caused by rare or heterogeneous mutations. In this study, we demonstrate how mismatch repair detection on tag arrays can be applied in a case-control study. Mismatch repair detection allows >1,000 amplicons to be screened for variations in a single laboratory reaction. Variation scanning in 939 amplicons, mostly in coding regions within a linkage peak, was done for 372 patients and 404 controls. In total, >180 Mb of DNA was scanned. Several variants more prevalent in patients than in controls were identified. This study demonstrates an approach to the discovery of susceptibility genes for common disease: large-scale direct sequence comparison between patients and controls. We believe this approach can be scaled up to allow sequence comparison in the whole-genome coding regions among large sets of cases and controls at a reasonable cost in the near future.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

Much effort and expense are being spent internationally to detect genetic polymorphisms contributing to susceptibility to complex human disease. Concomitantly, the technology for detecting and genotyping single nucleotide polymorphisms (SNPs) has undergone rapid development, yielding extensive catalogues of these polymorphisms across the genome. Population-based maps of the correlations amongst SNPs (linkage disequilibrium) are now being developed to accelerate the discovery of genes for complex human diseases. These genomic advances coincide with an increasing recognition of the importance of very large sample sizes for studying genetic effects. Together, these new genetic and epidemiological data hold renewed promise for the identification of susceptibility genes for complex traits. We review the state of knowledge about the structure of the human genome as related to SNPs and linkage disequilibrium, discuss the potential applications of this knowledge to mapping complex disease genes, and consider the issues facing whole genome association scanning using SNPs.

The soluble epoxide hydrolase gene harbors sequence variation associated with susceptibility to and protection from incident ischemic stroke

Stroke is the leading cause of severe disability and the third leading cause of death, accounting for one of every 15 deaths in the USA. We investigated the association of polymorphisms in the soluble epoxide hydrolase gene (EPHX2) with incident ischemic stroke in African-Americans and Whites. Twelve single nucleotide polymorphisms (SNPs) spanning EPHX2 were genotyped in a case-cohort sample of 1336 participants from the Atherosclerosis Risk in Communities (ARIC) study. In each racial group, Cox proportional hazard models were constructed to assess the relationship between incident ischemic stroke and EPHX2 polymorphisms. A score test method was used to investigate the association of common haplotypes of the gene with risk of ischemic stroke. In African-Americans, two common EPHX2 haplotypes with significant and opposing relationships to ischemic stroke risk were identified. In Whites, two common haplotypes showed suggestive indication of an association with ischemic stroke risk but, as in African-Americans, these relationships were in opposite direction. These findings suggest that multiple variants exist within or near the EPHX2 gene, with greatly contrasting relationships to ischemic stroke incidence; some associated with a higher incidence and others with a lower incidence.

The linkage and association of the gene encoding upstream stimulatory factor 1 with type 2 diabetes and metabolic syndrome in the Chinese population

AIMS/HYPOTHESIS

The transcription factor upstream stimulatory factor 1 (USF1) regulates the expression of genes involved in glucose and lipid metabolism and has been associated with familial combined hyperlipidaemia. USF1 is located on chromosome 1q22-23, a region with evidence for linkage to type 2 diabetes and various traits of the metabolic syndrome in Chinese and other populations. The aim of this study was to investigate the linkage and association of USF1 with type 2 diabetes and the metabolic syndrome in Chinese individuals.

MATERIALS AND METHODS

We genotyped three haplotype-tagging single nucleotide polymorphisms (SNPs) (rs3737787, rs2516841 and rs2516839) at USF1 in three samples of the Hong Kong Chinese population, including members of 179 families from the Hong Kong Family Diabetes Study, 1,383 hospital cases with type 2 diabetes and/or the metabolic syndrome and 454 normal control subjects.

RESULTS

We found significant association of individual polymorphisms and haplotypes with type 2 diabetes and/or metabolic syndrome-related traits in the family samples using either family-based or unrelated normal control subjects. However, these variants could not explain much of the evidence for linkage in this region. Moreover, they were not associated with type 2 diabetes and/or the metabolic syndrome in the hospital cases.

CONCLUSIONS/INTERPRETATION

The results are consistent with the hypothesis that variation at USF1 contributes to the risk of type 2 diabetes and the metabolic syndrome in families with strong evidence for linkage in the chromosome 1q region. However, they provide little support for USF1 as the susceptibility locus that generates the observed evidence for linkage at 1q21-25 for type 2 diabetes and/or the metabolic syndrome, and USF1 does not appear to have a major contribution to these phenotypes in the general Chinese population.

Genetic association studies

We review the rationale behind and discuss methods of design and analysis of genetic association studies. There are similarities between genetic association studies and classic epidemiological studies of environmental risk factors but there are also issues that are specific to studies of genetic risk factors such as the use of particular family-based designs, the need to account for different underlying genetic mechanisms, and the effect of population history. Association differs from linkage (covered elsewhere in this series) in that the alleles of interest will be the same across the whole population. As with other types of genetic epidemiological study, issues of design, statistical analysis, and interpretation are very important.

SNPselector: a web tool for selecting SNPs for genetic association studies

SUMMARY

Single nucleotide polymorphisms (SNPs) are commonly used for association studies to find genes responsible for complex genetic diseases. With the recent advance of SNP technology, researchers are able to assay thousands of SNPs in a single experiment. But the process of manually choosing thousands of genotyping SNPs for tens or hundreds of genes is time consuming. We have developed a web-based program, SNPselector, to automate the process. SNPselector takes a list of gene names or a list of genomic regions as input and searches the Ensembl genes or genomic regions for available SNPs. It prioritizes these SNPs on their tagging for linkage disequilibrium, SNP allele frequencies and source, function, regulatory potential and repeat status. SNPselector outputs result in compressed Excel spreadsheet files for review by the user.

AVAILABILITY

SNPselector is freely available at http://primer.duhs.duke.edu/

CYP17 and breast cancer: no overall effect, but what about interactions?

Three large studies published in recent issues of Breast Cancer Research reported no overall evidence of an association between the CYP17 5'-untranslated region MspA1 polymorphism and breast cancer. The present commentary briefly highlights a few important observations and discusses some additional approaches to further assessment of associations between CYP17 common variants and breast cancer risk. In particular, the evolution of evidence on breast cancer and the CYP17 MspA1 variant suggests that determination of possible interactions between gene variants postulated to influence risk and nongenetic risk factors would be more efficiently accomplished by pooled analyses, ideally involving all studies of breast cancer, than by attempting to synthesize published information. Furthermore, such analyses would also be relevant to investigation of potential gene-gene interactions between CYP17 and other common variants in genes encoding enzymes that are involved in the synthesis and inactivation of sex steroid hormones, preferably using optimal sets of single nucleotide polymorphisms.

