Meta-analysis for genome-wide association study identifies multiple variants at the BIN1 locus associated with late-onset Alzheimer's disease.

Recent GWAS studies focused on uncovering novel genetic loci related to AD have revealed associations with variants near CLU, CR1, PICALM and BIN1. In this study, we conducted a genome-wide association study in an independent set of 1034 cases and 1186 controls using the Illumina genotyping platforms. By coupling our data with available GWAS datasets from the ADNI and GenADA, we replicated the original associations in both PICALM (rs3851179) and CR1 (rs3818361). The PICALM variant seems to be non-significant after we adjusted for APOE e4 status. We further tested our top markers in 751 independent cases and 751 matched controls. Besides the markers close to the APOE locus, a marker (rs12989701) upstream of BIN1 locus was replicated and the combined analysis reached genome-wide significance level (p = 5E-08). We combined our data with the published Harold et al. study and meta-analysis with all available 6521 cases and 10360 controls at the BIN1 locus revealed two significant variants (rs12989701, p = 1.32E-10 and rs744373, p = 3.16E-10) in limited linkage disequilibrium (r²â€Š =  0.05) with each other. The independent contribution of both SNPs was supported by haplotype conditional analysis. We also conducted multivariate analysis in canonical pathways and identified a consistent signal in the downstream pathways targeted by Gleevec (P = 0.004 in Pfizer; P = 0.028 in ADNI and P = 0.04 in GenADA). We further tested variants in CLU, PICALM, BIN1 and CR1 for association with disease progression in 597 AD patients where longitudinal cognitive measures are sufficient. Both the PICALM and CLU variants showed nominal significant association with cognitive decline as measured by change in Clinical Dementia Rating-sum of boxes (CDR-SB) score from the baseline but did not pass multiple-test correction. Future experiments will help us better understand potential roles of these genetic loci in AD pathology.

Evaluation of approaches to identify associated SNPs that explain the linkage evidence in nuclear families with affected siblings.

Linkage analysis is often followed by association mapping to localize disease variants. In this paper, we evaluate approaches to determine how much of the observed linkage evidence, namely the identity-by-descent (IBD) sharing at the linkage peak, is explained by associated SNPs. We study several methods: Homozygote Sharing Tests (HST), Genotype Identity-by-Descent Sharing Test (GIST), and a permutation approach. We also propose a new approach, HSTMLB, combining HST and the Maximum Likelihood Binomial (MLB) linkage statistic. These methods can identify SNPs partially explaining the linkage peak, but only HST and HSTMLB can identify SNPs that do not fully explain the linkage evidence and be applied to multiple-SNPs. We contrast these methods with the association tests implemented in the software LAMP. In our simulations, GIST is more powerful at finding SNPs that partially explain the linkage peak, while HST and HSTMLB are equally powerful at identifying SNPs that do not fully explain the linkage peak. When applied to the North American Rheumatoid Arthritis Consortium data, HST and HSTMLB identify marker pairs that may fully explain the linkage peak on chromosome 6. In conclusion, HST and HSTMLB provide simple and flexible tools to identify SNPs that explain the IBD sharing at the linkage peak.

Genome-wide association study for Crohn's disease in the Quebec Founder Population identifies multiple validated disease loci.

Genome-wide association (GWA) studies offer a powerful unbiased method for the identification of multiple susceptibility genes for complex diseases. Here we report the results of a GWA study for Crohn's disease (CD) using family trios from the Quebec Founder Population (QFP). Haplotype-based association analyses identified multiple regions associated with the disease that met the criteria for genome-wide significance, with many containing a gene whose function appears relevant to CD. A proportion of these were replicated in two independent German Caucasian samples, including the established CD loci NOD2 and IBD5. The recently described IL23R locus was also identified and replicated. For this region, multiple individuals with all major haplotypes in the QFP were sequenced and extensive fine mapping performed to identify risk and protective alleles. Several additional loci, including a region on 3p21 containing several plausible candidate genes, a region near JAKMIP1 on 4p16.1, and two larger regions on chromosome 17 were replicated. Together with previously published loci, the spectrum of CD genes identified to date involves biochemical networks that affect epithelial defense mechanisms, innate and adaptive immune response, and the repair or remodeling of tissue.

