John S K Kauwe

Bioinformatics, Genetics, Neuroscience

PhD Evolution, Ecology, Population Biology
41.88

Publications

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    ABSTRACT: Observational research shows that higher body mass index (BMI) increases Alzheimer's disease (AD) risk, but it is unclear whether this association is causal. We applied genetic variants that predict BMI in Mendelian randomization analyses, an approach that is not biased by reverse causation or confounding, to evaluate whether higher BMI increases AD risk. We evaluated individual-level data from the AD Genetics Consortium (ADGC: 10,079 AD cases and 9613 controls), the Health and Retirement Study (HRS: 8403 participants with algorithm-predicted dementia status), and published associations from the Genetic and Environmental Risk for AD consortium (GERAD1: 3177 AD cases and 7277 controls). No evidence from individual single-nucleotide polymorphisms or polygenic scores indicated BMI increased AD risk. Mendelian randomization effect estimates per BMI point (95% confidence intervals) were as follows: ADGC, odds ratio (OR) = 0.95 (0.90-1.01); HRS, OR = 1.00 (0.75-1.32); GERAD1, OR = 0.96 (0.87-1.07). One subscore (cellular processes not otherwise specified) unexpectedly predicted lower AD risk. Copyright © 2015 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 06/2015; DOI:10.1016/j.jalz.2015.05.015 · 17.47 Impact Factor
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    ABSTRACT: Potentially modifiable risk factors including obesity, diabetes, hypertension, and smoking are associated with Alzheimer disease (AD) and represent promising targets for intervention. However, the causality of these associations is unclear. We sought to assess the causal nature of these associations using Mendelian randomization (MR). We used SNPs associated with each risk factor as instrumental variables in MR analyses. We considered type 2 diabetes (T2D, NSNPs = 49), fasting glucose (NSNPs = 36), insulin resistance (NSNPs = 10), body mass index (BMI, NSNPs = 32), total cholesterol (NSNPs = 73), HDL-cholesterol (NSNPs = 71), LDL-cholesterol (NSNPs = 57), triglycerides (NSNPs = 39), systolic blood pressure (SBP, NSNPs = 24), smoking initiation (NSNPs = 1), smoking quantity (NSNPs = 3), university completion (NSNPs = 2), and years of education (NSNPs = 1). We calculated MR estimates of associations between each exposure and AD risk using an inverse-variance weighted approach, with summary statistics of SNP-AD associations from the International Genomics of Alzheimer's Project, comprising a total of 17,008 individuals with AD and 37,154 cognitively normal elderly controls. We found that genetically predicted higher SBP was associated with lower AD risk (odds ratio [OR] per standard deviation [15.4 mm Hg] of SBP [95% CI]: 0.75 [0.62-0.91]; p = 3.4 × 10-3). Genetically predicted higher SBP was also associated with a higher probability of taking antihypertensive medication (p = 6.7 × 10-8). Genetically predicted smoking quantity was associated with lower AD risk (OR per ten cigarettes per day [95% CI]: 0.67 [0.51-0.89]; p = 6.5 × 10-3), although we were unable to stratify by smoking history; genetically predicted smoking initiation was not associated with AD risk (OR = 0.70 [0.37, 1.33]; p = 0.28). We saw no evidence of causal associations between glycemic traits, T2D, BMI, or educational attainment and risk of AD (all p > 0.1). Potential limitations of this study include the small proportion of intermediate trait variance explained by genetic variants and other implicit limitations of MR analyses. Inherited lifetime exposure to higher SBP is associated with lower AD risk. These findings suggest that higher blood pressure-or some environmental exposure associated with higher blood pressure, such as use of antihypertensive medications-may reduce AD risk.
    PLoS Medicine 06/2015; 12(6):e1001841. DOI:10.1371/journal.pmed.1001841 · 14.00 Impact Factor
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    Mark T. W. Ebbert, Perry G. Ridge, John S. K. Kauwe
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    ABSTRACT: Alzheimer’s disease affects millions of people worldwide and incidence is expected to rise as the population ages, but no effective therapies exist despite decades of research and more than 20 known disease markers. Research has shown that Alzheimer’s disease’s missing heritability remains extensive with an estimated 25% of phenotypic variance unexplained by known variants. The missing heritability may be explained by missing variants or by epistasis. Researchers often focus on individual loci rather than epistatic interactions, which is likely an oversimplification of the underlying biology since most phenotypes are affected by multiple genes. Focusing research efforts on epistasis will be critical to resolving Alzheimer’s disease etiology, and a major key to identifying and properly interpreting key epistatic interactions will be bridging the gap between statistical and biological epistasis. This review covers the current state of epistasis research in Alzheimer’s disease and how researchers can bridge the gap between statistical and biological epistasis to help resolve Alzheimer’s disease etiology.
