Genotype imputation

Analysing biological pathways in genome-wide association studies

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

An alternative to the search for single polymorphisms: toward molecular personality scales for the five-factor model

There is growing evidence that personality traits are affected by many genes, all of which have very small effects. As an alternative to the largely unsuccessful search for individual polymorphisms associated with personality traits, the authors identified large sets of potentially related single nucleotide polymorphisms (SNPs) and summed them to form molecular personality scales (MPSs) with from 4 to 2,497 SNPs. Scales were derived from two thirds of a large (N = 3,972) sample of individuals from Sardinia who completed the Revised NEO Personality Inventory (P. T. Costa, Jr., & R. R. McCrae, 1992) and were assessed in a genomewide association scan. When MPSs were correlated with the phenotype in the remaining one third of the sample, very small but significant associations were found for 4 of the 5e personality factors when the longest scales were examined. These data suggest that MPSs for Neuroticism, Openness to Experience, Agreeableness, and Conscientiousness (but not Extraversion) contain genetic information that can be refined in future studies, and the procedures described here should be applicable to other quantitative traits.

Family-based association analysis to finemap bipolar linkage peak on chromosome 8q24 using 2,500 genotyped SNPs and 15,000 imputed SNPs

OBJECTIVE

  Multiple linkage and association studies have suggested chromosome 8q24 as a promising candidate region for bipolar disorder (BP). We performed a detailed association analysis assessing the contribution of common genetic variation in this region to the risk of BP.

METHODS

  We analyzed 2,756 single nucleotide polymorphism (SNP) markers in the chromosome 8q24 region of 3,512 individuals from 737 families. In addition, we extended genotype imputation methods to family-based data and imputed 22,725 HapMap SNPs in the same region on 8q24. We applied a family-based method to test 15,552 high-quality genotyped or imputed SNPs for association with BP.

RESULTS

  Our association analysis identified the most significant marker (p=4.80 × 10(-5) ), near the gene encoding potassium voltage-gated channel KQT-like protein (KCNQ3). Other marginally significant markers were located near adenylate cyclase 8 (ADCY8) and ST3 beta-galactoside alpha-2,3-sialyltransferase 1 (ST3GAL1).

CONCLUSIONS

  We developed an approach to apply MACH imputation to family-based data, which can increase the power to detect association signals. Our association results showed suggestive evidence of association of BP with loci near KCNQ3, ADCY8, and ST3GAL1. Consistent with genes identified by genome-wide association studies for BP, our results suggest the involvement of ion channelopathy in BP pathogenesis. However, common variants are insufficient to explain linkage findings in 8q24; other genetic variation should be explored.

MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Genome-wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studies as well as meta-analyses of GWAS. Here, we use simulations and experimental genotypes to evaluate its accuracy and utility, considering choices of genotyping panels, reference panel configurations, and designs where genotyping is replaced with shotgun sequencing. Importantly, we show that genotype imputation not only facilitates cross study analyses but also increases power of genetic association studies. We show that genotype imputation of common variants using HapMap haplotypes as a reference is very accurate using either genome-wide SNP data or smaller amounts of data typical in fine-mapping studies. Furthermore, we show the approach is applicable in a variety of populations. Finally, we illustrate how association analyses of unobserved variants will benefit from ongoing advances such as larger HapMap reference panels and whole genome shotgun sequencing technologies.

To identify associations with rare variants, just WHaIT: Weighted haplotype and imputation-based tests

Empirical evidences suggest that both common and rare variants contribute to complex disease etiology. Although the effects of common variants have been thoroughly assessed in recent genome-wide association studies (GWAS), our knowledge of the impact of rare variants on complex diseases remains limited. A number of methods have been proposed to test for rare variant association in sequencing-based studies, a study design that is becoming popular but is still not economically feasible. On the contrary, few (if any) methods exist to detect rare variants in GWAS data, the data we have collected on thousands of individuals. Here we propose two methods, a weighted haplotype-based approach and an imputation-based approach, to test for the effect of rare variants with GWAS data. Both methods can incorporate external sequencing data when available. We evaluated our methods and compared them with methods proposed in the sequencing setting through extensive simulations. Our methods clearly show enhanced statistical power over existing methods for a wide range of population-attributable risk, percentage of disease-contributing rare variants, and proportion of rare alleles working in different directions. We also applied our methods to the IFIH1 region for the type 1 diabetes GWAS data collected by the Wellcome Trust Case-Control Consortium. Our methods yield p values in the order of 10⁻³, whereas the most significant p value from the existing methods is greater than 0.17. We thus demonstrate that the evaluation of rare variants with GWAS data is possible, particularly when public sequencing data are incorporated.

Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes

Next Generation Sequencing Technology has revolutionized our ability to study the contribution of rare genetic variation to heritable traits. However, existing single-marker association tests are underpowered for detecting rare risk variants. A more powerful approach involves pooling methods that combine multiple rare variants from the same gene into a single test statistic. Proposed pooling methods can be limited because they generally assume high-quality genotypes derived from deep-coverage sequencing, which may not be available. In this paper, we consider an intuitive and computationally efficient pooling statistic, the cumulative minor-allele test (CMAT). We assess the performance of the CMAT and other pooling methods on datasets simulated with population genetic models to contain realistic levels of neutral variation. We consider study designs ranging from exon-only to whole-gene analyses that contain noncoding variants. For all study designs, the CMAT achieves power comparable to that of previously proposed methods. We then extend the CMAT to probabilistic genotypes and describe application to low-coverage sequencing and imputation data. We show that augmenting sequence data with imputed samples is a practical method for increasing the power of rare-variant studies. We also provide a method of controlling for confounding variables such as population stratification. Finally, we demonstrate that our method makes it possible to use external imputation templates to analyze rare variants imputed into existing GWAS datasets. As proof of principle, we performed a CMAT analysis of more than 8 million SNPs that we imputed into the GAIN psoriasis dataset by using haplotypes from the 1000 Genomes Project.

Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

Genome-wide association scan of trait depression

BACKGROUND

Independent of temporal circumstances, some individuals have greater susceptibility to depressive affects, such as feelings of guilt, sadness, hopelessness, and loneliness. Identifying the genetic variants that contribute to these individual differences can point to biological pathways etiologically involved in psychiatric disorders.

METHODS

Genome-wide association scans for the depression scale of the Revised NEO Personality Inventory in community-based samples from a genetically homogeneous area of Sardinia, Italy (n = 3972) and from the Baltimore Longitudinal Study of Aging in the United States (n = 839).

