Heterogeneity in familial clustering of metabolic syndrome components in the multiethnic GENNID study.

OBJECTIVE: Metabolic syndrome (MetS) is defined by clustering of cardiometabolic components, which may be present in different combinations. The authors evaluated clustering in individuals and extended families within and across ancestry groups. METHODS: The prevalence of different combinations of MetS components (high fasting glucose, low high-density lipoprotein cholesterol, high triglycerides, high blood pressure, and abdominal obesity) was estimated in 1651 individuals (340 families) self-reporting as European American (EA), Hispanic/Mexican American (MA), African American (AA), and Japanese American (JA). Odds ratios were estimated using logistic regression with generalized estimating equations comparing individual MetS components, number, and combinations of components for each ancestry group versus EA. RESULTS: Clustering of all five components (Combination #16) was more prevalent in EA (29.9%) and MA (25.2%) individuals than in AA (18.7%) and JA (15.5%) individuals. Compared with EA individuals, AA individuals were 64% and 66% less likely to have high triglycerides and low high-density lipoprotein cholesterol, whereas JA individuals were 85% and 56% less likely to have abdominal obesity and high blood pressure, respectively. Compared with EA individuals, the odds of having two, four, or five components were at least 77% lower in JA individuals, whereas the odds of having three, four, or five components were at least 3.79 times greater in MA individuals. CONCLUSIONS: Understanding heterogeneity in MetS clustering may identify factors important in reducing health disparities.

Genome-wide association analysis of metabolic syndrome quantitative traits in the GENNID multiethnic family study.

BACKGROUND: To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups. METHODS: Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina's Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping. RESULTS: Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects. CONCLUSIONS: This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS.

Evidence for gene-smoking interactions for hearing loss and deafness in Japanese American families.

BACKGROUND: This study investigated the relationship between smoking and hearing loss and deafness (HLD) and whether the relationship is modified by genetic variation. Data for these analyses was from the subset of Japanese American families collected as part of the American Diabetes Association Genetics of Non-insulin Dependent Diabetes Mellitus study. Logistic regression with generalized estimating equations assessed the relationship between HLD and smoking. Nonparametric linkage analysis identified genetic regions harboring HLD susceptibility genes and ordered subset analysis was used to identify regions showing evidence for gene-smoking interactions. Genetic variants within these candidate regions were then each tested for interaction with smoking using logistic regression models. RESULTS: After adjusting for age, sex, diabetes status and smoking duration, for each pack of cigarettes smoked per day, risk of HLD increased 4.58 times (odds ratio (OR) = 4.58; 95% Confidence Interval (CI): (1.40,15.03)), and ever smokers were over 5 times more likely than nonsmokers to report HLD (OR = 5.22; 95% CI: (1.24, 22.03)). Suggestive evidence for linkage for HLD was observed in multiple genomic regions (Chromosomes 5p15, 8p23 and 17q21), and additional suggestive regions were identified when considering interactions with smoking status (Chromosomes 7p21, 11q23, 12q32, 15q26, and 20q13) and packs-per-day (Chromosome 8q21). CONCLUSIONS: To our knowledge this was the first report of possible gene-by-smoking interactions in HLD using family data. Additional work, including independent replication, is needed to understand the basis of these findings. HLD are important public health issues and understanding the contributions of genetic and environmental factors may inform public health messages and policies.

Transethnic meta-analysis of metabolic syndrome in a multiethnic study.

Genome-wide association studies (GWAS) have been used to establish thousands of genetic associations across numerous phenotypes. To improve the power of GWAS and generalize associations across ethnic groups, transethnic meta-analysis methods are used to combine the results of several GWAS from diverse ancestries. The goal of this study is to identify genetic associations for eight quantitative metabolic syndrome (MetS) traits through a meta-analysis across four ethnic groups. Traits were measured in the GENetics of Noninsulin dependent Diabetes Mellitus (GENNID) Study which consists of African-American (families = 73, individuals = 288), European-American (families = 79, individuals = 519), Japanese-American (families = 17, individuals = 132), and Mexican-American (families = 113, individuals = 610) samples. Genome-wide association results from these four ethnic groups were combined using four meta-analysis methods: fixed effects, random effects, TransMeta, and MR-MEGA. We provide an empirical comparison of the four meta-analysis methods from the GENNID results, discuss which types of loci (characterized by allelic heterogeneity) appear to be better detected by each of the four meta-analysis methods in the GENNID Study, and validate our results using previous genetic discoveries. We specifically compare the two transethnic methods, TransMeta and MR-MEGA, and discuss how each transethnic method's framework relates to the types of loci best detected by each method.