    05/2015; 2015:1-7. DOI:10.1155/2015/870123
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    ABSTRACT: APOE ɛ4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer's Project (IGAP) Consortium in APOE ɛ4+ (10 352 cases and 9207 controls) and APOE ɛ4- (7184 cases and 26 968 controls) subgroups as well as in the total sample testing for interaction between a single-nucleotide polymorphism (SNP) and APOE ɛ4 status. Suggestive associations (P<1 × 10(-4)) in stage 1 were evaluated in an independent sample (stage 2) containing 4203 subjects (APOE ɛ4+: 1250 cases and 536 controls; APOE ɛ4-: 718 cases and 1699 controls). Among APOE ɛ4- subjects, novel genome-wide significant (GWS) association was observed with 17 SNPs (all between KANSL1 and LRRC37A on chromosome 17 near MAPT) in a meta-analysis of the stage 1 and stage 2 data sets (best SNP, rs2732703, P=5·8 × 10(-9)). Conditional analysis revealed that rs2732703 accounted for association signals in the entire 100-kilobase region that includes MAPT. Except for previously identified AD loci showing stronger association in APOE ɛ4+ subjects (CR1 and CLU) or APOE ɛ4- subjects (MS4A6A/MS4A4A/MS4A6E), no other SNPs were significantly associated with AD in a specific APOE genotype subgroup. In addition, the finding in the stage 1 sample that AD risk is significantly influenced by the interaction of APOE with rs1595014 in TMEM106B (P=1·6 × 10(-7)) is noteworthy, because TMEM106B variants have previously been associated with risk of frontotemporal dementia. Expression quantitative trait locus analysis revealed that rs113986870, one of the GWS SNPs near rs2732703, is significantly associated with four KANSL1 probes that target transcription of the first translated exon and an untranslated exon in hippocampus (P⩽1.3 × 10(-8)), frontal cortex (P⩽1.3 × 10(-9)) and temporal cortex (P⩽1.2 × 10(-11)). Rs113986870 is also strongly associated with a MAPT probe that targets transcription of alternatively spliced exon 3 in frontal cortex (P=9.2 × 10(-6)) and temporal cortex (P=2.6 × 10(-6)). Our APOE-stratified GWAS is the first to show GWS association for AD with SNPs in the chromosome 17q21.31 region. Replication of this finding in independent samples is needed to verify that SNPs in this region have significantly stronger effects on AD risk in persons lacking APOE ɛ4 compared with persons carrying this allele, and if this is found to hold, further examination of this region and studies aimed at deciphering the mechanism(s) are warranted.Molecular Psychiatry advance online publication, 17 March 2015; doi:10.1038/mp.2015.23.
    Molecular Psychiatry 03/2015; DOI:10.1038/mp.2015.23 · 15.15 Impact Factor
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    ABSTRACT: Recently, a rare variant in the amyloid precursor protein gene (APP) was described in a population from Iceland. This variant, in which alanine is replaced by threonine at position 673 (A673T), appears to protect against late-onset Alzheimer disease (AD). We evaluated the frequency of this variant in AD cases and cognitively normal controls to determine whether this variant will significantly contribute to risk assessment in individuals in the United States. To determine the frequency of the APP A673T variant in a large group of elderly cognitively normal controls and AD cases from the United States and in 2 case-control cohorts from Sweden. Case-control association analysis of variant APP A673T in US and Swedish white individuals comparing AD cases with cognitively intact elderly controls. Participants were ascertained at multiple university-associated medical centers and clinics across the United States and Sweden by study-specific sampling methods. They were from case-control studies, community-based prospective cohort studies, and studies that ascertained multiplex families from multiple sources. Genotypes for the APP A673T variant were determined using the Infinium HumanExome V1 Beadchip (Illumina, Inc) and by TaqMan genotyping (Life Technologies). The A673T variant genotypes were evaluated in 8943 US AD cases, 10 480 US cognitively normal controls, 862 Swedish AD cases, and 707 Swedish cognitively normal controls. We identified 3 US individuals heterozygous for A673T, including 1 AD case (age at onset, 89 years) and 2 controls (age at last examination, 82 and 77 years). The remaining US samples were homozygous for the alanine (A673) allele. In the Swedish samples, 3 controls were heterozygous for A673T and all AD cases were homozygous for the A673 allele. We also genotyped a US family previously reported to harbor the A673T variant and found a mother-daughter pair, both cognitively normal at ages 72 and 84 years, respectively, who were both heterozygous for A673T; however, all individuals with AD in the family were homozygous for A673. The A673T variant is extremely rare in US cohorts and does not play a substantial role in risk for AD in this population. This variant may be primarily restricted to Icelandic and Scandinavian populations.