Genetic epidemiology of osteoporosis: past, present, and future

Research during the past several decades has unequivocally established a role of heredity in the etiology of osteoporosis. Major efforts are currently underway to identify the genes and allelic variants that confer genetic susceptibility to this common and disabling condition. Genome-wide linkage mapping in families, candidate gene association studies in unrelated individuals, and quantitative trait locus mapping in animal models are the primary strategies being used to search for the genetic contributors to osteoporosis. Genome-wide mapping efforts have identified the low-density lipoprotein receptor-related protein 5, bone morphogenetic protein 2, and 15-lipoxygenase as potential susceptibility genes for osteoporosis in the past few years, providing a rich new base for understanding bone biology. Candidate gene association analyses have also provided evidence for a modest role of allelic variants in several additional genes including collagen type Ialpha1, vitamin D receptor, and estrogen receptor-alpha. With the development of a high-density genome-wide polymorphism and haplotype map and continued improvements in high-throughput and cost-effective genotyping technologies, many more genetic contributors to osteoporosis will probably be identified in the near future. The results of this research should facilitate the development of new methods for diagnosing, preventing, and treating the growing clinical and public health problem of osteoporosis.

Recent developments in genomewide association scans: a workshop summary and review

With the imminent availability of ultra-high-volume genotyping platforms (on the order of 100,000-1,000,000 genotypes per sample) at a manageable cost, there is growing interest in the possibility of conducting genomewide association studies for a variety of diseases but, so far, little consensus on methods to design and analyze them. In April 2005, an international group of >100 investigators convened at the University of Southern California over the course of 2 days to compare notes on planned or ongoing studies and to debate alternative technologies, study designs, and statistical methods. This report summarizes these discussions in the context of the relevant literature. A broad consensus emerged that the time was now ripe for launching such studies, and several common themes were identified--most notably the considerable efficiency gains of multistage sampling design, specifically those made by testing only a portion of the subjects with a high-density genomewide technology, followed by testing additional subjects and/or additional SNPs at regions identified by this initial scan.

HAPLOT: a graphical comparison of haplotype blocks, tagSNP sets and SNP variation for multiple populations

Understanding of human variation relevant to association studies can benefit from population comparison, especially comparing populations in the same geographical region. Variations in linkage disequilibrium patterns, in tagSNP sets, and in SNP heterozygosities among populations can be used to infer the evolutionary pattern. We present here a win32 system based Perl/Tk application for visual comparisons of these variations in different populations.

AVAILABILITY

The application package is available at http://info.med.yale.edu/genetics/kkidd/programs.html

CONTACT

sheng.gu@yale.edu.

A comparison of tagging methods and their tagging space

Single-nucleotide polymorphism (SNP) tagging is widely used as a way of saving genotyping costs in association studies. A number of different tagging methods have been developed to reduce the number of markers to be genotyped while maintaining power for detecting effects on non-assayed SNPs. How the different methods perform in different settings, the degree to which they overlap and share common tags and how they differ are important questions. We investigated these questions by comparing three widely used tagging methods/algorithms--one haplotype r2-based method, one pair-wise r2-based method and one method which was based on haplotype diversity but focused on major haplotypes. Tagging efficiency was defined as the number of genotyped markers divided by the number of tagging SNPs. Tagging effectiveness was defined as the proportion of un-genotyped or 'hidden' SNPs being detected (having a pair-wise or haplotype r2 with a set of tagging SNPs over a threshold, e.g. haplotype r2> or =0.80). The ENCODE regions genotyped on the HapMap CEPH individuals were examined in this study. Tagging effectiveness was generally poor for rare SNPs than for common SNPs, for all three tagging methods. Inclusion of rare SNPs into initial HapMap scheme could enhance the performance of tags on rare hidden SNPs at the expense of increased genotyping cost. At a moderate tagging efficiency, more than 90% of hidden SNPs detected by tagging SNPs selected by one method were also detected by tagging SNPs selected by another method, and this figure could be increased to 100% if tagging efficiency was allowed to drop. These results indicate that the tagging space is highly concordant between different tagging methods, despite the fact that they often involve different sets of tagging SNPs.

Characterization of the linkage disequilibrium structure and identification of tagging-SNPs in five DNA repair genes

BACKGROUND

Characterization of the linkage disequilibrium (LD) structure of candidate genes is the basis for an effective association study of complex diseases such as cancer. In this study, we report the LD and haplotype architecture and tagging-single nucleotide polymorphisms (tSNPs) for five DNA repair genes: ATM, MRE11A, XRCC4, NBS1 and RAD50.

METHODS

The genes ATM, MRE11A, and XRCC4 were characterized using a panel of 94 unrelated female subjects (47 breast cancer cases, 47 controls) obtained from high-risk breast cancer families. A similar LD structure and tSNP analysis was performed for NBS1 and RAD50, using publicly available genotyping data. We studied a total of 61 SNPs at an average marker density of 10 kb. Using a matrix decomposition algorithm, based on principal component analysis, we captured >90% of the intragenetic variation for each gene.

RESULTS

Our results revealed that three of the five genes did not conform to a haplotype block structure (MRE11A, RAD50 and XRCC4). Instead, the data fit a more flexible LD group paradigm, where SNPs in high LD are not required to be contiguous. Traditional haplotype blocks assume recombination is the only dynamic at work. For ATM, MRE11A and XRCC4 we repeated the analysis in cases and controls separately to determine whether LD structure was consistent across breast cancer cases and controls. No substantial difference in LD structures was found.

CONCLUSION

This study suggests that appropriate SNP selection for an association study involving candidate genes should allow for both mutation and recombination, which shape the population-level genomic structure. Furthermore, LD structure characterization in either breast cancer cases or controls appears to be sufficient for future cancer studies utilizing these genes.

Association study of CREB1 and childhood-onset mood disorders

Several lines of evidence suggest that the cellular pathways involved in synaptic plasticity contribute to the risk of depression. These findings include the evidence that chronic antidepressant treatment upregulates the cAMP signal transduction cascade resulting in increased expression and function of the cAMP responsive element binding protein (CREB), a transcription factor that increases the expression of key growth factors involved in synaptogenesis and neurogenesis. Recently, linkage to CREB1 was reported for early-onset depression in families recruited from the Pittsburgh area. This finding was significant only in female sibling pairs from those families. Two specific DNA variants, -656G/A and a C insertion/deletion in intron 8, were identified in CREB1 that co-segregated with depression in two of the families. We sought to investigate the relationship of CREB1 to childhood-onset mood disorders (COMD) using a sample of 195 nuclear families (225 affected children) collected in Hungary. We genotyped the two CREB1 DNA variants previously identified as linked to depression as well as three additional polymorphisms spanning the gene. In addition, we genotyped the -656G/A DNA change and the intron 8 polymorphism in a sample of 112 probands with mood disorders collected in the Pittsburgh area and matched controls, and examined the distribution of alleles. The -656A allele was not observed in our samples and there was no evidence for association of the intron 8 polymorphism in either the sample from Pittsburgh (chi(2) = 0.061, 1 d.f., P = 0.803) or Hungary (chi(2) = 0.040, 1 d.f., P = 0.842). We found no evidence for an association with the other three polymorphisms or with the haplotypes of these markers. Further, we found no sex-specific relationship. Our results, therefore, do not support the previous evidence for this gene as a major factor contributing to depression.