APOE, MAPT, and SNCA genes and cognitive performance in Parkinson disease.

IMPORTANCE: Cognitive impairment is a common and disabling problem in Parkinson disease (PD) that is not well understood and is difficult to treat. Identification of genetic variants that influence the rate of cognitive decline or pattern of early cognitive deficits in PD might provide a clearer understanding of the etiopathogenesis of this important nonmotor feature. OBJECTIVE: To determine whether common variation in the APOE, MAPT, and SNCA genes is associated with cognitive performance in patients with PD. DESIGN, SETTING, AND PARTICIPANTS: We studied 1079 PD patients from 6 academic centers in the United States who underwent assessments of memory (Hopkins Verbal Learning Test-Revised [HVLT-R]), attention and executive function (Letter-Number Sequencing Test and Trail Making Test), language processing (semantic and phonemic verbal fluency tests), visuospatial skills (Benton Judgment of Line Orientation test), and global cognitive function (Montreal Cognitive Assessment). Participants underwent genotyping for the APOE ε2/ε3/ε4 alleles, MAPT H1/H2 haplotypes, and SNCA rs356219. We used linear regression to test for association between genotype and baseline cognitive performance with adjustment for age, sex, years of education, disease duration, and site. We used a Bonferroni correction to adjust for the 9 comparisons that were performed for each gene. MAIN OUTCOMES AND MEASURES: Nine variables derived from 7 psychometric tests. RESULTS: The APOE ε4 allele was associated with lower performance on the HVLT-R Total Recall (P = 6.7 × 10(-6); corrected P [Pc] = 6.0 × 10(-5)), Delayed Recall (P = .001; Pc = .009), and Recognition Discrimination Index (P = .004; Pc = .04); a semantic verbal fluency test (P = .002; Pc = .02); the Letter-Number Sequencing Test (P = 1 × 10(-5); Pc = 9 × 10(-5)); and Trail Making Test B minus Trail Making Test A (P = .002; Pc = .02). In a subset of 645 patients without dementia, the APOE ε4 allele was associated with lower scores on the HVLT-R Total Recall (P = .005; Pc = .045) and the semantic verbal fluency (P = .005; Pc = .045) measures. Variants of MAPT and SNCA were not associated with scores on any tests. CONCLUSIONS AND RELEVANCE: Our data indicate that the APOE ε4 allele is an important predictor of cognitive function in PD across multiple domains. Among PD patients without dementia, the APOE ε4 allele was only associated with lower performance on word list learning and semantic verbal fluency, a pattern more typical of the cognitive deficits seen in early Alzheimer disease than PD.

Evaluation of mild cognitive impairment subtypes in Parkinson's disease.

Mild cognitive impairment in Parkinson's disease (PD-MCI) is common and increases the risk for dementia. Establishing distinct PD-MCI cognitive subtypes could be valuable for eventually predicting those most likely to convert to dementia. However, the study of PD-MCI subtypes has not yielded consistent results among cohorts. To determine whether there are distinct cognitive subtypes among participants diagnosed with PD-MCI in the Pacific Northwest Udall Center Clinical Consortium, we cognitively subtyped 95 patients with PD-MCI, using the Movement Disorders Society Task Force diagnostic guidelines. Psychometric test scores were then subjected to principle components factor analysis to determine whether similar cognitive subgroups could be identified using statistical methodology. Multiple-domain PD-MCI was diagnosed in 95% of the sample, and a range of cognitive impairments were noted. Factor analysis yielded seven factors and demonstrated overlap of phonemic verbal fluency on two factors, as well as the loading of verbal fluency on the same factor as a visuospatial measure; however, these factors did not partition the sample into distinct cognitive subtypes. Separation of cognitive subtypes based on the current PD-MCI criteria, or via statistical methods, may not provide sufficient information to describe distinct PD groups. Future efforts to validate the PD-MCI criteria and identify combinations of genetic or other risk factors for cognitive impairment are warranted.