    JAMA Neurology 12/2014; DOI:10.1001/jamaneurol.2014.2157 · 7.01 Impact Factor
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    ABSTRACT: Background: We sought to identify optimal approaches by calibrating longitudinal cognitive performance across studies with different neuropsychological batteries. Methods: We examined four approaches to calibrate cognitive performance in nine longitudinal studies of Alzheimer's disease (AD) (n = 10,875): (1) common test, (2) standardize and average available tests, (3) confirmatory factor analysis (CFA) with continuous indicators, and (4) CFA with categorical indicators. To compare precision, we determined the minimum sample sizes needed to detect 25% cognitive decline with 80% power. To compare criterion validity, we correlated cognitive change from each approach with 6-year changes in average cortical thickness and hippocampal volume using available MRI data from the AD Neuroimaging Initiative. Results: CFA with categorical indicators required the smallest sample size to detect 25% cognitive decline with 80% power (n = 232) compared to common test (n = 277), standardize-and-average (n = 291), and CFA with continuous indicators (n = 315) approaches. Associations with changes in biomarkers changes were the strongest for CFA with categorical indicators. Conclusions: CFA with categorical indicators demonstrated greater power to detect change and superior criterion validity compared to other approaches. It has wide applicability to directly compare cognitive performance across studies, making it a good way to obtain operational phenotypes for genetic analyses of cognitive decline among people with AD. © 2014 S. Karger AG, Basel.
    Neuroepidemiology 11/2014; 43(3-4):194-205. DOI:10.1159/000367970 · 2.48 Impact Factor
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    ABSTRACT: Cerebrospinal fluid (CSF) 42 amino acid species of amyloid beta (Aβ42) and tau levels are strongly correlated with the presence of Alzheimer's disease (AD) neuropathology including amyloid plaques and neurodegeneration and have been successfully used as endophenotypes for genetic studies of AD. Additional CSF analytes may also serve as useful endophenotypes that capture other aspects of AD pathophysiology. Here we have conducted a genome-wide association study of CSF levels of 59 AD-related analytes. All analytes were measured using the Rules Based Medicine Human DiscoveryMAP Panel, which includes analytes relevant to several disease-related processes. Data from two independently collected and measured datasets, the Knight Alzheimer's Disease Research Center (ADRC) and Alzheimer's Disease Neuroimaging Initiative (ADNI), were analyzed separately, and combined results were obtained using meta-analysis. We identified genetic associations with CSF levels of 5 proteins (Angiotensin-converting enzyme (ACE), Chemokine (C-C motif) ligand 2 (CCL2), Chemokine (C-C motif) ligand 4 (CCL4), Interleukin 6 receptor (IL6R) and Matrix metalloproteinase-3 (MMP3)) with study-wide significant p-values (p<1.46×10-10) and significant, consistent evidence for association in both the Knight ADRC and the ADNI samples. These proteins are involved in amyloid processing and pro-inflammatory signaling. SNPs associated with ACE, IL6R and MMP3 protein levels are located within the coding regions of the corresponding structural gene. The SNPs associated with CSF levels of CCL4 and CCL2 are located in known chemokine binding proteins. The genetic associations reported here are novel and suggest mechanisms for genetic control of CSF and plasma levels of these disease-related proteins. Significant SNPs in ACE and MMP3 also showed association with AD risk. Our findings suggest that these proteins/pathways may be valuable therapeutic targets for AD. Robust associations in cognitively normal individuals suggest that these SNPs also influence regulation of these proteins more generally and may therefore be relevant to other diseases.