Genetic tests of biologic systems in affective disorders

To liberate candidate gene analyses from criticisms of inexhaustiveness of examination of specific candidate genes, or incompleteness in the choice of candidate genes to study for specific neurobiological pathways, study of sizeable sets of genes pertinent to each putative pathophysiological pathway is required. For many years, genes have been tested in a 'one by one' manner for association with major affective disorders, primarily bipolar illness. However, it is conceivable that not individual genes but abnormalities in several genes within a system or in several neuronal, neural, or hormonal systems are implicated in the functional hypotheses for etiology of affective disorders. Compilation of candidate genes for entire pathways is a challenge, but can reasonably be carried out for the major affective disorders as discussed here. We present here five groupings of genes implicated by neuropharmacological and other evidence, which suggest 252 candidate genes worth examining. Inexhaustiveness of gene interrogation would apply to many studies in which only one polymorphism per gene is analyzed. In contrast to whole-genome association studies, a study of a limited number of candidate genes can readily exploit information on genomic sequence variations obtained from databases and/or resequencing, and has an advantage of not having the complication of an extremely stringent statistical criterion for association.

Identification of risk and age-at-onset genes on chromosome 1p in Parkinson disease

We previously reported a linkage region on chromosome 1p (LOD = 3.41) for genes controlling age at onset (AAO) in Parkinson disease (PD). This region overlaps with the previously reported PARK10 locus. To identify the gene(s) associated with AAO and risk of PD in this region, we first applied a genomic convergence approach that combined gene expression and linkage data. No significant results were found. Second, we performed association mapping across a 19.2-Mb region centered under the AAO linkage peak. An iterative association mapping approach was done by initially genotyping single-nucleotide polymorphisms at an average distance of 100 kb apart and then by increasing the density of markers as needed. Using the overall data set of 267 multiplex families, we identified six associated genes in the region, but further screening of a subset of 83 families linked to the chromosome 1 locus identified only two genes significantly associated with AAO in PD: the gamma subunit of the translation initiation factor EIF2B gene (EIF2B3), which was more significant in the linked subset and the ubiquitin-specific protease 24 gene (USP24). Unexpectedly, the human immunodeficiency virus enhancer-binding protein 3 gene (HIVEP3) was found to be associated with risk for susceptibility to PD. We used several criteria to define significant results in the presence of multiple testing, including criteria derived from a novel cluster approach. The known or putative functions of these genes fit well with the current suspected pathogenic mechanisms of PD and thus show great potential as candidates for the PARK10 locus.

Association of common variation in the HNF1alpha gene region with risk of type 2 diabetes

It is currently unclear how often genes that are mutated to cause rare, early-onset monogenic forms of disease also harbor common variants that contribute to the more typical polygenic form of each disease. The gene for MODY3 diabetes, HNF1alpha, lies in a region that has shown linkage to late-onset type 2 diabetes (12q24, NIDDM2), and previous association studies have suggested a weak trend toward association for common missense variants in HNF1alpha with glucose-related traits. Based on genotyping of 79 common SNPs in the 118 kb spanning HNF1alpha, we selected 21 haplotype tag single nucleotide polymorphisms (SNPs) and genotyped them in >4,000 diabetic patients and control subjects from Sweden, Finland, and Canada. Several SNPs from the coding region and 5' of the gene demonstrated nominal association with type 2 diabetes, with the most significant marker (rs1920792) having an odds ratio of 1.17 and a P value of 0.002. We then genotyped three SNPs with the strongest evidence for association to type 2 diabetes (rs1920792, I27L, and A98V) in an additional 4,400 type 2 diabetic and control subjects from North America and Poland and compared our results with those of the original sample and of Weedon et al. None of the results were consistently observed across all samples, with the possible exception of a modest association of the rare (3-5%) A98V variant. These results indicate that common variants in HNF1alpha either play no role in type 2 diabetes, a very small role, or a role that cannot be consistently observed without consideration of as yet unmeasured genetic or environmental modifiers.

Definition and clinical importance of haplotypes

Advances in genotyping and sequencing technologies, coupled with the development of sophisticated statistical methods, have afforded investigators novel opportunities to define the role of sequence variation in the development of common human diseases. At the forefront of these investigations is the use of dense maps of single-nucleotide polymorphisms (SNPs) and the haplotypes derived from these polymorphisms. Here we review basic concepts of high-density genetic maps of SNPs and haplotypes and how they are typically generated and used in human genetic research. We also provide useful examples and tools available for researchers interested in incorporating haplotypes into their studies. Finally, we discuss the latest concepts for the analysis of haplotypes related to human disease, including haplotype blocks, the International HapMap Project, and the future directions of these resources.

Association of endothelial lipase gene (LIPG) haplotypes with high-density lipoprotein cholesterol subfractions and apolipoprotein AI plasma levels in Japanese Americans

The LIPG gene on chromosome 18 encodes for endothelial lipase, a member of the triglyceride lipase family. Mouse models suggest that variation in LIPG influences high-density lipoprotein (HDL) metabolism, but only limited data are available in humans. This study examined associations of LIPG haplotypes, comprising a single nucleotide polymorphism (SNP) in the promoter region (-384A>C), and a nonsynonymous SNP in exon 3 (Thr111Ile or 584C>T), with lipoprotein risk factors in 541 adult Japanese Americans. A marginal association was found between LIPG diplotypes and HDL cholesterol (p=0.045). Stronger associations were seen for HDL3 cholesterol (p=0.005) and Apolipoprotein AI plasma levels (p=0.002). After adjustment for age, gender, smoking and medications, individuals homozygous for the minor allele at both SNPs (*4 haplotype) had a more favorable risk factor profile, compared to other haplotype combinations. No relationship was seen for plasma triglyceride levels or low-density lipoprotein (LDL) size, but the homozygous *4 diplotype was also associated with lower Apolipoprotein B and LDL cholesterol levels (p=0.001 and 0.015, respectively). In conclusion, this community-based family study of Japanese Americans demonstrates that LIPG variants are associated with HDL related risk factors, and may play a role in susceptibility to cardiovascular disease in this population.