Association mapping of the PARK10 region for Parkinson's disease susceptibility genes.

BACKGROUND: Previous studies indicate that as many as six genes within the PARK10 region (RNF11, UQCRH, HIVEP3, EIF2B3, USP24, ELAVL4) might modify susceptibility or age at onset in Parkinson's disease (PD). METHODS: We sought to identify new PD susceptibility genes and to validate previously nominated candidate genes within the PARK10 region using a two-stage design. We used data from a large, publicly-available genome-wide association study (GWAS) in the discovery stage (n = 2000 cases and 1986 controls) and data from three independent studies for the replication stage (total n = 2113 cases and 2095 controls). Marker density was increased by imputation using HapMap 3 and 1000 Genomes reference panels, and over 40,000 single nucleotide polymorphisms (SNPs) were used in the final analysis. The association between each SNP and PD was modeled using logistic regression with an additive allele dosage effect and adjusted for sex, age, and axes of geographical variation. RESULTS: Although the discovery stage yielded promising findings for SNPs in several novel genes, including DAB1, none of the results were validated in the replication stage. Furthermore, in meta-analyses across all datasets no genes within PARK10 reached significance after accounting for multiple testing. CONCLUSION: Our results suggest that common variation in the PARK10 region is not associated with PD risk. However, additional studies are needed to assess the role of PARK10 in modifying age at onset and to determine whether rare variants in this region might affect PD susceptibility.

APOE ε4 increases risk for dementia in pure synucleinopathies.

OBJECTIVE: To test for an association between the apolipoprotein E (APOE) ϵ4 allele and dementias with synucleinopathy. DESIGN: Genetic case-control association study. SETTING: Academic research. PATIENTS: Autopsied subjects were classified into 5 categories: dementia with high-level Alzheimer disease (AD) neuropathologic changes (NCs) but without Lewy body disease (LBD) NCs (AD group; n=244), dementia with LBDNCs and high-level ADNCs (LBD-AD group; n=224), dementia with LBDNCs and no or low levels of ADNCs (pure DLB [pDLB] group; n=91), Parkinson disease dementia (PDD) with no or low levels of ADNCs (n=81), and control group (n=269). MAIN OUTCOME MEASURE The APOE allele frequencies. RESULTS The APOE ϵ4 allele frequency was significantly higher in the AD (38.1%), LBD-AD (40.6%), pDLB (31.9%), and PDD (19.1%) groups compared with the control group (7.2%; overall χ(2)(4)=185.25; P=5.56 × 10(-39)), and it was higher in the pDLB group than the PDD group (P= .01). In an age-adjusted and sex-adjusted dominant model, ϵ4 was strongly associated with AD (odds ratio, 9.9; 95% CI, 6.4-15.3), LBD-AD (odds ratio, 12.6; 95% CI, 8.1-19.8), pDLB (odds ratio, 6.1; 95% CI, 3.5-10.5), and PDD (odds ratio, 3.1; 95% CI, 1.7-5.6). CONCLUSIONS The APOE ϵ4 allele is a strong risk factor across the LBD spectrum and occurs at an increased frequency in pDLB relative to PDD. This suggests that ϵ4 increases the likelihood of presenting with dementia in the context of a pure synucleinopathy. The elevated ϵ4 frequency in the pDLB and PDD groups, in which the overall brain neuritic plaque burden was low, indicates that apoE might contribute to neurodegeneration through mechanisms unrelated to amyloid processing.

Risk prediction for complex diseases: application to Parkinson disease.

PURPOSE: The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease. METHODS: A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve. RESULTS: Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone. CONCLUSION: Adding genome-wide association study-derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.Genet Med 2013:15(5):361-367.

Common variation in the LRRK2 gene is a risk factor for Parkinson's disease.