    PLoS Genetics 10/2014; 10(10):e1004758. DOI:10.1371/journal.pgen.1004758 · 8.17 Impact Factor
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    ABSTRACT: Because APOE locus variants contribute to risk of late-onset Alzheimer disease (LOAD) and to differences in age at onset (AAO), it is important to know whether other established LOAD risk loci also affect AAO in affected participants.
    JAMA Neurology 09/2014; DOI:10.1001/jamaneurol.2014.1491 · 7.01 Impact Factor
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    ABSTRACT: Cholesterol has been implicated in the pathogenesis of late-onset Alzheimer's disease (LOAD) and the cholesteryl ester transfer protein (CETP) is critical to cholesterol regulation within the cell, making CETP an Alzheimer's disease candidate gene. Several studies have suggested that CETP I405V (rs5882) is associated with cognitive function and LOAD risk, but findings vary and most studies have been conducted using relatively small numbers of samples. To test whether this variant is involved in cognitive function and LOAD progression, we genotyped 4486 subjects with up to 12 years of longitudinal cognitive assessment. Analyses revealed an average 0.6-point decrease per year in the rate of cognitive decline for each additional valine (p < 0.011). We failed to detect the association between CETP I405V and LOAD status (p < 0.28). We conclude that CETP I405V is associated with preserved cognition over time but is not associated with LOAD status.
    Neurobiology of Aging 08/2014; 36(1). DOI:10.1016/j.neurobiolaging.2014.08.022 · 4.85 Impact Factor
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    ABSTRACT: Introduction MAPT encodes for tau, the predominant component of neurofibrillary tangles that are neuropathological hallmarks of Alzheimer's disease (AD). Genetic association of MAPT variants with late-onset AD (LOAD) risk has been inconsistent, although insufficient power and incomplete assessment of MAPT haplotypes may account for this. Methods We examined the association of MAPT haplotypes with LOAD risk in more than 20,000 subjects (n-cases = 9,814, n-controls = 11,550) from Mayo Clinic (n-cases = 2,052, n-controls = 3,406) and the Alzheimer's Disease Genetics Consortium (ADGC, n-cases = 7,762, n-controls = 8,144). We also assessed associations with brain MAPT gene expression levels measured in the cerebellum (n = 197) and temporal cortex (n = 202) of LOAD subjects. Six single nucleotide polymorphisms (SNPs) which tag MAPT haplotypes with frequencies greater than 1% were evaluated. Results H2-haplotype tagging rs8070723-G allele associated with reduced risk of LOAD (odds ratio, OR = 0.90, 95% confidence interval, CI = 0.85-0.95, p = 5.2E-05) with consistent results in the Mayo (OR = 0.81, p = 7.0E-04) and ADGC (OR = 0.89, p = 1.26E-04) cohorts. rs3785883-A allele was also nominally significantly associated with LOAD risk (OR = 1.06, 95% CI = 1.01-1.13, p = 0.034). Haplotype analysis revealed significant global association with LOAD risk in the combined cohort (p = 0.033), with significant association of the H2 haplotype with reduced risk of LOAD as expected (p = 1.53E-04) and suggestive association with additional haplotypes. MAPT SNPs and haplotypes also associated with brain MAPT levels in the cerebellum and temporal cortex of AD subjects with the strongest associations observed for the H2 haplotype and reduced brain MAPT levels (beta = -0.16 to -0.20, p = 1.0E-03 to 3.0E-03). Conclusions These results confirm the previously reported MAPT H2 associations with LOAD risk in two large series, that this haplotype has the strongest effect on brain MAPT expression amongst those tested and identify additional haplotypes with suggestive associations, which require replication in independent series. These biologically congruent results provide compelling evidence to screen the MAPT region for regulatory variants which confer LOAD risk by influencing its brain gene expression.
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    ABSTRACT: Background Identification of the physiological changes that occur during the early stages of Alzheimer’s disease (AD) may provide critical insights for the diagnosis, prognosis and treatment of disease. Cerebrospinal fluid (CSF) biomarkers are a rich source of information that reflect the brain proteome. Methods We applied a novel approach to screen a panel of ~190 CSF analytes quantified by multiplex immunoassay and detected common associations in the Knight- Alzheimer’s Disease Research Center (ADRC;N=311) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI;N=293) cohorts. CSF ptau181-Aβ42 ratio was used as a continuous trait, rather than case control status in these analyses. Results We demonstrate the ptau181-Aβ42 ratio has more statistical power than traditional modeling approaches and that the levels of CSF Fatty Acid Binding Protein (H-FABP) and 12 other correlated analytes increase as the disease progresses. These results were validated using the traditional case control status model. Stratification of our dataset demonstrated that increases in these analytes occur very early in the disease course and were apparent even in non-demented individuals with AD pathology (low ptau181, low Aß42) compared to pathology-negative elderly control subjects (low ptau181, high Aß42). FABP-Aß42 ratio demonstrates a similar hazard ratio for disease conversion to ptau181-Aß42 even though the overlap in classification is incomplete suggesting that FABP contributes independent information as a predictor Conclusions Our results clearly indicate that the approach presented here can be employed to correctly identify novel biomarkers for AD, and that CSF H-FABP levels start to increase at very early stages of the disease.