Allelic association of the human homologue of the mouse modifier Ptprj with breast cancer

Human homologues of mouse cancer modifier genes may play a role in cancer risk and prognosis. A proportion of the familial risk of common cancers may be attributable to variants in such genes, each contributing to a small effect. The protein tyrosine phosphatase receptor type J (PTPRJ) has been recently identified as being the protein encoded by the Scc1 mouse gene (susceptibility to colon cancer-1). In addition, the PTPRJ gene has been shown to be somatically altered in several human cancer types such as colon, lung and breast cancers and to have the characteristics of a tumour-suppressor gene. The purpose of this study was to determine whether common variants in the PTPRJ gene represent low penetrance breast cancer susceptibility alleles. To test this hypothesis, we assessed single nucleotide polymorphisms (SNPs) tagging the common SNPs and haplotypes of the gene in 4512 cases and 4554 controls from the East Anglian population. We observed a difference in the haplotype frequency distributions between cases and controls (P = 0.0023, OR = 0.81 [0.72-0.92]). Thus, carrying a specific PTPRJ haplotype confers a protective effect on the risk of breast cancer. This result establishes the principle that mouse cancer modifier genes are candidates for low penetrance human breast cancer susceptibility genes.

The association of common SNPs and haplotypes in the CETP and MDR1 genes with lipids response to fluvastatin in familial hypercholesterolemia

OBJECTIVE

To examine whether genetic polymorphisms in the cholesteryl-ester transfer protein (CETP) and the P-glycoprotein drug transporter (MDR1), are associated with variable lipid response to fluvastatin.

METHODS

Lipid levels were determined in a compliance-monitored clinical study at baseline and following 20 weeks of treatment with 40 mg dose of fluvastatin in 76 FH patients. CETP and MDR1 SNP genotyping was performed and linear regression was used to examine the associations between common SNPs and haplotypes and lipid response.

RESULTS

Treatment with 40 mg of fluvastatin resulted in mean low density lipoprotein cholesterol (LDL-C) reduction of 21.5%; mean triglyceride (TG) reduction of 8.3%; and a mean high-density lipoprotein cholesterol (HDL-C) increase of 13.4%. Five tagging SNPs in both genes were used to reconstruct five and six haplotypes accounting for 71.4% and 90.2% of the observed haplotypes in the CETP and MDR1 genes, respectively. CETP-H13 and MDR1-h4 were associated with an increase in LDL-C response. CETP-H5 was significantly associated with decreased TG and HDL-C response, whereas MDR1-h10 was associated with decreased TG response. A multivariate regression model indicated an independent additive effect of CETP-H5 and MDR1-h10 on the level of TG response.

CONCLUSIONS

CETP and MDR1 have independent effects on lipid changes following fluvastatin treatment. The results of this study may lead to an improved understanding of the genetic determinants of lipids response to treatment.

Disentangling fetal and maternal susceptibility for pre-eclampsia: a British multicenter candidate-gene study

The Genetics of Pre-Eclampsia (GOPEC) collaboration aims to identify genetic factors in U.K. families affected by pre-eclampsia. A number of genetic studies have reported associations with pre-eclampsia, but attempts to replicate these findings have yielded inconsistent results. We describe the results of extensive genotyping of seven candidate genes previously reported as conferring susceptibility to pre-eclampsia. Six hundred fifty-seven women affected by pre-eclampsia and their families were genotyped at 28 single-nucleotide polymorphisms in the genes encoding angiotensinogen, the angiotensin receptors, factor V Leiden variant, methylene tetrahydrofolate reductase, nitric oxide synthase, and TNFalpha. Genotypes were analyzed by the transmission/disequilibrium test. Genotype risk ratios (GRRs) associated with maternal genotypes had a range of 0.70-1.16; GRRs associated with fetal genotypes had a range of 0.72-1.11. No GRR achieved the prespecified criteria for statistical significance (posterior probability >.05). We conclude that none of the genetic variants tested in this large study of strictly defined pre-eclamptic pregnancies confers a high risk of disease. The results emphasize the importance of conducting rigorously designed studies of adequate size to provide precise genetic risks with narrow confidence intervals, if overreporting of false-positive results is to be avoided.

Techniques for the identification of genes involved in psychiatric disorders

OBJECTIVE

Most psychiatric disorders are complex genetic traits involving both genetic and environmental risk factors. This paper aims to review the gene identification strategies being applied by molecular geneticists in their efforts to elucidate the genetic and molecular basis of psychiatric disorders. Future strategies will also be canvassed.

METHOD

The psychiatric genetic literature was reviewed to identify current strategies applied to gene identification, with examples provided where available. The future strategies and applications that will arise from genome projects, including the International Haplotype Mapping Project, are also discussed.

RESULTS

Many advances in the techniques of gene discovery, and the increasing resources available, are rapidly being adopted by researchers and applied to the complex problem of identifying susceptibility genes for mental illnesses. Perhaps the single most important advance to date is the Human Genome Project and all that has stemmed from the vast quantity of information that this endeavour has provided. With these technological advances and the massive increase of publicly available genetic resources, several genes have recently been implicated in the susceptibility to psychiatric illnesses including schizophrenia and depression. After many years of fruitless endeavours, these recent reports indicate that the labours of researchers in psychiatric genetics are beginning to show exciting results.

CONCLUSIONS

Identification of these susceptibility genes holds great promise, with the unravelling of the molecular and biochemical basis of some conditions now being a more realistic and tangible goal. The increasing number of genes being identified augers well for the future treatment of psychiatric disorders. The genes identified, and the pathways of genes and proteins that they implicate, will provide potential novel targets for new therapeutic drugs. Psychiatric genetics appears to be poised for significant advances in our knowledge and understanding of the molecular genetic basis of mental illness.

Polymorphisms within the C-reactive protein (CRP) promoter region are associated with plasma CRP levels

Elevated plasma levels of C-reactive protein (CRP), an inflammation-sensitive marker, have emerged as an important predictor of future cardiovascular disease and metabolic abnormalities in apparently healthy men and women. Here, we performed a systematic survey of common nucleotide variation across the genomic region encompassing the CRP gene locus. Of the common single-nucleotide polymorphisms (SNPs) identified, several in the CRP promoter region are strongly associated with CRP levels in a large cohort study of cardiovascular risk in European American and African American young adults. We also demonstrate the functional importance of these SNPs in vitro.