RESULTS

Meta-analytic results for genotyped or imputed single nucleotide polymorphisms indicate that the strongest association signals for trait depression were found in RORA (rs12912233; p = 6 × 10⁻⁷), a gene involved in circadian rhythm. A plausible biological association was also found with single nucleotide polymorphisms within GRM8 (rs17864092; p = 5 × 10⁻⁶), a metabotropic receptor for glutamate, a major excitatory neurotransmitter in the central nervous system.

CONCLUSIONS

These findings suggest shared genetic basis underlying the continuum from personality traits to psychopathology.

Statistical analysis strategies for association studies involving rare variants

The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.

Genome-wide association analysis identifies three psoriasis susceptibility loci

We carried out a meta-analysis of two recent psoriasis genome-wide association studies with a combined discovery sample of 1,831 affected individuals (cases) and 2,546 controls. One hundred and two loci selected based on P value rankings were followed up in a three-stage replication study including 4,064 cases and 4,685 controls from Michigan, Toronto, Newfoundland and Germany. In the combined meta-analysis, we identified three new susceptibility loci, including one at NOS2 (rs4795067, combined P = 4 × 10⁻¹¹), one at FBXL19 (rs10782001, combined P = 9 × 10⁻¹⁰) and one near PSMA6-NFKBIA (rs12586317, combined P = 2 × 10⁻⁸). All three loci were also associated with psoriatic arthritis (rs4795067, combined P = 1 × 10⁻⁵; rs10782001, combined P = 4 × 10⁻⁸; and rs12586317, combined P = 6 × 1⁻⁵) and purely cutaneous psoriasis (rs4795067, combined P = 1 × 10⁻⁸; rs10782001, combined P = 2 × 10⁻⁶; and rs12586317, combined P = 1 × 10⁻⁶). We also replicated a recently identified association signal near RNF114 (rs495337, combined P = 2 × 10⁻⁷).

Most genome-wide significant susceptibility loci for schizophrenia and bipolar disorder reported to date cross-traditional diagnostic boundaries

Recent findings from genetic epidemiology and from genome-wide association studies point strongly to a partial overlap in the genes that contribute susceptibility to schizophrenia and bipolar disorder (BD). Previous data have also directly implicated one of the best supported schizophrenia-associated loci, zinc finger binding protein 804A (ZNF804A), as showing trans-disorder effects, and the same is true for one of the best supported bipolar loci, calcium channel, voltage-dependent, L type, alpha 1C subunit (CACNA1C) which has also been associated with schizophrenia. We have undertaken a cross-phenotype study based upon the remaining variants that show genome-wide evidence for association in large schizophrenia and BD meta-analyses. These comprise in schizophrenia, SNPs in or in the vicinity of transcription factor 4 (TCF4), neurogranin (NRGN) and an extended region covering the MHC locus on chromosome 6. For BD, the strongly supported variants are in the vicinity of ankyrin 3, node of Ranvier (ANK3) and polybromo-1 (PBRM1). Using data sets entirely independent of their original discoveries, we observed strong evidence that the PBRM1 locus is also associated with schizophrenia (P = 0.00015) and nominally significant evidence (P < 0.05) that the NRGN and the extended MHC region are associated with BD. Moreover, considering this highly restricted set of loci as a group, the evidence for trans-disorder effects is compelling (P = 4.7 × 10(-5)). Including earlier reported data for trans-disorder effects for ZNF804A and CACNA1C, six out of eight of the most robustly associated loci for either disorder show trans-disorder effects.

Genome-wide association study identifies a psoriasis susceptibility locus at TRAF3IP2

Psoriasis is a multifactorial skin disease characterized by epidermal hyperproliferation and chronic inflammation, the most common form of which is psoriasis vulgaris (PsV). We present a genome-wide association analysis of 2,339,118 SNPs in 472 PsV cases and 1,146 controls from Germany, with follow-up of the 147 most significant SNPs in 2,746 PsV cases and 4,140 controls from three independent replication panels. We identified an association at TRAF3IP2 on 6q21 and genotyped two SNPs at this locus in two additional replication panels (the combined discovery and replication panels consisted of 6,487 cases and 8,037 controls; combined P = 2.36 × 10⁻¹⁰ for rs13210247 and combined P = 1.24 × 10⁻¹⁶ for rs33980500). About 15% of psoriasis cases develop psoriatic arthritis (PsA). A stratified analysis of our datasets including only PsA cases (1,922 cases compared to 8,037 controls, P = 4.57 × 10⁻¹² for rs33980500) suggested that TRAF3IP2 represents a shared susceptibility for PsV and PsA. TRAF3IP2 encodes a protein involved in IL-17 signaling and which interacts with members of the Rel/NF-κB transcription factor family.

Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations

BACKGROUND

Presence of interaction between a genotype and certain factor in determination of a trait's value, it is expected that the trait's variance is increased in the group of subjects having this genotype. Thus, test of heterogeneity of variances can be used as a test to screen for potentially interacting single-nucleotide polymorphisms (SNPs). In this work, we evaluated statistical properties of variance heterogeneity analysis in respect to the detection of potentially interacting SNPs in a case when an interaction variable is unknown.

RESULTS

Through simulations, we investigated type I error for Bartlett's test, Bartlett's test with prior rank transformation of a trait to normality, and Levene's test for different genetic models. Additionally, we derived an analytical expression for power estimation. We showed that Bartlett's test has acceptable type I error in the case of trait following a normal distribution, whereas Levene's test kept nominal Type I error under all scenarios investigated. For the power of variance homogeneity test, we showed (as opposed to the power of direct test which uses information about known interacting factor) that, given the same interaction effect, the power can vary widely depending on the non-estimable direct effect of the unobserved interacting variable. Thus, for a given interaction effect, only very wide limits of power of the variance homogeneity test can be estimated. Also we applied Levene's approach to test genome-wide homogeneity of variances of the C-reactive protein in the Rotterdam Study population (n = 5959). In this analysis, we replicate previous results of Pare and colleagues (2010) for the SNP rs12753193 (n = 21,799).

CONCLUSIONS

Screening for differences in variances among genotypes of a SNP is a promising approach as a number of biologically interesting models may lead to the heterogeneity of variances. However, it should be kept in mind that the absence of variance heterogeneity for a SNP can not be interpreted as the absence of involvement of the SNP in the interaction network.