Mechanistic role of a disease-associated genetic variant within the ADAM33 asthma susceptibility gene.

BACKGROUND: ADAM33 has been identified as an asthma-associated gene in an out-bred population. Genetic studies suggested that the functional role of this metalloprotease was in airway remodeling. However, the mechanistic roles of the disease-associated SNPs have yet to be elucidated especially in the context of the pathophysiology of asthma. One disease-associated SNP, BC+1, which resides in intron BC toward the 5' end of ADAM33, is highly associated with the disease. METHODS: The region surrounding this genetic variant was cloned into a model system to determine if there is a regulatory element within this intron that influences transcription. RESULTS: The BC+1 protective allele did not impose any affect on the transcription of the reporter gene. However, the at-risk allele enforced such a repressive affect on the promoter that no protein product from the reporter gene was detected. These results indicated that there exists within intron BC a regulatory element that acts as a repressor for gene expression. Moreover, since SNP BC+1 is a common genetic variant, this region may interact with other undefined regulatory elements within ADAM33 to provide a rheostat effect, which modulates pre-mRNA processing. Thus, SNP BC+1 may have an important role in the modulation of ADAM33 gene expression. CONCLUSION: These data provide for the first time a functional role for a disease-associated SNP in ADAM33 and begin to shed light on the deregulation of this gene in the pathophysiology of asthma.

Joint modeling of linkage and association using affected sib-pair data.

There has been a growing interest in developing strategies for identifying single-nucleotide polymorphisms (SNPs) that explain a linkage signal by joint modeling of linkage and association. We compare several existing methods and propose a new method called the homozygote sharing transmission-disequilibrium test (HSTDT) to detect linkage and association or to identify SNPs explaining the linkage signal on chromosome 6 for rheumatoid arthritis using 100 replicates of the Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data. Existing methods considered included the family-based tests of association implemented in FBAT, a transmission-disequilibrium test, a conditional logistic regression approach, a likelihood-based approach implemented in LAMP, and the homozygote sharing test (HST). We compared the type I error rates and power for tests classified into three categories according to their null hypotheses: 1) no association in the presence of linkage (i.e., a SNP explains none of the linkage evidence), 2) no linkage adjusting for the association (i.e., a SNP explains all linkage evidence), and 3) no linkage and no association. For testing association in the presence of linkage, we found similar power among all tests except for the homozygote sharing test that had lower power. When testing linkage adjusting for association, similar power was observed between LAMP and HST, but lower power for the conditional logistic regression method. When testing linkage or association, the conditional logistic regression method was more powerful than FBAT.

Genome scan of Han Chinese schizophrenia families from Taiwan: confirmation of linkage to 10q22.3.

OBJECTIVE: Genome-wide linkage analyses of schizophrenia have identified several regions that may harbor schizophrenia susceptibility genes, but given the complex etiology of the disorder, it is unlikely that all susceptibility regions have been detected. To address this issue, the authors ascertained 606 Han Chinese families comprising 1,234 affected members. METHOD: Probands with schizophrenia were recruited from six data collection field research centers in Taiwan. Each proband underwent a diagnostic screen with supplemental medical records and a semistructured interview. Following this screen, the authors administered the Mandarin Chinese version of the Diagnostic Interview for Genetic Studies. Best-estimate final diagnoses were made by two board-certified psychiatrists. The genotyping was conducted by the Center for Inherited Disease Research, with 386 markers spaced at an average of 9-centimorgan (cM) intervals. Empirical simulations were generated to determine genome-wide significance. RESULTS: The authors found five regions with nonparametric linkage z scores 2.0 or greater. These were the following: 2.08 was reached for D1S551 (113.7) cM at 1p31.1 and 2.31 for D2S410 (125.2 cM) at 2q14.1; 2.00 was reached for D4S2361 (93.5 cM) at 4q21.23, and 2.07 for D15S1012 (36 cM) at 15q14, the largest nonparametric linkage z score was 2.88 for D10S2327 (100.92 cM) at 10q22.3. CONCLUSIONS: Our 10q22.3 finding at 100.9 cM is consistent with a previously reported nonparametric linkage score of 4.27 at 107.2 cM on chromosome 10, although it did not attain genome-wide significance in this study.