Characterization of multilocus linkage disequilibrium

Linkage disequilibrium (LD) in the human genome, often measured as pairwise correlation between adjacent markers, shows substantial spatial heterogeneity. Congruent with these results, studies have found that certain regions of the genome have far less haplotype diversity than expected if the alleles at multiple markers were independent, while other sets of adjacent markers behave almost independently. Regions with limited haplotype diversity have been described as "blocked" or "haplotype blocks." In this article, we propose a new method that aims to distinguish between blocked and unblocked regions in the genome. Like some other approaches, the method analyses haplotype diversity. Unlike other methods, it allows for adjacent, distinct blocks and also multiple, independent single nucleotide polymorphisms (SNPs) separating blocks. Based on an approximate likelihood model and a parsimony criterion to penalize for model complexity, the method partitions a genomic region into blocks relatively quickly, and simulations suggest that its partitions are accurate. We also propose a new, efficient method to select SNPs for association analysis, namely tag SNPs. These methods compare favorably to similar blocking and tagging methods using simulations.

Polymorphisms in signal transducer and activator of transcription 3 and lung function in asthma

Background

Identifying genetic determinants for lung function is important in providing insight into the pathophysiology of asthma. Signal transducer and activator of transcription 3 is a transcription factor latent in the cytoplasm; the gene (STAT3) is activated by a wide range of cytokines, and may play a role in lung development and asthma pathogenesis.

Methods

We genotyped six single nucleotide polymorphisms (SNPs) in the STAT3 gene in a cohort of 401 Caucasian adult asthmatics. The associations between each SNP and forced expiratory volume in 1 second (FEV₁), as a percent of predicted, at the baseline exam were tested using multiple linear regression models. Longitudinal analyses involving repeated measures of FEV₁ were conducted with mixed linear models. Haplotype analyses were conducted using imputed haplotypes. We completed a second association study by genotyping the same six polymorphisms in a cohort of 652 Caucasian children with asthma.

Results

We found that three polymorphisms were significantly associated with baseline FEV₁: homozygotes for the minor alleles of each polymorphism had lower FEV₁ than homozygotes for the major alleles. Moreover, these associations persisted when we performed an analysis on repeated measures of FEV₁ over 8 weeks. A haplotypic analysis based on the six polymorphisms indicated that two haplotypes were associated with baseline FEV₁. Among the childhood asthmatics, one polymorphism was associated with both baseline FEV₁ and the repeated measures of FEV₁ over 4 years.

Conclusion

Our results indicate that genetic variants in STAT3, independent of asthma treatment, are determinants of FEV₁ in both adults and children with asthma, and suggest that STAT3 may participate in inflammatory pathways that have an impact on level of lung function.

Developmentally restricted genetic determinants of human arsenic metabolism: association between urinary methylated arsenic and CYT19 polymorphisms in children

We report the results of a screen for genetic association with urinary arsenic metabolite levels in three arsenic metabolism candidate genes, PNP, GSTO, and CYT19, in 135 arsenic-exposed subjects from the Yaqui Valley in Sonora, Mexico, who were exposed to drinking water concentrations ranging from 5.5 to 43.3 ppb. We chose 23 polymorphic sites to test in the arsenic-exposed population. Initial phenotypes evaluated included the ratio of urinary inorganic arsenic(III) to inorganic arsenic(V) and the ratio of urinary dimethylarsenic(V) to monomethylarsenic(V) (D:M). In the initial association screening, three polymorphic sites in the CYT19 gene were significantly associated with D:M ratios in the total population. Subsequent analysis of this association revealed that the association signal for the entire population was actually caused by an extremely strong association in only the children (7-11 years of age) between CYT19 genotype and D:M levels. With children removed from the analysis, no significant genetic association was observed in adults (18-79 years). The existence of a strong, developmentally regulated genetic association between CYT19 and arsenic metabolism carries import for both arsenic pharmacogenetics and arsenic toxicology, as well as for public health and governmental regulatory officials.

Principles of haplotype mapping and potential applications to attention-deficit/hyperactivity disorder

Approaches to the study of common, complex genetic disorders like attention-deficit/hyperactivity disorder (ADHD) are evolving rapidly. Traditional linkage and association mapping each have distinct roles to play. Rapid advances in genomic information and technologies make association studies more attractive, including the possibility in the near future of whole genome association scans. This review covers the following broad topics: 1) the principles of linkage and association analyses as they apply to ADHD, and 2) the implications of genome architecture for association studies of complex diseases like ADHD. The structure of linkage disequilibrium is approached through review of the statistical measures of allelic associations and their relationship to observed haplotypes. The patterns of haplotypes across the human genome are discussed, as well as the implications of linkage disequilibrium mapping for association studies in general and ADHD specifically. Finally, the extent to which the allelic architecture of a candidate ADHD gene is publicly available and the web resources to access this information are covered. Today, the wealth of polymorphism data available on the worldwide web enables researchers to focus powerful methodologic tools on candidate genes and regions of interest. Coupling this with larger patient collections and more refined phenotyping will move forward the identification of disease-associated polymorphisms and ultimately the development of genetically based pharmaceuticals and diagnostic tests.

Analysis of single nucleotide polymorphisms in the promoter region of interleukin-10 by denaturing high-performance liquid chromatography

Interleukin-10 (IL10), an anti-inflammatory cytokine, has been implicated in a variety of immune- and inflammatory-related diseases. We investigated the following SNPs: -1082, -819, -592 in the promoter region of IL10 in a normal (control) population and selected diseases: breast cancer (BrCa), systemic lupus erythematosus (SLE), and B-cell chronic lymphocytic leukemia (B-CLL) by denaturing high-performance liquid chromatography (DHPLC) and found distinct genotype and haplotype patterns. DHPLC was performed using the Transgenomic WAVE instrument, a mutational discovery tool that allows for high throughout analysis of SNPs. The principle of DHPLC is based on separation of homo- and heteroduplex formation of individual polymerase chain reaction products at specific melting temperatures and set gradients. The melting temperature selected for each SNP was based on size and sequence of the polymerase chain reaction product (for -1082, 57 degrees C; for -819, 58 degrees C; and for -592, 59.2 degrees C). Before fragment mutational analysis, all samples were denatured at 95 degrees C and slowly reannealed to allow for reassociation of different strands. Heteroduplex samples were easily distinguished from homoduplex samples. In order to identify wild type from homozygous mutant, two homoduplex polymerase chain reaction samples had to be mixed together, denatured at 95 degrees C and reannealed. The homozygous mutant, when combined with wild type, displayed a double peak on chromatogram. Once distinct chromatograms were established for each of the SNPs and the nucleotide changes confirmed by sequencing, genotype and haplotype frequencies were tabulated for the groups studied.