The Machado-Joseph disease-associated mutant form of ataxin-3 regulates parkin ubiquitination and stability

Machado-Joseph disease (MJD), the most common dominantly inherited ataxia worldwide, is caused by a polyglutamine (polyQ) expansion in the deubiquitinating (DUB) enzyme ataxin-3. Interestingly, MJD can present clinically with features of Parkinsonism. In this study, we identify parkin, an E3 ubiquitin-ligase responsible for a common familial form of Parkinson's disease, as a novel ataxin-3 binding partner. The interaction between ataxin-3 and parkin is direct, involves multiple domains and is greatly enhanced by parkin self-ubiquitination. Moreover, ataxin-3 deubiquitinates parkin directly in vitro and in cells. Compared with wild-type ataxin-3, MJD-linked polyQ-expanded mutant ataxin-3 is more active, possibly owing to its greater efficiency at DUB K27- and K29-linked Ub conjugates on parkin. Remarkably, mutant but not wild-type ataxin-3 promotes the clearance of parkin via the autophagy pathway. The finding is consistent with the reduction in parkin levels observed in the brains of transgenic mice over-expressing polyQ-expanded but not wild-type ataxin-3, raising the intriguing possibility that increased turnover of parkin may contribute to the pathogenesis of MJD and help explain some of its parkinsonian features.

Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Gene-based interaction analysis by incorporating external linkage disequilibrium information

Gene-gene interactions have an important role in complex human diseases. Detection of gene-gene interactions has long been a challenge due to their complexity. The standard method aiming at detecting SNP-SNP interactions may be inadequate as it does not model linkage disequilibrium (LD) among SNPs in each gene and may lose power due to a large number of comparisons. To improve power, we propose a principal component (PC)-based framework for gene-based interaction analysis. We analytically derive the optimal weight for both quantitative and binary traits based on pairwise LD information. We then use PCs to summarize the information in each gene and test for interactions between the PCs. We further extend this gene-based interaction analysis procedure to allow the use of imputation dosage scores obtained from a popular imputation software package, MACH, which incorporates multilocus LD information. To evaluate the performance of the gene-based interaction tests, we conducted extensive simulations under various settings. We demonstrate that gene-based interaction tests are more powerful than SNP-based tests when more than two variants interact with each other; moreover, tests that incorporate external LD information are generally more powerful than those that use genotyped markers only. We also apply the proposed gene-based interaction tests to a candidate gene study on high-density lipoprotein. As our method operates at the gene level, it can be applied to a genome-wide association setting and used as a screening tool to detect gene-gene interactions.

Genome-wide associations and functional genomic studies of musculoskeletal adverse events in women receiving aromatase inhibitors

PURPOSE

We performed a case-control genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with musculoskeletal adverse events (MS-AEs) in women treated with aromatase inhibitors (AIs) for early breast cancer.

PATIENTS AND METHODS

A nested case-control design was used to select patients enrolled onto the MA.27 phase III trial comparing anastrozole with exemestane. Cases were matched to two controls and were defined as patients with grade 3 or 4 MS-AEs (according to the National Cancer Institute's Common Terminology Criteria for Adverse Events v3.0) or those who discontinued treatment for any grade of MS-AE within the first 2 years. Genotyping was performed with the Illumina Human610-Quad BeadChip.

RESULTS

The GWAS included 293 cases and 585 controls. A total of 551,358 SNPs were analyzed, followed by imputation and fine mapping of a region of interest on chromosome 14. Four SNPs on chromosome 14 had the lowest P values (2.23E-06 to 6.67E-07). T-cell leukemia 1A (TCL1A) was the gene closest (926-7000 bp) to the four SNPs. Functional genomic studies revealed that one of these SNPs (rs11849538) created an estrogen response element and that TCL1A expression was estrogen dependent, was associated with the variant SNP genotypes in estradiol-treated lymphoblastoid cells transfected with estrogen receptor alpha and was directly related to interleukin 17 receptor A (IL17RA) expression.

CONCLUSION

This GWAS identified SNPs associated with MS-AEs in women treated with AIs and with a gene (TCL1A) which, in turn, was related to a cytokine (IL17). These findings provide a focus for further research to identify patients at risk for MS-AEs and to explore the mechanisms for these adverse events.

Genome-wide association-, replication-, and neuroimaging study implicates HOMER1 in the etiology of major depression

BACKGROUND

Genome-wide association studies are a powerful tool for unravelling the genetic background of complex disorders such as major depression.

METHODS

We conducted a genome-wide association study of 604 patients with major depression and 1364 population based control subjects. The top hundred findings were followed up in a replication sample of 409 patients and 541 control subjects.

RESULTS

Two SNPs showed nominally significant association in both the genome-wide association study and the replication samples: 1) rs9943849 (p(combined) = 3.24E-6) located upstream of the carboxypeptidase M (CPM) gene and 2) rs7713917 (p(combined) = 1.48E-6), located in a putative regulatory region of HOMER1. Further evidence for HOMER1 was obtained through gene-wide analysis while conditioning on the genotypes of rs7713917 (p(combined) = 4.12E-3). Homer1 knockout mice display behavioral traits that are paradigmatic of depression, and transcriptional variants of Homer1 result in the dysregulation of cortical-limbic circuitry. This is consistent with the findings of our subsequent human imaging genetics study, which revealed that variation in single nucleotide polymorphism rs7713917 had a significant influence on prefrontal activity during executive cognition and anticipation of reward.

CONCLUSION

Our findings, combined with evidence from preclinical and animal studies, suggest that HOMER1 plays a role in the etiology of major depression and that the genetic variation affects depression via the dysregulation of cognitive and motivational processes.

E2-2 protein and Fuchs's corneal dystrophy

BACKGROUND

Fuchs's corneal dystrophy (FCD) is a leading cause of corneal transplantation and affects 5% of persons in the United States who are over the age of 40 years. Clinically visible deposits called guttae develop under the corneal endothelium in patients with FCD. A loss of endothelial cells and deposition of an abnormal extracellular matrix are observed microscopically. In advanced disease, the cornea swells and becomes cloudy because the remaining endothelial cells are not sufficient to keep the cornea dehydrated and clear. Although rare genetic variation that contributes to both early-onset and typical late-onset forms of FCD has been identified, to our knowledge, no common variants have been reported.

METHODS

We performed a genomewide association study and replicated the most significant observations in a second, independent group of subjects.