    Biological psychiatry 05/2014; DOI:10.1016/j.biopsych.2013.11.032 · 9.47 Impact Factor
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    Carlos Cruchaga, Mark T W Ebbert, John S K Kauwe
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    ABSTRACT: The use of cerebrospinal fluid levels of Aβ42 and pTau181 as endophenotypes for genetic studies of Alzheimer's disease (AD) has led to successful identification of both rare and common AD risk variants. In addition, this approach has provided meaningful hypotheses for the biological mechanisms by which known AD risk variants modulate the disease process. In this article we discuss these successes and outline challenges to effective and continued applications of this approach. We contrast the statistical power of this approach with traditional case-control designs and discuss solutions to address challenges in quality control and data analysis for these phenotypes. Finally, we discuss the potential for the use of this approach with larger samples as well as the incorporation of next generation sequencing and for future work with other endophenotypes for AD.
    03/2014; 2(1):23-29. DOI:10.1007/s40142-014-0031-0
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    ABSTRACT: Genome-wide association studies (GWAS) have identified several risk variants for late-onset Alzheimer's disease (LOAD). These common variants have replicable but small effects on LOAD risk and generally do not have obvious functional effects. Low-frequency coding variants, not detected by GWAS, are predicted to include functional variants with larger effects on risk. To identify low-frequency coding variants with large effects on LOAD risk, we carried out whole-exome sequencing (WES) in 14 large LOAD families and follow-up analyses of the candidate variants in several large LOAD case-control data sets. A rare variant in PLD3 (phospholipase D3; Val232Met) segregated with disease status in two independent families and doubled risk for Alzheimer's disease in seven independent case-control series with a total of more than 11,000 cases and controls of European descent. Gene-based burden analyses in 4,387 cases and controls of European descent and 302 African American cases and controls, with complete sequence data for PLD3, reveal that several variants in this gene increase risk for Alzheimer's disease in both populations. PLD3 is highly expressed in brain regions that are vulnerable to Alzheimer's disease pathology, including hippocampus and cortex, and is expressed at significantly lower levels in neurons from Alzheimer's disease brains compared to control brains. Overexpression of PLD3 leads to a significant decrease in intracellular amyloid-β precursor protein (APP) and extracellular Aβ42 and Aβ40 (the 42- and 40-residue isoforms of the amyloid-β peptide), and knockdown of PLD3 leads to a significant increase in extracellular Aβ42 and Aβ40. Together, our genetic and functional data indicate that carriers of PLD3 coding variants have a twofold increased risk for LOAD and that PLD3 influences APP processing. This study provides an example of how densely affected families may help to identify rare variants with large effects on risk for disease or other complex traits.
    Nature 01/2014; 505(7484-7484):550-554. DOI:10.1038/nature12825 · 42.35 Impact Factor
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    ABSTRACT: Alzheimer's disease (AD) is the most common and complex neurodegenerative disease in the elderly individuals. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. These GWAS have yielded important new insights into the genetic mechanisms of AD. However, these newly identified AD susceptibility loci exert only very small risk effects and cannot fully explain the underlying AD genetic risk. We hypothesize that combining the findings from different AD GWAS may have greater power than genetic analysis alone. To identify new AD risk factors, we integrated findings from 3 previous large-scale AD GWAS (n = 14,138) using a gene-based meta-analysis and subsequently conducted a pathway analysis using the kyoto encyclopedia of genes and genomes and gene ontology databases. Interestingly, we not only confirmed previous findings, but also highlighted, for the first time, the involvement of cardiovascular disease-related pathways in AD. Our results provided the clues as to the link between these diseases using pathway analysis methods. We believe that these findings will be very useful for future genetic studies of AD.
