Todd L. Edwards

Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease.

Parkinson disease (PD) is a chronic neurodegenerative disorder with a cumulative prevalence of greater than one per thousand. To date three independent genome‐wide association studies (GWAS) have investigated the genetic susceptibility to PD. These studies implicated several genes as PD risk loci with strong, but not genome‐wide significant, associations.

Genetic variation in eleven phase I drug metabolism genes in an ethnically diverse population

The extent of genetic variation found in drug metabolism genes and its contribution to interindividual variation in response to medication remains incompletely understood. To better determine the identity and frequency of variation in 11 phase I drug metabolism genes, the exons and flanking intronic regions of the cytochrome P450 (CYP) isoenzyme genes CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4 and CYP3A5 were amplified from genomic DNA and sequenced. A total of 60 kb of bi-directional sequence was generated from each of 93 human DNAs, which included Caucasian, African-American and Asian samples. There were 388 different polymorphisms identified. These included 269 non-coding, 45 synonymous and 74 non-synonymous polymorphisms. Of these, 54% were novel and included 176 non-coding, 14 synonymous and 21 non-synonymous polymorphisms. Of the novel variants observed, 85 were represented by single occurrences of the minor allele in the sample set. Much of the variation observed was from low-frequency alleles. Comparatively, these genes are variation-rich. Calculations measuring genetic diversity revealed that while the values for the individual genes are widely variable, the overall nucleotide diversity of 7.7 x 10(-4) and polymorphism parameter of 11.5 x 10(-4) are higher than those previously reported for other gene sets. Several independent measurements indicate that these genes are under selective pressure, particularly for polymorphisms corresponding to non-synonymous amino acid changes. There is relatively little difference in measurements of diversity among the ethnic groups, but there are large differences among the genes and gene subfamilies themselves. Of the three CYP subfamilies involved in phase I drug metabolism (1, 2, and 3), subfamily 2 displays the highest levels of genetic diversity.

Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had P meta<1×10−4, including the gene HLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available.

Patterns of cytokine profiles differ with pregnancy outcome and ethnicity

BACKGROUND Preterm birth (PTB) is hypothesized to be an inflammatory response disease. However, no single factor alone is likely to explain PTB risk. It is more probable that coordinated networks of cytokines affect risk. METHODS Therefore, we examined the relationships between amniotic fluid (AF) cytokines/chemokines and related biomarkers in PTB and normal term deliveries in African Americans and Caucasians. Data were obtained from African American (41 preterm labor and 91 term labor) and Caucasian (105 preterm labor and 100 term labor) pregnant mothers. Pro-inflammatory cytokines and related molecules interleukin (IL)-1, IL-6, IL-8, and tumor necrosis factor- (TNF)-alpha, TNF soluble receptors (sTNFR1 and sTNFR2), and anti-inflammatory cytokine IL-10 that were all previously associated with PTB were studied. Correlations between biomarkers were calculated; differences of correlation coefficients between AF from African American and Caucasian samples in preterm labor and term labor were measured. RESULTS Multiple differences were observed between African American and Caucasian preterm and term birth groups. In term birth the strongest differences were between pro- and anti-inflammatory correlations, whereas in PTB differences were equally distributed between pro-inflammatory/anti-inflammatory and pro-inflammatory/pro-inflammatory correlations. Three correlation patterns differed significantly between AF from PTB African Americans with and without microbial invasion of the intra-amniotic cavity (MIAC); no differences were observed in Caucasians with MIAC. CONCLUSION Correlation analyses of cytokine measurements suggest coordinated interplay during pregnancy; significant differences exist between African Americans and Caucasians. Such analyses can serve as a means of understanding risk factors in these populations.

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction.

BackgroundMultifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.ResultsIn this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad-sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and specific detection was 59.4% versus detection using classification error at 62% and specific detection was 52.2%.ConclusionOf the 10 measures evaluated, the likelihood ratio and normalized mutual information (NMI) are measures that consistently improve the detection and power of MDR in simulated data over using classification error. These measures also reduce the inclusion of spurious variables in a multi-locus model. Thus, MDR, which has already been demonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alternative fitness functions.

HTR1B, ADIPOR1, PPARGC1A, and CYP19A1 and Obesity in a Cohort of Caucasians and African Americans: An Evaluation of Gene-Environment Interactions and Candidate Genes

The World Health Organization estimates that the number of obese and overweight adults has increased to 1.6 billion, with concomitant increases in comorbidity. While genetic factors for obesity have been extensively studied in Caucasians, fewer studies have investigated genetic determinants of body mass index (BMI; weight (kg)/height (m)(2)) in African Americans. A total of 38 genes and 1,086 single nucleotide polymorphisms (SNPs) in African Americans (n = 1,173) and 897 SNPs in Caucasians (n = 1,165) were examined in the Southern Community Cohort Study (2002-2009) for associations with BMI and gene × environment interactions. A statistically significant association with BMI survived correction for multiple testing at rs4140535 (β = -0.04, 95% confidence interval: -0.06, -0.02; P = 5.76 × 10(-5)) in African Americans but not in Caucasians. Gene-environment interactions were observed with cigarette smoking and a SNP in ADIPOR1 in African Americans, as well as between a different SNP in ADIPOR1 and physical activity in Caucasians. A SNP in PPARGC1A interacted with alcohol consumption in African Americans, and a different SNP in PPARGC1A was nominally associated in Caucasians. A SNP in CYP19A1 interacted with dietary energy intake in African Americans, and another SNP in CYP191A had an independent association with BMI in Caucasians.