RESULTS

Alleles in the transcription factor 4 gene (TCF4), encoding a member of the E-protein family (E2-2), were associated with typical FCD (P=2.3x10(-26)). The association increased the odds of having FCD by a factor of 30 for persons with two copies of the disease variants (homozygotes) and discriminated between case subjects and control subjects with about 76% accuracy. At least two regions of the TCF4 locus were associated independently with FCD. Alleles in the gene encoding protein tyrosine phosphatase receptor type G (PTPRG) were associated with FCD (P=4.0x10(-7)), but the association did not reach genomewide significance.

CONCLUSIONS

Genetic variation in TCF4 contributes to the development of FCD. (Funded by the National Eye Institute and others.)

Founder population-specific HapMap panel increases power in GWA studies through improved imputation accuracy and CNV tagging

The combining of genome-wide association (GWA) data across populations represents a major challenge for massive global meta-analyses. Genotype imputation using densely genotyped reference samples facilitates the combination of data across different genotyping platforms. HapMap data is typically used as a reference for single nucleotide polymorphism (SNP) imputation and tagging copy number polymorphisms (CNPs). However, the advantage of having population-specific reference panels for founder populations has not been evaluated. We looked at the properties and impact of adding 81 individuals from a founder population to HapMap3 reference data on imputation quality, CNP tagging, and power to detect association in simulations and in an independent cohort of 2138 individuals. The gain in SNP imputation accuracy was highest among low-frequency markers (minor allele frequency [MAF] < 5%), for which adding the population-specific samples to the reference set increased the median R(2) between imputed and genotyped SNPs from 0.90 to 0.94. Accuracy also increased in regions with high recombination rates. Similarly, a reference set with population-specific extension facilitated the identification of better tag-SNPs for a subset of CNPs; for 4% of CNPs the R(2) between SNP genotypes and CNP intensity in the independent population cohort was at least twice as high as without the extension. We conclude that even a relatively small population-specific reference set yields considerable benefits in SNP imputation, CNP tagging accuracy, and the power to detect associations in founder populations and population isolates in particular.

Genome-wide association studies in nephrology research

Kidney diseases constitute a serious public health burden worldwide, with substantial associated morbidity and mortality. The role of a genetic contribution to kidney disease is supported by heritability studies of kidney function measures, the presence of monogenic diseases with renal manifestations, and familial aggregation studies of complex kidney diseases, such as chronic kidney disease. Because complex diseases arise from the combination of multiple genetic and environmental risk factors, the identification of underlying genetic susceptibility variants has been challenging. Recently, genome-wide association studies have emerged as a method to conduct searches for such susceptibility variants. They have successfully identified genomic loci that contain variants associated with kidney diseases and measures of kidney function. For example, common variants in the UMOD and PRKAG2 genes are associated with risk of chronic kidney disease; variants in CLDN14 with risk of kidney stone disease; and variants in or near SHROOM3, STC1, LASS2, GCKR, NAT8/ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, FAM122A/PIP5K1B, ATXN2, DACH1, UBE2Q2/FBXO22, and SLC7A9, with differences in glomerular filtration rate. The purpose of this review is to provide an overview of the genome-wide association study method as it relates to nephrology research and summarize recent findings in the field. Results from genome-wide association studies of renal phenotypes represent a first step toward improving our knowledge about underlying mechanisms of kidney function and disease and ultimately may aid in the improved treatment and prevention of kidney diseases.

Genome-wide association study identifies new HLA class II haplotypes strongly protective against narcolepsy

Narcolepsy is a rare sleep disorder with the strongest human leukocyte antigen (HLA) association ever reported. Since the associated HLA-DRB1*1501-DQB1*0602 haplotype is common in the general population (15-25%), it has been suggested that it is almost necessary but not sufficient for developing narcolepsy. To further define the genetic basis of narcolepsy risk, we performed a genome-wide association study (GWAS) in 562 European individuals with narcolepsy (cases) and 702 ethnically matched controls, with independent replication in 370 cases and 495 controls, all heterozygous for DRB1*1501-DQB1*0602. We found association with a protective variant near HLA-DQA2 (rs2858884; P < 3 x 10(-8)). Further analysis revealed that rs2858884 is strongly linked to DRB1*03-DQB1*02 (P < 4 x 10(-43)) and DRB1*1301-DQB1*0603 (P < 3 x 10(-7)). Cases almost never carried a trans DRB1*1301-DQB1*0603 haplotype (odds ratio = 0.02; P < 6 x 10(-14)). This unexpected protective HLA haplotype suggests a virtually causal involvement of the HLA region in narcolepsy susceptibility.

Linking variants from genome-wide association analysis to function via transcriptional network analysis

A current challenge in interpretation of genome-wide association studies is to establish the mechanistic links between the measured genotype and observed phenotype. The integration of gene expression with disease genome-wide association studies is emerging as an important strategy for deciphering these regulatory mechanisms. For renal disease, the availability of both tissue- and disease-specific expression data makes the strategy a compelling option. In this review, three approaches of integrating single nucleotide polymorphism (SNP) genotypes with transcriptional regulation are discussed as follows: (1) interpreting the functional role of transcripts affected by a SNP, (2) identifying the mechanistic role of noncoding SNPs in regulation, and (3) identifying regulatory candidate SNPs with expression associations. Combining these strategies in an integrative manner should allow the discovery of more extensive regulatory information. Linking genetics to systems biology more directly promises the opportunity to explain how genetic variants contribute to disease in a truly holistic manner.

Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing receptor (CASR) gene

Calcium has a pivotal role in biological functions, and serum calcium levels have been associated with numerous disorders of bone and mineral metabolism, as well as with cardiovascular mortality. Here we report results from a genome-wide association study of serum calcium, integrating data from four independent cohorts including a total of 12,865 individuals of European and Indian Asian descent. Our meta-analysis shows that serum calcium is associated with SNPs in or near the calcium-sensing receptor (CASR) gene on 3q13. The top hit with a p-value of 6.3×10^-37 is rs1801725, a missense variant, explaining 1.26% of the variance in serum calcium. This SNP had the strongest association in individuals of European descent, while for individuals of Indian Asian descent the top hit was rs17251221 (p = 1.1×10^-21), a SNP in strong linkage disequilibrium with rs1801725. The strongest locus in CASR was shown to replicate in an independent Icelandic cohort of 4,126 individuals (p = 1.02×10^-4). This genome-wide meta-analysis shows that common CASR variants modulate serum calcium levels in the adult general population, which confirms previous results in some candidate gene studies of the CASR locus. This study highlights the key role of CASR in calcium regulation.