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    ABSTRACT: Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene-by-gene (G × G), or gene-by-environment interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity (LRT(MV)) or either effect alone (LRT(M) or LRT(V)) in the presence of covariates. Using extensive simulations for our method and others, we found that all parametric tests were sensitive to nonnormality regardless of any trait transformations. Coupling our test with the parametric bootstrap solves this issue. Using simulations and empirical data from a known mean-only functional variant, we demonstrate how LD can produce variance-heterogeneity loci (vQTL) in a predictable fashion based on differential allele frequencies, high D', and relatively low r² values. We propose that a joint test for mean and variance heterogeneity is more powerful than a variance-only test for detecting vQTL. This takes advantage of loci that also have mean effects without sacrificing much power to detect variance only effects. We discuss using vQTL as an approach to detect G × G interactions and also how vQTL are related to relationship loci, and how both can create prior hypothesis for each other and reveal the relationships between traits and possibly between components of a composite trait.
    Genetic Epidemiology 01/2014; 38(1):51-9. DOI:10.1002/gepi.21778 · 2.95 Impact Factor
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    ABSTRACT: TREM and TREM-like receptors are a structurally similar protein family encoded by genes clustered on chromosome 6p21.11. Recent studies have identified a rare coding variant (p.R47H) in TREM2 that confers a high risk for Alzheimer's disease (AD). In addition, common single nucleotide polymorphisms in this genomic region are associated with cerebrospinal fluid biomarkers for AD and a common intergenic variant found near the TREML2 gene has been identified to be protective for AD. However, little is known about the functional variant underlying the latter association or its relationship with the p.R47H. Here, we report comprehensive analyses using whole-exome sequencing data, cerebrospinal fluid biomarker analyses, meta-analyses (16,254 cases and 20,052 controls) and cell-based functional studies to support the role of the TREML2 coding missense variant p.S144G (rs3747742) as a potential driver of the meta-analysis AD-associated genome-wide association studies signal. Additionally, we demonstrate that the protective role of TREML2 in AD is independent of the role of TREM2 gene as a risk factor for AD.
    Neurobiology of aging 12/2013; DOI:10.1016/j.neurobiolaging.2013.12.010 · 4.85 Impact Factor
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    ABSTRACT: Alzheimer's disease (AD) is an international health concern that has a devastating effect on patients and families. While several genetic risk factors for AD have been identified much of the genetic variance in AD remains unexplained. There are limited published assessments of the familiality of Alzheimer's disease. Here we present the largest genealogy-based analysis of AD to date. We assessed the familiality of AD in The Utah Population Database (UPDB), a population-based resource linking electronic health data repositories for the state with the computerized genealogy of the Utah settlers and their descendants. We searched UPDB for significant familial clustering of AD to evaluate the genetic contribution to disease. We compared the Genealogical Index of Familiality (GIF) between AD individuals and randomly selected controls and estimated the Relative Risk (RR) for a range of family relationships. Finally, we identified pedigrees with a significant excess of AD deaths. The GIF analysis showed that pairs of individuals dying from AD were significantly more related than expected. This excess of relatedness was observed for both close and distant relationships. RRs for death from AD among relatives of individuals dying from AD were significantly increased for both close and more distant relatives. Multiple pedigrees had a significant excess of AD deaths. These data strongly support a genetic contribution to the observed clustering of individuals dying from AD. This report is the first large population-based assessment of the familiality of AD mortality and provides the only reported estimates of relative risk of AD mortality in extended relatives to date. The high-risk pedigrees identified show a true excess of AD mortality (not just multiple cases) and are greater in depth and width than published AD pedigrees. The presence of these high-risk pedigrees strongly supports the possibility of rare predisposition variants not yet identified.
    PLoS ONE 10/2013; 8(10):e77087. DOI:10.1371/journal.pone.0077087 · 3.53 Impact Factor
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    ABSTRACT: The Genetics Core of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer’s disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development. Electronic supplementary material The online version of this article (doi:10.1007/s11682-013-9262-z) contains supplementary material, which is available to authorized users.
    Brain Imaging and Behavior 10/2013; 8(2). DOI:10.1007/s11682-013-9262-z · 3.39 Impact Factor
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    ABSTRACT: Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer's disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10(-8)) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.
    Nature Genetics 10/2013; 45(12):1-9. DOI:10.1038/ng.2802 · 29.65 Impact Factor

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