An association analysis of Alzheimer disease candidate genes detects an ancestral risk haplotype clade in ACE and putative multilocus association between ACE, A2M, and LRRTM3

Alzheimers disease (AD) is the most common form of progressive dementia in the elderly. It is a neurodegenerative disorder characterized by the neuropathologic findings of neurofibrillary tangles and amyloid plaques that accumulate in vulnerable brain regions. AD etiology has been studied by many groups, but since the discovery of the APOE ε4 allele, no further genes have been mapped conclusively to late‐onset AD (LOAD). In this study, we examined genetic association with LOAD susceptibility in 738 Caucasian families (4,704 individuals) and an independent case–control dataset with 296 cases and 566 controls exploring 11 candidate genes (47 SNPs common to both samples). In addition to tests for main effects and haplotypes, the MDR‐PDT was used to search for gene–gene interactions in the family data. We observed significant haplotype effects in ACE in family and case–control samples using standard and cladistic haplotype models. ACE was also part of significant 2 and 3‐locus MDR‐PDT joint effects models with Alpha‐2‐Macroglobulin (A2M), which mediates the clearance of Aβ, and Leucine‐Rich Repeat Transmembrane‐3 (LRRTM3), a nested gene in Alpha‐3 Catenin (CTNNA3) which binds Presenilin‐1. This result did not replicate in the case–control sample, and may not be a true positive. These genes are related to Aβ clearance; thus this constellation of effects might constitute an axis of susceptibility for LOAD. The consistent ACE haplotype result between independent family‐based and unrelated case–control datasets is strong evidence in favor of ACE as a susceptibility locus for AD, and replicates results from several other studies in a large sample.

Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models.

Background/Aims: In genetic studies of complex disease a consideration for the investigator is detection of joint effects. The Multifactor Dimensionality Reduction (MDR) algorithm searches for these effects with an exhaustive approach. Previously unknown aspects of MDR performance were the power to detect interactive effects given large numbers of non-model loci or varying degrees of heterogeneity among multiple epistatic disease models. Methods: To address the performance with many non-model loci, datasets of 500 cases and 500 controls with 100 to 10,000 SNPs were simulated for two-locus models, and one hundred 500-case/500-control datasets with 100 and 500 SNPs were simulated for three-locus models. Multiple levels of locus heterogeneity were simulated in several sample sizes. Results: These results show MDR is robust to locus heterogeneity when the definition of power is not as conservative as in previous simulation studies where all model loci were required to be found by the method. The results also indicate that MDR performance is related more strongly to broad-sense heritability than sample size and is not greatly affected by non-model loci. Conclusions: A study in which a population with high heritability estimates is sampled predisposes the MDR study to success more than a larger ascertainment in a population with smaller estimates.

A rare novel deletion of the tyrosine hydroxylase gene in Parkinson disease

Tyrosine hydroxylase (TH) enzyme is a rate limiting enzyme in dopamine biosynthesis. Missense mutation in both alleles of the TH gene is known to cause dopamine‐related phenotypes, including dystonia and infantile Parkinsonism. However, it is not clear if single allele mutation in TH modifies the susceptibility to the adult form of Parkinson disease (PD). We reported a novel deletion of entire TH gene in an adult with PD. The deletion was first identified by copy number variation (CNV) analysis in a genome‐wide association study using Illumina Infinium BeadChips. After screening 635 cases and 642 controls, the deletion was found in one PD case but not in any control. The deletion was confirmed by multiple quantitative PCR (qPCR) assays. There is no additional exonic single nucleotide variant in the one copy of TH gene of the patient. The patient has an age‐at‐onset of 54 years, no evidence for dystonia, and was responsive to L‐DOPA. This case supports the importance of the TH gene in PD pathogenesis and raises more attention to rare variants in candidate genes being a risk factor for Parkinson disease.

Apoptotic Engulfment Pathway and Schizophrenia

Background Apoptosis has been speculated to be involved in schizophrenia. In a previously study, we reported the association of the MEGF10 gene with the disease. In this study, we followed the apoptotic engulfment pathway involving the MEGF10, GULP1, ABCA1 and ABCA7 genes and tested their association with the disease. Methodology/Principal Findings Ten, eleven and five SNPs were genotyped in the GULP1, ABCA1 and ABCA7 genes respectively for the ISHDSF and ICCSS samples. In all 3 genes, we observed nominally significant associations. Rs2004888 at GULP1 was significant in both ISHDSF and ICCSS samples (p = 0.0083 and 0.0437 respectively). We sought replication in independent samples for this marker and found highly significant association (p = 0.0003) in 3 Caucasian replication samples. But it was not significant in the 2 Chinese replication samples. In addition, we found a significant 2-marker (rs2242436 * rs3858075) interaction between the ABCA1 and ABCA7 genes in the ISHDSF sample (p = 0.0022) and a 3-marker interaction (rs246896 * rs4522565 * rs3858075) amongst the MEGF10, GULP1 and ABCA1 genes in the ICCSS sample (p = 0.0120). Rs3858075 in the ABCA1 gene was involved in both 2- and 3-marker interactions in the two samples. Conclusions/Significance From these data, we concluded that the GULP1 gene and the apoptotic engulfment pathway are involved in schizophrenia in subjects of European ancestry and multiple genes in the pathway may interactively increase the risks to the disease.