Calcium levels in blood serum play an important role in many biological processes. The regulation of serum calcium is under strong genetic control. This study describes the first meta-analysis of a genome-wide association study from four cohorts totaling 12,865 participants of European and Indian Asian descent. Confirming previous results in some candidate gene studies, we find that common polymorphisms at the calcium-sensing receptor (CASR) gene locus are associated with serum calcium concentrations. We show that CASR variants give rise to the strongest signals associated with serum calcium levels in both European and Indian Asian populations, while no other locus reaches genome-wide significance. Our results show that CASR is a key player in genetic regulation of serum calcium in the adult general population.

Association between common variation in genes encoding sweet taste signaling components and human sucrose perception

Variation in taste perception of different chemical substances is a well-known phenomenon in both humans and animals. Recent advances in the understanding of sweet taste signaling have identified a number of proteins involved in this signal transduction. We evaluated the hypothesis that sequence variations occurring in genes encoding taste signaling molecules can influence sweet taste perception in humans. Our population consisted of unrelated individuals (n = 160) of Caucasian, African-American, and Asian descent. Threshold and suprathreshold sensitivities of participants for sucrose were estimated using a sorting test and signal detection analysis that produced cumulative R-index area under the curve (AUC) scores. Genetic association analysis revealed significant correlation of sucrose AUC scores with genetic variation occurring in the GNAT3 gene (single point P = 10(-3) to 10(-4)), which encodes the taste-specific G(alpha) protein subunit gustducin. Subsequent sequencing identified additional GNAT3 variations having significant association with sucrose AUC scores. Collectively, GNAT3 polymorphisms explain 13% of the variation in sucrose perception. Our findings underscore the importance of common genetic variants influencing human taste perception.

Family-based association analysis of 42 hereditary prostate cancer families identifies the Apolipoprotein L3 region on chromosome 22q12 as a risk locus

Multiple genome-wide scans for hereditary prostate cancer (HPC) have identified susceptibility loci on nearly every chromosome. However, few results have been replicated with statistical significance. One exception is chromosome 22q, for which five independent linkage studies yielded strong evidence for a susceptibility locus in HPC families. Previously, we refined this region to a 2.53 Mb interval, using recombination mapping in 42 linked pedigrees. We now refine this locus to a 15 kb interval, spanning Apolipoprotein L3 (APOL3), using family-based association analyses of 150 total prostate cancer (PC) cases from two independent family collections with 506 unrelated population controls. Analysis of the two independent sets of PC cases highlighted single nucleotide polymorphisms (SNPs) within the APOL3 locus showing the strongest associations with HPC risk, with the most robust results observed when all 150 cases were combined. Analysis of 15 tagSNPs across the 5' end of the locus identified six SNPs with P-values < or =2 × 10(-4). The two independent sets of HPC cases highlight the same 15 kb interval at the 5' end of the APOL3 gene and provide strong evidence that SNPs within this 15 kb interval, or in strong linkage disequilibrium with it, contribute to HPC risk. Further analyses of this locus in an independent population-based, case-control study revealed an association between an SNP within the APOL3 locus and PC risk, which was not confirmed in the Cancer Genetic Markers of Susceptibility data set. This study further characterizes the 22q locus in HPC risk and suggests that the role of this region in sporadic PC warrants additional studies.

Case-control association study of TGOLN2 in attempted suicide

Family, twin, and adoption studies provide convincing evidence for a genetic contribution to suicidal behavior. The heritability for suicidal behavior depends in part on the transmission of psychiatric disorders, such as mood disorders and substance use disorders, but is also partly independent of them. Three linkage studies using the attempted suicide phenotype in pedigrees with bipolar disorder, major depression, or alcoholism have provided consistent evidence that 2p11-12 harbors a susceptibility gene for attempted suicide. A microarray expression study using postmortem brain samples has implicated a gene from the 2p11-12 candidate region, the trans-Golgi network protein 2 (TGOLN2) gene, as being consistently up-regulated in suicide cases as compared to controls. Here, we present a TGOLN2 case-control association study using nine single nucleotide polymorphisms (SNPs). These nine SNPs, which include seven tag SNPs and two coding SNPs, have been genotyped in 517 mood disorder subjects with a history of attempted suicide and 515 normal controls. Allelic and genotypic analyses of the case-control sample did not provide evidence for association with the attempted suicide phenotype. Eight of the nine SNPs provided supportive evidence for association (P-values ranging from 0.008 to 0.03) when we compared the attempted suicide cases with a history of alcoholism to the attempted suicide cases without a history of alcoholism. However, this association finding was not replicated in an independent sample. Taken together, these analyses do not provide support for the hypothesis that common genetic variation in TGOLN2 contributes significantly to the risk for attempted suicide in subjects with major mood disorders.

Association of CR1, CLU and PICALM with Alzheimer's disease in a cohort of clinically characterized and neuropathologically verified individuals

In this study, we assess 34 of the most replicated genetic associations for Alzheimer's disease (AD) using data generated on Affymetrix SNP 6.0 arrays and imputed at over 5.7 million markers from a unique cohort of over 1600 neuropathologically defined AD cases and controls (1019 cases and 591 controls). Testing the top genes from the AlzGene meta-analysis, we confirm the well-known association with APOE single nucleotide polymorphisms (SNPs), the CLU, PICALM and CR1 SNPs recently implicated in unusually large data sets, and previously implicated CST3 and ACE SNPs. In the cases of CLU, PICALM and CR1, as well as in APOE, the odds ratios we find are slightly larger than those previously reported in clinical samples, consistent with what we believe to be more accurate classification of disease in the clinically characterized and neuropathologically confirmed AD cases and controls.

Genome-wide association study identifies variants at CSF1, OPTN and TNFRSF11A as genetic risk factors for Paget's disease of bone

Paget's disease of bone (PDB) is a common disorder with a strong genetic component characterized by focal increases in bone turnover, which in some cases is caused by mutations in SQSTM1. To identify additional susceptibility genes, we performed a genome-wide association study in 750 individuals with PDB (cases) without SQSTM1 mutations and 1,002 controls and identified three candidate disease loci, which were then replicated in an independent set of 500 cases and 535 controls. The strongest signal was with rs484959 on 1p13 near the CSF1 gene (P = 5.38 x 10(-24)). Significant associations were also observed with rs1561570 on 10p13 within the OPTN gene (P = 6.09 x 10(-13)) and with rs3018362 on 18q21 near the TNFRSF11A gene (P = 5.27 x 10(-13)). These studies provide new insights into the pathogenesis of PDB and identify OPTN, CSF1 and TNFRSF11A as candidate genes for disease susceptibility.