BACKGROUND: Common variants in the LRRK2 gene influence the risk of Parkinson's disease (PD) in Asians, but whether the same is true in European-derived populations is less clear. METHODS: We genotyped 66 LRRK2 tagging single-nucleotide polymorphisms (SNPs) in 575 PD patients and 689 controls from the northwestern United States (tier 1). PD-associated SNPs (P < .05) were then genotyped in an independent sample of 3617 cases and 2512 controls from the United States and Spain (tier 2). Logistic regression was used to model additive SNP genotype effects adjusted for age and sex among white individuals. RESULTS: Two regions showed independent association with PD in tier 1, and SNPs in both regions were successfully replicated in tier 2 (rs10878226, combined odds ratio [OR], 1.20; 95% confidence interval [CI], 1.08-1.33; P = 6.3 × 10(-4); rs11176013, OR, 0.89; CI, 0.83-0.95; P = 4.6 × 10(-4)). CONCLUSIONS: Our data suggest that common variation within LRRK2 conveys susceptibility for PD in individuals of European ancestry.

GBA mutations increase risk for Lewy body disease with and without Alzheimer disease pathology.

OBJECTIVES: Mutations in the GBA gene occur in 7% of patients with Parkinson disease (PD) and are a well-established susceptibility factor for PD, which is characterized by Lewy body disease (LBD) neuropathologic changes (LBDNCs). We sought to determine whether GBA influences risk of dementia with LBDNCs, Alzheimer disease (AD) neuropathologic changes (ADNCs), or both. METHODS: We screened the entire GBA coding region for mutations in controls and in subjects with dementia and LBDNCs and no or low levels of ADNCs (pure dementia with Lewy bodies [pDLB]), LBDNCs and high-level ADNCs (LBD-AD), and high-level ADNCs but without LBDNCs (AD). RESULTS: Among white subjects, pathogenic GBA mutations were identified in 6 of 79 pDLB cases (7.6%), 8 of 222 LBD-AD cases (3.6%), 2 of 243 AD cases (0.8%), and 3 of 381 controls (0.8%). Subjects with pDLB and LBD-AD were more likely to carry mutations than controls (pDLB: odds ratio [OR] = 7.6; 95% confidence interval [CI] = 1.8-31.9; p = 0.006; LBD-AD: OR = 4.6; CI = 1.2-17.6; p = 0.025), but there was no significant difference in frequencies between the AD and control groups (OR = 1.1; CI = 0.2-6.6; p = 0.92). There was a highly significant trend test across groups (χ(2)(1) = 19.3; p = 1.1 × 10(-5)), with the likelihood of carrying a GBA mutation increasing in the following direction: control/AD < LBD-AD < pDLB. CONCLUSIONS: GBA is a susceptibility gene across the LBD spectrum, but not in AD, and appears to convey a higher risk for PD and pDLB than for LBD-AD. PD and pDLB might be more similar to one another in genetic determinants and pathophysiology than either disease is to LBD-AD.

Multivariate linkage scan for metabolic syndrome traits in families with type 2 diabetes.

The purpose of this study was to evaluate evidence for linkage to interrelated quantitative features of the metabolic syndrome (MetS). Data on eight quantitative MetS traits (body weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein (HDL) cholesterol, triglycerides (TGs), and fasting glucose and insulin measurements) and a 10 cM genome scan were available for 78 white families (n = 532 subjects). These data were used to conduct multipoint, multivariate linkage analyses, including tests for coincident linkage and complete pleiotropy. The strongest evidence for linkage from the bivariate analyses was observed on chromosome 1 (1p22.2) (HDL-TG; univariate lod score equivalent (lod(eq) = 3.99)) with stronger results from the trivariate analysis at the same location (HDL-TG-Insulin; lod(eq) = 4.32). Seven additional susceptibility regions (lod(eq) scores >1.9) were observed (1p36, 1q23, 2q21.2, 8q23.3, 14q23.2, 14q32.11, and 20p11.21). The results from this study indicate that several correlated traits of the MetS are influenced by the same gene(s) that account for some of the clustering of the MetS features.