Haplotype structure and phylogenetic shadowing of a hypervariable region in the CAPN10 gene

It has been proposed that variation in calpain 10 (CAPN10) contributes to the risk of type 2 diabetes (T2D). A previous survey of CAPN10 in ethnically diverse populations revealed an intronic region with a significant excess of polymorphism levels relative to inter-species sequence divergence, suggesting that this region was the target of long-standing balancing selection. Based on the thrifty genotype hypothesis, variation that increases risk to T2D in contemporary humans at one time conferred a survival advantage in ancestral populations. Thus, the signature of positive natural selection in a T2D candidate gene could identify a genomic region containing variation that influences disease susceptibility. Here, we investigate this hypothesis by re-sequencing the CAPN10 region with unusual polymorphism levels in T2D cases and controls (n=91) from a Mexican American (MA) population, and by using networks to infer the evolutionary relationships between the major haplotypes. Haplotype tag SNPs (htSNPs) were then selected in each population sample and in MA cases and controls. By placing the htSNPs on the haplotype network, we investigate how cross-population differences in CAPN10 genetic architecture may affect the detection of the disease association. Interestingly, despite the small scale of our case-control study, we observe a nearly significant signal of association between T2D and variation in the putative target of balancing selection. Finally, we use phylogenetic shadowing across 10 primate species to search for conserved non-coding elements that may affect the expression and function of CAPN10. These elements are postulated to be the targets of long-standing balancing selection.

Emerging strategies and applications of pharmacogenomics

The rapid pace of genomic science advancements, including the completion of the human genome sequence, the extensive cataloguing of genetic variation and the acceleration of technologies to assess such variation, combined with clinical programmes with rich phenotypic data, serve as the foundation for the design and execution of pharmacogenomic studies which have an impact on the pharmaceutical pipeline from early discovery through to the marketplace. The authors discuss the required infrastructure to support pharmacogenomic studies and provide insight into the strategies and practical application to influence decision making in the pharmaceutical setting. Further, the influence of pharmacogenomics is currently affecting patient care in the oncology area and is highlighted as evident impact in the marketplace.

Linkage disequilibrium: ancient history drives the new genetics

This brief review provides a summary of the biological causes of genetic association between tightly linked markers--termed linkage disequilibrium--and unlinked markers--termed population structure. We also review the utility of linkage disequilibrium data in gene mapping in isolated populations, in the estimation of recombination rates and in studying the history of particular alleles, including the detection of natural selection. We discuss current understanding of the extent and patterns of linkage disequilibrium in the genome, and its promise for genetic association studies in complex disease. Finally, we highlight the importance of using appropriate statistical procedures, such as the false discovery rate, to maximize the chances of success in large scale association studies.

A high-resolution linkage-disequilibrium map of the human major histocompatibility complex and first generation of tag single-nucleotide polymorphisms

Autoimmune, inflammatory, and infectious diseases present a major burden to human health and are frequently associated with loci in the human major histocompatibility complex (MHC). Here, we report a high-resolution (1.9 kb) linkage-disequilibrium (LD) map of a 4.46-Mb fragment containing the MHC in U.S. pedigrees with northern and western European ancestry collected by the Centre d'Etude du Polymorphisme Humain (CEPH) and the first generation of haplotype tag single-nucleotide polymorphisms (tagSNPs) that provide up to a fivefold increase in genotyping efficiency for all future MHC-linked disease-association studies. The data confirm previously identified recombination hotspots in the class II region and allow the prediction of numerous novel hotspots in the class I and class III regions. The region of longest LD maps outside the classic MHC to the extended class I region spanning the MHC-linked olfactory-receptor gene cluster. The extended haplotype homozygosity analysis for recent positive selection shows that all 14 outlying haplotype variants map to a single extended haplotype, which most commonly bears HLA-DRB1*1501. The SNP data, haplotype blocks, and tagSNPs analysis reported here have been entered into a multidimensional Web-based database (GLOVAR), where they can be accessed and viewed in the context of relevant genome annotation. This LD map allowed us to give coordinates for the extremely variable LD structure underlying the MHC.

Genome-wide association studies for common diseases and complex traits

Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.

Genome-wide association studies: theoretical and practical concerns

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

Optimal Selection of SNP Markers for Disease Association Studies

Genetic association studies with population samples hold the promise of uncovering the susceptibility genes underlying the heritability of complex or common disease. Most association studies rely on the use of surrogate markers, single-nucleotide polymorphism (SNP) being the most suitable due to their abundance and ease of scoring. SNP marker selection is aimed to increase the chances that at least one typed SNP would be in linkage disequilibrium (LD) with the disease causative variant, while at the same time controlling the cost of the study in terms of the number of markers genotyped and samples. Empirical studies reporting block-like segments in the genome with high LD and low haplotype diversity have motivated a marker selection strategy whereby subsets of SNPs that 'tag' the common haplotypes of a region are picked for genotyping, avoiding typing redundant SNPs. Based on these initial observations, a plethora of 'tagging' algorithms for selecting minimum informative subsets of SNPs has recently appeared in the literature. These differ mostly in two major aspects: the quality or correlation measure used to define tagging and the algorithm used for the minimization of the final number of tagging SNPs. In this review we describe the available tagging algorithms utilizing a 3-step unifying framework, point out their methodological and conceptual differences, and make an assessment of their assumptions, performance, and scalability.

Algorithms for inferring haplotypes

Haplotype phase information in diploid organisms provides valuable information on human evolutionary history and may lead to the development of more efficient strategies to identify genetic variants that increase susceptibility to human diseases. Molecular haplotyping methods are labor-intensive, low-throughput, and very costly. Therefore, algorithms based on formal statistical theories were shown to be very effective and cost-efficient for haplotype reconstruction. This review covers 1) population-based haplotype inference methods: Clark's algorithm, expectation-maximization (EM) algorithm, coalescence-based algorithms (pseudo-Gibbs sampler and perfect/imperfect phylogeny), and partition-ligation algorithm implemented by a fully Bayesian model (Haplotyper) or by EM (PLEM); 2) family-based haplotype inference methods; 3) the handling of genotype scoring uncertainties (i.e., genotyping errors and raw two-dimensional genotype scatterplots) in inferring haplotypes; and 4) haplotype inference methods for pooled DNA samples. The advantages and limitations of each algorithm are discussed. By using simulations based on empirical data on the G6PD gene and TNFRSF5 gene, I demonstrate that different algorithms have different degrees of sensitivity to various extents of population diversities and genotyping error rates. Future development of statistical algorithms for addressing haplotype reconstruction will resort more and more to ideas based on combinatorial mathematics, graphical models, and machine learning, and they will have profound impacts on population genetics and genetic epidemiology with the advent of the human HapMap.