Analyses and comparison of imputation-based association methods

Genotype imputation methods have become increasingly popular for recovering untyped genotype data. An important application with imputed genotypes is to test genetic association for diseases. Imputation-based association test can provide additional insight beyond what is provided by testing on typed tagging SNPs only. A variety of effective imputation-based association tests have been proposed. However, their performances are affected by a variety of genetic factors, which have not been well studied. In this study, using both simulated and real data sets, we investigated the effects of LD, MAF of untyped causal SNP and imputation accuracy rate on the performances of seven popular imputation-based association methods, including MACH2qtl/dat, SNPTEST, ProbABEL, Beagle, Plink, BIMBAM and SNPMStat. We also aimed to provide a comprehensive comparison among methods. Results show that: 1). imputation-based association tests can boost signals and improve power under medium and high LD levels, with the power improvement increasing with strengthening LD level; 2) the power increases with higher MAF of untyped causal SNPs under medium to high LD level; 3). under low LD level, a high imputation accuracy rate cannot guarantee an improvement of power; 4). among methods, MACH2qtl/dat, ProbABEL and SNPTEST perform similarly and they consistently outperform other methods. Our results are helpful in guiding the choice of imputation-based association test in practical application.

Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis

A genome-wide association scan of approximately 6.6 million genotyped or imputed variants in 882 Sardinian individuals with multiple sclerosis (cases) and 872 controls suggested association of CBLB gene variants with disease, which was confirmed in 1,775 cases and 2,005 controls (rs9657904, overall P = 1.60 x 10(-10), OR = 1.40). CBLB encodes a negative regulator of adaptive immune responses, and mice lacking the ortholog are prone to experimental autoimmune encephalomyelitis, the animal model of multiple sclerosis.

Digital quantification of human eye color highlights genetic association of three new loci

Previous studies have successfully identified genetic variants in several genes associated with human iris (eye) color; however, they all used simplified categorical trait information. Here, we quantified continuous eye color variation into hue and saturation values using high-resolution digital full-eye photographs and conducted a genome-wide association study on 5,951 Dutch Europeans from the Rotterdam Study. Three new regions, 1q42.3, 17q25.3, and 21q22.13, were highlighted meeting the criterion for genome-wide statistically significant association. The latter two loci were replicated in 2,261 individuals from the UK and in 1,282 from Australia. The LYST gene at 1q42.3 and the DSCR9 gene at 21q22.13 serve as promising functional candidates. A model for predicting quantitative eye colors explained over 50% of trait variance in the Rotterdam Study. Over all our data exemplify that fine phenotyping is a useful strategy for finding genes involved in human complex traits.

We measured human eye color to hue and saturation values from high-resolution, digital, full-eye photographs of several thousand Dutch Europeans. This quantitative approach, which is extremely cost-effective, portable, and time efficient, revealed that human eye color varies along more dimensions than the one represented by the blue-green-brown categories studied previously. Our work represents the first genome-wide study of quantitative human eye color. We clearly identified 3 new loci, LYST, 17q25.3, TTC3/DSCR9, in contributing to the natural and subtle eye color variation along multiple dimensions, providing new leads towards a more detailed understanding of the genetic basis of human eye color. Our quantitative prediction model explained over 50% of eye color variance, representing the highest accuracy achieved so far in genomic prediction of human complex and quantitative traits, with relevance for future forensic applications.

Genome-wide association studies in diverse populations

Genome-wide association (GWA) studies have identified a large number of SNPs associated with disease phenotypes. As most GWA studies have been performed in populations of European descent, this Review examines the issues involved in extending the consideration of GWA studies to diverse worldwide populations. Although challenges exist with issues such as imputation, admixture and replication, investigation of a greater diversity of populations could make substantial contributions to the goal of mapping the genetic determinants of complex diseases for the human population as a whole.

Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior

Smoking is a common risk factor for many diseases. We conducted genome-wide association meta-analyses for the number of cigarettes smoked per day (CPD) in smokers (n = 31,266) and smoking initiation (n = 46,481) using samples from the ENGAGE Consortium. In a second stage, we tested selected SNPs with in silico replication in the Tobacco and Genetics (TAG) and Glaxo Smith Kline (Ox-GSK) consortia cohorts (n = 45,691 smokers) and assessed some of those in a third sample of European ancestry (n = 9,040). Variants in three genomic regions associated with CPD (P < 5 x 10(-8)), including previously identified SNPs at 15q25 represented by rs1051730[A] (effect size = 0.80 CPD, P = 2.4 x 10(-69)), and SNPs at 19q13 and 8p11, represented by rs4105144[C] (effect size = 0.39 CPD, P = 2.2 x 10(-12)) and rs6474412-T (effect size = 0.29 CPD, P = 1.4 x 10(-8)), respectively. Among the genes at the two newly associated loci are genes encoding nicotine-metabolizing enzymes (CYP2A6 and CYP2B6) and nicotinic acetylcholine receptor subunits (CHRNB3 and CHRNA6), all of which have been highlighted in previous studies of smoking and nicotine dependence. Nominal associations with lung cancer were observed at both 8p11 (rs6474412[T], odds ratio (OR) = 1.09, P = 0.04) and 19q13 (rs4105144[C], OR = 1.12, P = 0.0006).

Genome-wide meta-analyses identify multiple loci associated with smoking behavior

Consistent but indirect evidence has implicated genetic factors in smoking behavior. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], beta = 1.03, standard error (s.e.) = 0.053, P = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], beta = 0.367, s.e. = 0.059, P = 5.7 x 10(-10); and rs1028936[A], beta = 0.446, s.e. = 0.074, P = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], beta = 0.333, s.e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.

Genome-wide association of anthropometric traits in African- and African-derived populations

Genome-wide association (GWA) studies have identified common variants that are associated with a variety of traits and diseases, but most studies have been performed in European-derived populations. Here, we describe the first genome-wide analyses of imputed genotype and copy number variants (CNVs) for anthropometric measures in African-derived populations: 1188 Nigerians from Igbo-Ora and Ibadan, Nigeria, and 743 African-Americans from Maywood, IL. To improve the reach of our study, we used imputation to estimate genotypes at approximately 2.1 million single-nucleotide polymorphisms (SNPs) and also tested CNVs for association. No SNPs or common CNVs reached a genome-wide significance level for association with height or body mass index (BMI), and the best signals from a meta-analysis of the two cohorts did not replicate in approximately 3700 African-Americans and Jamaicans. However, several loci previously confirmed in European populations showed evidence of replication in our GWA panel of African-derived populations, including variants near IHH and DLEU7 for height and MC4R for BMI. Analysis of global burden of rare CNVs suggested that lean individuals possess greater total burden of CNVs, but this finding was not supported in an independent European population. Our results suggest that there are not multiple loci with strong effects on anthropometric traits in African-derived populations and that sample sizes comparable to those needed in European GWA studies will be required to identify replicable associations. Meta-analysis of this data set with additional studies in African-ancestry populations will be helpful to improve power to detect novel associations.