Early onset bipolar disorder: possible linkage to chromosome 9q34.

OBJECTIVES: Bipolar disorder (BD) is characterized by manic and depressive states that onset at various times in life. Research shows that early onset forms of BD are associated with a stronger genetic loading for the illness. We hypothesized that using age at onset to look at subsets of BD families in a genetic linkage analysis would prove useful in separating etiologically homogeneous BD sub-groups and subsequently identifying genetic susceptibility regions. METHODS: We used the wave-I National Institute of Mental Health (NIMH) Genetics Initiative BD sample, which includes 540 individuals from 97 families with BD, in an ordered-subsets linkage analysis with age at onset of mania as the subset-identifying covariate. This analysis was performed using GENEHUNTER-PLUS followed by the ordered-subsets analysis program. This program generates empirical p-values for the subset with the largest LOD score to determine whether this value was significantly higher than the baseline LOD score using all families. RESULTS: Three chromosomal regions resulted in LOD scores above 2.0: 2.21 (6q25), 3.21 (9q34), and 2.16 (20q11). The largest increase in LOD score was observed on chromosome 9q34 between markers D9S290 and D9S915 in the subset of 58 families that had mania onset before age 20. Families with a minimal mania onset less than 20 years had a significantly greater number of psychiatric comorbidities (p = 0.02) and a marginal increase in depressive symptoms (p = 0.10). CONCLUSIONS: Further investigation into chromosomal region 9q34 is necessary to determine whether this region may harbor a gene specific to families with a minimal age at onset of less than 20.

Identifying SNPs predictive of phenotype using random forests.

There has been a great interest and a few successes in the identification of complex disease susceptibility genes in recent years. Association studies, where a large number of single-nucleotide polymorphisms (SNPs) are typed in a sample of cases and controls to determine which genes are associated with a specific disease, provide a powerful approach for complex disease gene mapping. Genes of interest in those studies may contain large numbers of SNPs that classical statistical methods cannot handle simultaneously without requiring prohibitively large sample sizes. By contrast, high-dimensional nonparametric methods thrive on large numbers of predictors. This work explores the application of one such method, random forests, to the problem of identifying SNPs predictive of the phenotype in the case-control study design. A random forest is a collection of classification trees grown on bootstrap samples of observations, using a random subset of predictors to define the best split at each node. The observations left out of the bootstrap samples are used to estimate prediction error. The importance of a predictor is quantified by the increase in misclassification occurring when the values of the predictor are randomly permuted. We extend the concept of importance to pairs of predictors, to capture joint effects, and we explore the behavior of importance measures over a range of two-locus disease models in the presence of a varying number of SNPs unassociated with the phenotype. We illustrate the application of random forests with a data set of asthma cases and unaffected controls genotyped at 42 SNPs in ADAM33, a previously identified asthma susceptibility gene. SNPs and SNP pairs highly associated with asthma tend to have the highest importance index value, but predictive importance and association do not always coincide.

Identification of polymorphisms explaining a linkage signal: application to the GAW14 simulated data.

We applied three approaches for the identification of polymorphisms explaining the linkage evidence to the Genetic Analysis Workshop 14 simulated data: 1) the genotype-IBD sharing test (GIST); 2) an approach suggested by Horikawa and colleagues; and 3) the homozygote sharing test (HST). These tests were compared with a family-based association test. Two linked regions with highest nonparametric linkage scores were selected to apply these methods. In the first region, Horikawa's method identified the most SNPs within the region containing the disease susceptibility locus, while HST performed best in the second region. However, Horikawa's method also had the most type I errors. These methods show potential as additional tools to complement family-based association tests for the identification of disease susceptibility variants.

Screening large-scale association study data: exploiting interactions using random forests.