Whole-genome patterns of common DNA variation in three human populations

Individual differences in DNA sequence are the genetic basis of human variability. We have characterized whole-genome patterns of common human DNA variation by genotyping 1,586,383 single-nucleotide polymorphisms (SNPs) in 71 Americans of European, African, and Asian ancestry. Our results indicate that these SNPs capture most common genetic variation as a result of linkage disequilibrium, the correlation among common SNP alleles. We observe a strong correlation between extended regions of linkage disequilibrium and functional genomic elements. Our data provide a tool for exploring many questions that remain regarding the causal role of common human DNA variation in complex human traits and for investigating the nature of genetic variation within and between human populations.

Linkage disequilibrium patterns and tagSNP transferability among European populations

The pattern of linkage disequilibrium (LD) is critical for association studies, in which disease-causing variants are identified by allelic association with adjacent markers. The aim of this study is to compare the LD patterns in several distinct European populations. We analyzed four genomic regions (in total, 749 kb) containing candidate genes for complex traits. Individuals were genotyped for markers that are evenly distributed at an average spacing of approximately 2-4 kb in eight population-based samples from ongoing epidemiological studies across Europe. The Centre d'Etude du Polymorphisme Humain (CEPH) trios of the HapMap project were included and were used as a reference population. In general, we observed a conservation of the LD patterns across European samples. Nevertheless, shifts in the positions of the boundaries of high-LD regions can be demonstrated between populations, when assessed by a novel procedure based on bootstrapping. Transferability of LD information among populations was also tested. In two of the analyzed gene regions, sets of tagging single-nucleotide polymorphisms (tagSNPs) selected from the HapMap CEPH trios performed surprisingly well in all local European samples. However, significant variation in the other two gene regions predicts a restricted applicability of CEPH-derived tagging markers. Simulations based on our data set show the extent to which further gain in tagSNP efficiency and transferability can be achieved by increased SNP density.

Linkage disequilibrium grouping of single nucleotide polymorphisms (SNPs) reflecting haplotype phylogeny for efficient selection of tag SNPs

Single nucleotide polymorphisms (SNPs) have been proposed to be grouped into haplotype blocks harboring a limited number of haplotypes. Within each block, the portion of haplotypes is expected to be tagged by a selected subset of SNPs; however, none of the proposed selection algorithms have been definitive. To address this issue, we developed a tag SNP selection algorithm based on grouping of SNPs by the linkage disequilibrium (LD) coefficient r(2) and examined five genes in three ethnic populations--the Japanese, African Americans, and Caucasians. Additionally, we investigated ethnic diversity by characterizing 979 SNPs distributed throughout the genome. Our algorithm could spare 60% of SNPs required for genotyping and limit the imprecision in allele-frequency estimation of nontag SNPs to 2% on average. We discovered the presence of a mosaic pattern of LD plots within a conventionally inferred haplotype block. This emerged because multiple groups of SNPs with strong intragroup LD were mingled in their physical positions. The pattern of LD plots showed some similarity, but the details of tag SNPs were not entirely concordant among three populations. Consequently, our algorithm utilizing LD grouping allows selection of a more faithful set of tag SNPs than do previous algorithms utilizing haplotype blocks.

Molecular screening and association analyses of the interleukin 6 receptor gene variants with type 2 diabetes, diabetic nephropathy, and insulin sensitivity

IL-6 levels and polymorphisms have been implicated in type 2 diabetes mellitus (T2DM) and insulin resistance. The IL-6 receptor (IL-6R) comprises two subunits, IL-6R and gp130, of which IL-6R confers specificity to IL-6 action and is located in a region of replicated linkage to T2DM on chromosome 1q21. We screened this gene for variation in Northern European Caucasian and African-American ethnic groups. We identified 11 variants with a minor allele frequency over 5%, including two amino acid changes (D358A and V385I) and four variants in the 3' untranslated region. No variant was associated with obesity or measures of insulin sensitivity, but two single nucleotide polymorphisms in the 3' untranslated region showed a trend to an association with T2DM in all Caucasians, and three single nucleotide polymorphisms, including D358A, showed a trend (P < 0.06) to an association with T2DM among the subset of Northern European Caucasians. Variant V385I was unique to African-Americans and was significantly associated with diabetes and diabetic nephropathy (P < 0.05). Among individuals heterozygous for the four variants in the transcribed sequence, one allele was significantly overrepresented, thus suggesting the existence of a regulatory variant controlling mRNA stability or expression. IL-6R is not likely to explain the linkage to diabetes in this region, but our work supports a minor role of variants in T2DM risk and suggests that sequence variants may alter IL-6R mRNA levels and possibly levels of soluble IL-6R.

Positive selection on MMP3 regulation has shaped heart disease risk

BACKGROUND

The evolutionary forces of mutation, natural selection, and genetic drift shape the pattern of phenotypic variation in nature, but the roles of these forces in defining the distributions of particular traits have been hard to disentangle. To better understand the mechanisms contributing to common variation in humans, we investigated the evolutionary history of a functional polymorphism in the upstream regulatory region of the MMP3 gene. This single base pair insertion/deletion variant, which results in a run of either 5 or 6 thymidines 1608 bp from the transcription start site, alters transcription factor binding and influences levels of MMP3 mRNA and protein. The polymorphism contributes to variation in arterial traits and to the risk of coronary heart disease and its progression.

RESULTS

Phylogenetic and population genetic analysis of primate sequences indicate that the binding site region is rapidly evolving and has been a hot spot for mutation for tens of millions of years. We also find evidence for the action of positive selection, beginning approximately 24,000 years ago, increasing the frequency of the high-expression allele in Europe but not elsewhere. Positive selection is evident in statistical tests of differentiation among populations and haplotype diversity within populations. Europeans have greater arterial elasticity and suffer dramatically fewer coronary heart disease events than they would have had this selection not occurred.

CONCLUSIONS

Locally elevated mutation rates and strong positive selection on a cis-regulatory variant have shaped contemporary phenotypic variation and public health.

Haplotypes produced from rare variants in the promoter and coding regions of angiotensinogen contribute to variation in angiotensinogen levels

The most efficient study design to map genes underlying complex traits will be determined by assumptions about whether the genetic effects are likely to be due to relatively few common variants or multiple rare variants. To examine the possibility that rare variants may influence blood pressure, we sequenced a 6.8 kb region of the angiotensinogen (AGT) gene in 29 male Nigerians with high plasma AGT levels and 28 with low levels. The frequency of haplotypes produced from rare variants in the promoter and coding regions was significantly different between the two groups, and it is unlikely that this difference was due to the manner in which the rare variants were selected. Further analysis suggested that most of the haplotypes produced by these rare variants are found on a haplotype background created by three common SNPs. Our study confirms in an additional trait that rare variants can influence the distribution of complex traits; whether these variants can be captured by common SNPs or haplotypes requires further investigation.