Integrating pathway analysis and genetics of gene expression for genome-wide association studies

Genome-wide association studies (GWAS) have achieved great success identifying common genetic variants associated with common human diseases. However, to date, the massive amounts of data generated from GWAS have not been maximally leveraged and integrated with other types of data to identify associations beyond those associations that meet the stringent genome-wide significance threshold. Here, we present a novel approach that leverages information from genetics of gene expression studies to identify biological pathways enriched for expression-associated genetic loci associated with disease in publicly available GWAS results. Specifically, we first identify SNPs in population-based human cohorts that associate with the expression of genes (eSNPs) in the metabolically active tissues liver, subcutaneous adipose, and omental adipose. We then use this functionally annotated set of SNPs to investigate pathways enriched for eSNPs associated with disease in publicly available GWAS data. As an example, we tested 110 pathways from the Kyoto Encylopedia of Genes and Genomes (KEGG) database and identified 16 pathways enriched for genes corresponding to eSNPs that show evidence of association with type 2 diabetes (T2D) in the Wellcome Trust Case Control Consortium (WTCCC) T2D GWAS. We then replicated these findings in the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) study. Many of the pathways identified have been proposed as important candidate pathways for T2D, including the calcium signaling pathway, the PPAR signaling pathway, and TGF-beta signaling. Importantly, we identified other pathways not previously associated with T2D, including the tight junction, complement and coagulation pathway, and antigen processing and presentation pathway. The integration of pathways and eSNPs provides putative functional bridges between GWAS and candidate genes or pathways, thus serving as a potential powerful approach to identifying biological mechanisms underlying GWAS findings.

Transcriptome analysis and molecular signature of human retinal pigment epithelium

Retinal pigment epithelium (RPE) is a polarized cell layer critical for photoreceptor function and survival. The unique physiology and relationship to the photoreceptors make the RPE a critical determinant of human vision. Therefore, we performed a global expression profiling of native and cultured human fetal and adult RPE and determined a set of highly expressed ‘signature’ genes by comparing the observed RPE gene profiles to the Novartis expression database (SymAtlas: http://wombat.gnf.org/index.html) of 78 tissues. Using stringent selection criteria of at least 10-fold higher expression in three distinct preparations, we identified 154 RPE signature genes, which were validated by qRT-PCR analysis in RPE and in an independent set of 11 tissues. Several of the highly expressed signature genes encode proteins involved in visual cycle, melanogenesis and cell adhesion and Gene ontology analysis enabled the assignment of RPE signature genes to epithelial channels and transporters (ClCN4, BEST1, SLCA20) or matrix remodeling (TIMP3, COL8A2). Fifteen RPE signature genes were associated with known ophthalmic diseases, and 25 others were mapped to regions of disease loci. An evaluation of the RPE signature genes in a recently completed AMD genomewide association (GWA) data set revealed that TIMP3, GRAMD3, PITPNA and CHRNA3 signature genes may have potential roles in AMD pathogenesis and deserve further examination. We propose that RPE signature genes are excellent candidates for retinal diseases and for physiological investigations (e.g. dopachrome tautomerase in melanogenesis). The RPE signature gene set should allow the validation of RPE-like cells derived from human embryonic or induced pluripotent stem cells for cell-based therapies of degenerative retinal diseases.

Exploring genetic susceptibility to cancer in diverse populations

Incidence rates for many cancers differ markedly by race/ethnicity and furthering our understanding of the genetic and environmental causes of such disparities is a scientific and public health need. Genome-wide association studies (GWAS) are widely acknowledged to provide important information about the etiology of common cancers. To date, these studies have been primarily conducted in European-derived populations. There are important reasons for extending the reach of GWAS studies to other groups and for conducting multiethnic genetic studies involving multiple populations and admixed populations. These include a (1) need to discover the full scope of variants that affect risk of disease in all populations, (2) furthering the understanding of disease pathways, and (3) to assist in fine-mapping of genetic associations by exploiting the differences in linkage disequilibrium between populations to narrow the range of marker alleles demarking regions that contain a true biologically relevant variant. Challenges to multiethnic studies relating to study power, control for hidden population structure, imputation, and use of shared controls for multiple cancer endpoints are discussed.

The genome-wide association study--a new era for common polygenic disorders

This review covers the advances made in the last decade utilizing the high-density single-nucleotide microarrays to screen the entire human genome for genetic risk variants and outlines future strategies to draw deeper into the human genetic front. The sequence of the human genome provides the blueprint for life, while its variation provides the spice of life. Approximately 99.5% of the human genome DNA sequence is identical among humans with 0.5% of the genome sequence (15 million bps) accounting for all individual differences including susceptibility for disease. The new technology of the computerized chip array containing up to millions of SNPs as DNA markers makes possible genome-wide association studies to detect genetic predisposition to common polygenic disorders such as coronary artery disease (CAD). The sample sizes required for these studies are massive and large; worldwide consortiums such as CARDIoGRAM have been formed to accommodate this requirement. The progress has been remarkable with the identification of 9p21 followed by several others within the past 2 years. It is expected that most of the common variants (minor allele frequency, MAF >5%) will be identified for CAD within the next 2 to 3 years. Rare variants (MAF <5%) will require direct sequencing which will be delayed somewhat. The ultimate objective for the future is the sequencing and functional analysis of the causative polymorphisms. This will require a new approach involving several disciplines, namely, bioinformatics, high-throughput cell expression, and animal models.

The pursuit of genome-wide association studies: where are we now?

It is now 5 years since the first genome-wide association studies (GWAS), published in 2005, identified a common risk allele with large effect size for age-related macular degeneration in a small sample set. Following this exciting finding, researchers have become optimistic about the prospect of the genome-wide association approach. However, most of the risk alleles identified in the subsequent GWAS for various complex diseases are common with small effect sizes (odds ratio <1.5). So far, more than 450 GWAS have been published and the associations of greater than 2000 single nucleotide polymorphisms (SNPs) or genetic loci were reported. The aim of this review paper is to give an overview of the evolving field of GWAS, discuss the progress that has been made by GWAS and some of the interesting findings, and summarize what we have learned over the past 5 years about the genetic basis of human complex diseases. This review will focus on GWAS of SNPs association for complex diseases but not studies of copy number variations.