BACKGROUND: Genome-wide association studies for complex diseases will produce genotypes on hundreds of thousands of single nucleotide polymorphisms (SNPs). A logical first approach to dealing with massive numbers of SNPs is to use some test to screen the SNPs, retaining only those that meet some criterion for further study. For example, SNPs can be ranked by p-value, and those with the lowest p-values retained. When SNPs have large interaction effects but small marginal effects in a population, they are unlikely to be retained when univariate tests are used for screening. However, model-based screens that pre-specify interactions are impractical for data sets with thousands of SNPs. Random forest analysis is an alternative method that produces a single measure of importance for each predictor variable that takes into account interactions among variables without requiring model specification. Interactions increase the importance for the individual interacting variables, making them more likely to be given high importance relative to other variables. We test the performance of random forests as a screening procedure to identify small numbers of risk-associated SNPs from among large numbers of unassociated SNPs using complex disease models with up to 32 loci, incorporating both genetic heterogeneity and multi-locus interaction. RESULTS: Keeping other factors constant, if risk SNPs interact, the random forest importance measure significantly outperforms the Fisher Exact test as a screening tool. As the number of interacting SNPs increases, the improvement in performance of random forest analysis relative to Fisher Exact test for screening also increases. Random forests perform similarly to the univariate Fisher Exact test as a screening tool when SNPs in the analysis do not interact. CONCLUSIONS: In the context of large-scale genetic association studies where unknown interactions exist among true risk-associated SNPs or SNPs and environmental covariates, screening SNPs using random forest analyses can significantly reduce the number of SNPs that need to be retained for further study compared to standard univariate screening methods.

A novel permutation testing method implicates sixteen nicotinic acetylcholine receptor genes as risk factors for smoking in schizophrenia families.

Smoking is a common correlate of schizophrenia, which leads to medical morbidity. Although twin and adoption studies have consistently implicated genes in the etiology of both smoking and schizophrenia, finding genes has been difficult. Several authors have suggested that clinical or neurobiological features associated with schizophrenia, such as smoking, might improve the ability to detect schizophrenia susceptibility genes by identifying genes related to the etiology of that feature. The objective of this study is to assess evidence for linkage of sixteen nicotinic acetylcholine receptor genes and smoking in schizophrenia families, using data from the NIMH Genetics Initiative for schizophrenia. Sixteen nicotinic acetylcholine receptor genes were selected prior to analysis. We used a multipoint sibling pair linkage analysis program, SIBPAL2, with a smoking trait in schizophrenia families. The significance of the group of candidate genes, in addition to each individual candidate gene, was assessed using permutation testing, which adjusted for multiple comparisons. The group of genes showed significant linkage to the smoking trait after adjusting for multiple comparisons through permutation testing (p = 0.039). In addition, two of the individual candidate genes were significant (CHRNA2, p = 0.044) and (CHRNB2, p = 0.015) and two genes were marginally significant (CHRNA7, p = 0.095; CHRNA1, p = 0.076). The significance of the complex hypothesis, involving sixteen genes, implicates the nicotinic system in smoking for schizophrenic families. Individual gene analysis suggests that CHRNA2 and CHRNB2 may play a particular role in this involvement. Such findings help prioritize genes for future case control studies. In addition, we provide a novel permutation method that is useful in future analyses involving a single hypothesis, with multiple candidate genes.

Evidence for linkage between regulatory enzymes in glycolysis and schizophrenia in a multiplex sample.

Observations of impaired glucose regulation in schizophrenia are long-standing, although their pathological and etiological significance is uncertain. One approach to the issue that minimizes environmental variables (e.g., medication and diet) is to determine whether genes related to glucose regulation show genetic linkage to schizophrenia. We examined the potential role of glucose metabolism in schizophrenia through a genome scan of affection status in schizophrenia and an empirical method for deriving P-values. Data were utilized from the NIMH Genetics Initiative for Schizophrenia dataset, which comprises a total sample consisting of 71 pedigrees containing 218 nuclear families and 987 individuals. A genome scan with 459 markers spaced at an average of 10 cM intervals was conducted using the linkage analysis program Genehunter separately for European- and African-American groups. Enzymes that regulate glycolysis were identified and the genes regulating these enzymes were located through the Online Mendelian Inheritance in Man (OMIM) website. The focus in this study was on genes located near previously reported schizophrenia susceptibility regions. The genome-wide significance of these genes to schizophrenia was assessed using permutation testing. When results were adjusted for multiple testing within and across ethnic groups, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 (PFKFB2; chromosome 1q32.2) achieved genome-wide significance (P = 0.04). In addition, hexokinase 3 (HK3; chromosome 5q35.3) was also suggestive of linkage (P = 0.09). For the European-American sample, PFKFB2 (1q32.2), hexokinase 3 (HK3; 5q35.3), and pyruvate kinase 3 (PK3; chromosome 15q23) achieved significance at the 0.05 level. None of the genes showed significance in the African-American sample. Our results provide further support for the view that genes that regulate glucose metabolism may also influence susceptibility to schizophrenia. More generally, they support the view that relationships between glucose dysregulation and schizophrenia are inherent to the disorder, and are not merely epiphenomena related to medication or other treatment factors.