Common ERBB2 polymorphisms and risk of breast cancer in a white British population: a case-control study

Introduction

About two-thirds of the excess familial risk associated with breast cancer is still unaccounted for and may be explained by multiple weakly predisposing alleles. A gene thought to be involved in low-level predisposition to the disease is ERBB2 (HER2). This gene is involved in cell division, differentiation, and apoptosis and is frequently amplified in breast tumours. Its amplification correlates with poor prognosis. Moreover, the coding polymorphism I655V has previously been associated with an increased risk of breast cancer.

Methods

We aimed to determine if common polymorphisms (frequency ≥ 5%) in ERBB2 were associated with breast cancer risk in a white British population. Five single-nucleotide polymorphisms (SNPs) were selected for study: SNP 1 near the promoter, SNP 2 in intron 1, SNP 3 in intron 4, SNP 4 in exon 17 (I655V), and SNP 5 in exon 27 (A1170P). We tested their association with breast cancer in a large case–control study (n = 2192 cases and 2257 controls).

Results

There were no differences in genotype frequencies between cases and controls for any of the SNPs examined. To investigate the possibility that a common polymorphism not included in our study might be involved in breast cancer predisposition, we also constructed multilocus haplotypes. Our set of SNPs generated all existing (n = 6) common haplotypes and no differences were seen in haplotype frequencies between cases and controls (P = 0.44).

Conclusions

In our population, common ERBB2 polymorphisms are not involved in predisposition to breast cancer.

CBLB variants in type 1 diabetes and their genetic interaction with CTLA4

Type 1 diabetes (T1D) is a multifactorial disease with genetic and environmental components involved. Recent studies of an animal model of T1D, the Komeda diabetes-prone rat, have demonstrated that the Casitas-B-lineage lymphoma b (cblb) gene is a major susceptibility gene in the development of diabetes and other autoimmune features of this rat. As a result of the inhibitory role of Cbl-b in T cell costimulation, dysregulation of Cbl-b may also contribute to autoimmune diseases in man. Different isoforms of Cbl-b exist; we evaluated expression levels of two known transcript variants. Constitutive expression of both isoforms was demonstrated, as well as an increased expression, after cytokine exposure, of an isoform lacking exon 16, suggesting a possible role of this variant in the pathogenesis of autoimmunity. We screened coding regions of the human CBLB gene for mutations in a panel of individuals affected with several autoimmune diseases. Eight single nucleotide polymorphisms (SNPs) were detected. One SNP in exon 12 of the CBLB gene was significantly demonstrated to be associated to T1D in a large Danish T1D family material of 480 families. Evidence for common genetic factors underlying several autoimmune diseases has come from studies of cytotoxic T lymphocyte antigen 4 (CTLA4), which encodes another negatively regulatory molecule in the immune system. Gene-gene interactions probably play substantial roles in T1D susceptibility. We performed stratification of CBLB exon 12 SNP data, according to an established CTLA4 marker, CT60, and evidence for a genetic interaction between the CTLA4 and CBLB genes, involved in the same biological pathway of T cell receptor signaling, was observed.

Analysis of single-locus tests to detect gene/disease associations

A goal of association analysis is to determine whether variation in a particular candidate region or gene is associated with liability to complex disease. To evaluate such candidates, ubiquitous Single Nucleotide Polymorphisms (SNPs) are useful. It is critical, however, to select a set of SNPs that are in substantial linkage disequilibrium (LD) with all other polymorphisms in the region. Whether there is an ideal statistical framework to test such a set of 'tag SNPs' for association is unknown. Compared to tests for association based on frequencies of haplotypes, recent evidence suggests tests for association based on linear combinations of the tag SNPs (Hotelling T(2) test) are more powerful. Following this logical progression, we wondered if single-locus tests would prove generally more powerful than the regression-based tests? We answer this question by investigating four inferential procedures: the maximum of a series of test statistics corrected for multiple testing by the Bonferroni procedure, T(B), or by permutation of case-control status, T(P); a procedure that tests the maximum of a smoothed curve fitted to the series of of test statistics, T(S); and the Hotelling T(2) procedure, which we call T(R). These procedures are evaluated by simulating data like that from human populations, including realistic levels of LD and realistic effects of alleles conferring liability to disease. We find that power depends on the correlation structure of SNPs within a gene, the density of tag SNPs, and the placement of the liability allele. The clearest pattern emerges between power and the number of SNPs selected. When a large fraction of the SNPs within a gene are tested, and multiple SNPs are highly correlated with the liability allele, T(S) has better power. Using a SNP selection scheme that optimizes power but also requires a substantial number of SNPs to be genotyped (roughly 10-20 SNPs per gene), power of T(P) is generally superior to that for the other procedures, including T(R). Finally, when a SNP selection procedure that targets a minimal number of SNPs per gene is applied, the average performances of T(P) and T(R) are indistinguishable.

The Epidemiologic Approach to Pharmacogenomics

The epidemiologic approach enables the systematic evaluation of potential improvements in the safety and efficacy of drug treatment which might result from targeting treatment on the basis of genomic information. The main epidemiologic designs are the randomized control trial, the cohort study, and the case-control study, and derivatives of these proposed for investigating gene-environment interactions. However, no one design is ideal for every situation, and methodological issues, notably selection bias, information bias, confounding and chance, all play a part in determining which study design is best for a given situation. There is also a need to employ a range of different designs to establish a portfolio of evidence about specific gene-drug interactions. In view of the complexity of gene-drug interactions, pooling of data across studies is likely to be needed in order to have adequate statistical power to test hypotheses. We suggest that there may be opportunities (i) to exploit samples from trials already completed to investigate possible gene-drug interactions; (ii) to consider the use of the case-only design nested within randomized controlled trials as a possible means of reducing genotyping costs when dichotomous outcomes are being investigated; and (iii) to make use of population-based disease registries that can be linked with tissue samples, treatment information and death records, to investigate gene-treatment interactions in survival.

Applying a new generation of genetic maps to understand human inflammatory disease

The sequencing of the human genome and the intense study of its variation in different human populations have improved our understanding of the genome's architecture. It is now becoming clear that segments of the genome that are unbroken by reshuffling or recombination during meiosis create a mosaic of DNA 'haplotype blocks'. Here, we discuss the advantages and limitations of this block structure. Haplotype blocks hold the promise of reducing the complexity of analysing the human genome for association with disease. But can they deliver on this promise? First generation maps of these block patterns, such as the admixture and haplotype maps, are now emerging and, it is to be hoped, will accelerate the discovery of alleles that contribute to susceptibility to human inflammatory diseases.