ProbABEL package for genome-wide association analysis of imputed data

Background

Over the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account.

Results

We developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations.

Conclusions

ProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.

Genetic variation in the estrogen metabolic pathway and mammographic density as an intermediate phenotype of breast cancer

Introduction

Several studies have examined the effect of genetic variants in genes involved in the estrogen metabolic pathway on mammographic density, but the number of loci studied and the sample sizes evaluated have been small and pathways have not been evaluated comprehensively. In this study, we evaluate the association between mammographic density and genetic variants of the estrogen metabolic pathway.

Methods

A total of 239 SNPs in 34 estrogen metabolic genes were studied in 1,731 Swedish women who participated in a breast cancer case-control study, of which 891 were cases and 840 were controls. Film mammograms of the medio-lateral oblique view were digitalized and the software Cumulus was used for computer-assisted semi-automated thresholding of mammographic density. Generalized linear models controlling for possible confounders were used to evaluate the effects of SNPs on mammographic density. Results found to be nominally significant were examined in two independent populations. The admixture maximum likelihood-based global test was performed to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three subpathways for androgen synthesis, androgen-to-estrogen conversion and estrogen removal.

Results

Genetic variants of genes involved in estrogen metabolism exhibited no appreciable effect on mammographic density. None of the nominally significant findings were validated. In addition, global analyses on the overall estrogen metabolic pathway and its subpathways did not yield statistically significant results.

Conclusions

Overall, there is no conclusive evidence that genetic variants in genes involved in the estrogen metabolic pathway are associated with mammographic density in postmenopausal women.

Pooled analysis of phosphatidylinositol 3-kinase pathway variants and risk of prostate cancer

The phosphatidylinositol 3-kinase (PI3K) pathway regulates various cellular processes, including cellular proliferation and intracellular trafficking, and may affect prostate carcinogenesis. Thus, we explored the association between single-nucleotide polymorphisms (SNP) in PI3K genes and prostate cancer. Pooled data from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium were examined for associations between 89 SNPs in PI3K genes (PIK3C2B, PIK3AP1, PIK3C2A, PIK3CD, and PIK3R3) and prostate cancer risk in 8,309 cases and 9,286 controls. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using logistic regression. SNP rs7556371 in PIK3C2B was significantly associated with prostate cancer risk [OR(per allele), 1.08 (95% CI, 1.03-1.14); P(trend) = 0.0017] after adjustment for multiple testing (P(adj) = 0.024). Simultaneous adjustment of rs7556371 for nearby SNPs strengthened the association [OR(per allele), 1.21 (95% CI, 1.09-1.34); P(trend) = 0.0003]. The adjusted association was stronger for men who were diagnosed before the age of 65 years [OR(per allele), 1.47 (95% CI, 1.20-1.79); P(trend) = 0.0001] or had a family history [OR(per allele) = 1.57 (95% CI, 1.11-2.23); P(trend) = 0.0114], and was strongest in those with both characteristics [OR(per allele) = 2.31 (95% CI, 1.07-5.07), P-interaction = 0.005]. Increased risks were observed among men in the top tertile of circulating insulin-like growth factor-I (IGF-I) levels [OR(per allele) = 1.46 (95% CI, 1.04-2.06); P(trend) = 0.075]. No differences were observed with disease aggressiveness (Gleason grade >or=8 or stage T(3)/T(4) or fatal). In conclusion, we observed a significant association between PIK3C2B and prostate cancer risk, especially for familial, early-onset disease, which may be attributable to IGF-dependent PI3K signaling.

[Advances in genome-wide association study of coronary heart disease]

As a new effective strategy for researching complex diseases, genome-wide association study (GWAS) has being developed rapidly in recent years worldwide. The world genomic study has been taken into a new stage, since a series of disease related genes or variants have been identified by GWAS strategy. Coronary heart disease (CHD) is a complex disease that is caused by both environmental and genetic factors, and has become one of the leading causes of death and disability worldwide. With the application of GWAS strategy, researchers from all over the world have identified many susceptibility loci or regions of CHD that were unable to be identified by candidate gene case-control study. The present paper reviewed the important progresses worldwide attained in GWAS of CHD in recent years. Then it is also expounded the challenges we are facing nowadays in GWAS as well as the future study direction. The information outlined in this paper provides us a valuable guidance upon further exploration into genetic mechanism of CHD.

Voxelwise genome-wide association study (vGWAS)

The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448,293 single nucleotide polymorphisms in each of 31,622 voxels of the entire brain across 740 elderly subjects (mean age+/-s.d.: 75.52+/-6.82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure.

Genome-wide association study of homocysteine levels in Filipinos provides evidence for CPS1 in women and a stronger MTHFR effect in young adults

Plasma homocysteine (Hcy) level is associated with cardiovascular disease and may play an etiologic role in vascular damage, a precursor for atherosclerosis. We performed a genome-wide association study for Hcy in 1786 unrelated Filipino women from the Cebu Longitudinal Health and Nutrition Survey (CLHNS). The most strongly associated single-nucleotide polymorphism (SNP) (rs7422339, P = 4.7 x 10(-13)) encodes Thr1405Asn in the gene CPS1 and explained 3.0% of variation in the Hcy level. The widely studied MTHFR C677T SNP (rs1801133) was also highly significant (P = 8.7 x 10(-10)) and explained 1.6% of the trait variation. We also genotyped these two SNPs in 1679 CLHNS young adult offspring. The MTHFR C677T SNP was strongly associated with Hcy (P = 1.9 x 10(-26)) and explained approximately 5.1% of the variation in the offspring. In contrast, the CPS1 variant was significant only in females (P = 0.11 in all; P = 0.0087 in females). Combined analysis of all samples confirmed that the MTHFR variant was more strongly associated with Hcy in the offspring (interaction P = 1.2 x 10(-5)). Furthermore, although there was evidence for a positive synergistic effect between the CPS1 and MTHFR SNPs in the offspring (interaction P = 0.0046), there was no significant evidence for an interaction in the mothers (P = 0.55). These data confirm a recent finding that CPS1 is a locus influencing Hcy levels in women and suggest that genetic effects on Hcy may differ across developmental stages.