Mapping complex traits using Random Forests.

Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels.

Genome scan of three quantitative traits in schizophrenia pedigrees.

BACKGROUND: Twin and adoption studies have consistently implicated genes in the etiology of schizophrenia. Molecular genetic studies have found some consistent support for linkage to many regions of the genome. Despite these encouraging results, none of these findings have achieved genome-wide levels of statistical significance, and none have been consistently replicated. METHODS: This report is a follow-up of a genome scan that analyzed linkage to the diagnosis of schizophrenia in a series of sibling pairs in the National Institute of Mental Health Genetics Initiative for Schizophrenia data. In this report, we use the same sample to assess linkage to three quantitative traits developed from the Scale for the Assessment of Negative Symptoms and the Scale for the Assessment of Positive Symptoms: positive, negative, and disorganized symptoms. RESULTS: We show suggestive linkage to chromosomes 6, 9, and 20 for the disorganized trait and to chromosome 12 for the negative trait. We also show weak association with PAH (phenylalanine hydroxylase) on that chromosome. CONCLUSIONS: The findings on chromosome 6 replicate some prior findings, the other loci are novel. A larger sample would provide more power to detect both linkage and association for this complex disorder.

Association of the ADAM33 gene with asthma and bronchial hyperresponsiveness.

Asthma is a common respiratory disorder characterized by recurrent episodes of coughing, wheezing and breathlessness. Although environmental factors such as allergen exposure are risk factors in the development of asthma, both twin and family studies point to a strong genetic component. To date, linkage studies have identified more than a dozen genomic regions linked to asthma. In this study, we performed a genome-wide scan on 460 Caucasian families and identified a locus on chromosome 20p13 that was linked to asthma (log(10) of the likelihood ratio (LOD), 2.94) and bronchial hyperresponsiveness (LOD, 3.93). A survey of 135 polymorphisms in 23 genes identified the ADAM33 gene as being significantly associated with asthma using case-control, transmission disequilibrium and haplotype analyses (P = 0.04 0.000003). ADAM proteins are membrane-anchored metalloproteases with diverse functions, which include the shedding of cell-surface proteins such as cytokines and cytokine receptors. The identification and characterization of ADAM33, a putative asthma susceptibility gene identified by positional cloning in an outbred population, should provide insights into the pathogenesis and natural history of this common disease.

A mutation in the LDL receptor-related protein 5 gene results in the autosomal dominant high-bone-mass trait.

Osteoporosis is a complex disease that affects >10 million people in the United States and results in 1.5 million fractures annually. In addition, the high prevalence of osteopenia (low bone mass) in the general population places a large number of people at risk for developing the disease. In an effort to identify genetic factors influencing bone density, we characterized a family that includes individuals who possess exceptionally dense bones but are otherwise phenotypically normal. This high-bone-mass trait (HBM) was originally localized by linkage analysis to chromosome 11q12-13. We refined the interval by extending the pedigree and genotyping additional markers. A systematic search for mutations that segregated with the HBM phenotype uncovered an amino acid change, in a predicted beta-propeller module of the low-density lipoprotein receptor-related protein 5 (LRP5), that results in the HBM phenotype. During analysis of >1,000 individuals, this mutation was observed only in affected individuals from the HBM kindred. By use of in situ hybridization to rat tibia, expression of LRP5 was detected in areas of bone involved in remodeling. Our findings suggest that the HBM mutation confers a unique osteogenic activity in bone remodeling, and this understanding may facilitate the development of novel therapies for the treatment of osteoporosis.