Publications by authors named "Anita L DeStefano"

92 Publications

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores.

Nat Commun 2021 06 7;12(1):3417. Epub 2021 Jun 7.

Servei de Neurologia, Hospital Universitari i Politècnic La Fe, Valencia, Spain.

Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease.
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http://dx.doi.org/10.1038/s41467-021-22491-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184987PMC
June 2021

Cardiovascular health, genetic risk, and risk of dementia in the Framingham Heart Study.

Neurology 2020 09 20;95(10):e1341-e1350. Epub 2020 Jul 20.

From the Departments of Biostatistics (G.M.P., A.S.B., V.X., A.L.D.) and Epidemiology (R.S.V.), Boston University School of Public Health; Boston University and NHLBI's Framingham Heart Study (A.S.B., C.L.S., V.X., R.S.V., A.L.D., S.S.), Framingham; Department of Neurology (A.S.B., C.L.S., A.L.D., S.S.), Boston University School of Medicine, MA; Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases (C.L.S., S.S.), University of Texas Health Sciences Center, San Antonio; Sections of Preventive Medicine & Epidemiology and Cardiology (V.X., R.S.V.), Department of Medicine, Boston University, MA; Melbourne Dementia Research Centre (M.P.P.), The Florey Institute for Neuroscience and Mental Health; Faculty of Medicine, Dentistry, and Health Sciences (M.P.P.), University of Melbourne, Parkville; Centre for Human Psychopharmacology (M.P.P.), Swinburne University of Technology, Hawthorn, Australia; and Harvard T.H. Chan School of Public Health (M.P.P.), Boston, MA.

Objective: To determine the joint role of ideal cardiovascular health (CVH) and genetic risk on risk of dementia.

Methods: We categorized CVH on the basis of the American Heart Association Ideal CVH Index and genetic risk through a genetic risk score (GRS) of common genetic variants and the ε4 genotype in 1,211 Framingham Heart Study (FHS) offspring cohort participants. We used multivariable Cox proportional hazards regression models to examine the association between CVH, genetic risk, and incident all-cause dementia with up to 10 years of follow-up (mean 8.4 years, 96 incident dementia cases), adjusting for age, sex, and education.

Results: We observed that a high GRS (>80th percentile) was associated with a 2.6-fold risk of dementia (95% confidence interval [CI] of hazard ratio [HR] 1.23-5.29; = 0.012) compared with having a low GRS (<20th percentile); carrying at least 1 ε4 allele was associated with a 2.3-fold risk of dementia compared with not carrying an ε4 allele (95% CI of HR 1.49-3.53; = 0.0002), and having a favorable CVH showed a 0.45-fold lower risk of dementia (95% CI of HR 0.20-1.01; = 0.0527) compared to having an unfavorable CVH when all 3 components were included in the model. We did not observe an interaction between CVH and GRS ( = 0.99) or ε4 ( = 0.16).

Conclusions: We observed that both genetic risk and CVH contribute additively to dementia risk.
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http://dx.doi.org/10.1212/WNL.0000000000010306DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538213PMC
September 2020

Evaluation of population stratification adjustment using genome-wide or exonic variants.

Genet Epidemiol 2020 10 30;44(7):702-716. Epub 2020 Jun 30.

Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.

Population stratification may cause an inflated type-I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however, population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two established approaches: principal components analysis (PCA) and mixed-effects models and assess the utility of genome-wide (GW) and exonic variants, by simulation and using a data set from the Framingham Heart Study. Our results illustrate that although the PCs and genetic relationship matrices computed by GW and exonic markers are different, the type-I error rate of association tests for common variants with additive effect appear to be properly controlled in the presence of population stratification. In addition, by considering single nucleotide variants (SNVs) that have different levels of confounding by population stratification, we also compare the power across multiple association approaches to account for population stratification such as PC-based corrections and mixed-effects models. We find that while these two methods achieve a similar power for SNVs that have a low or medium level of confounding by population stratification, mixed-effects model can reach a higher power for SNVs highly confounded by population stratification.
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http://dx.doi.org/10.1002/gepi.22332DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722041PMC
October 2020

Genetic architecture of subcortical brain structures in 38,851 individuals.

Nat Genet 2019 11 21;51(11):1624-1636. Epub 2019 Oct 21.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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http://dx.doi.org/10.1038/s41588-019-0511-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055269PMC
November 2019

Analysis of Whole-Exome Sequencing Data for Alzheimer Disease Stratified by APOE Genotype.

JAMA Neurol 2019 Sep;76(9):1099-1108

Department of Medicine (Biomedical Genetics), Boston University Schools of Medicine and Public Health, Boston, Massachusetts.

Importance: Previous genome-wide association studies of common variants identified associations for Alzheimer disease (AD) loci evident only among individuals with particular APOE alleles.

Objective: To identify APOE genotype-dependent associations with infrequent and rare variants using whole-exome sequencing.

Design, Setting, And Participants: The discovery stage included 10 441 non-Hispanic white participants in the Alzheimer Disease Sequencing Project. Replication was sought in 2 independent, whole-exome sequencing data sets (1766 patients with AD, 2906 without AD [controls]) and a chip-based genotype imputation data set (8728 patients with AD, 9808 controls). Bioinformatics and functional analyses were conducted using clinical, cognitive, neuropathologic, whole-exome sequencing, and gene expression data obtained from a longitudinal cohort sample including 402 patients with AD and 647 controls. Data were analyzed between March 2017 and September 2018.

Main Outcomes And Measures: Score, Firth, and sequence kernel association tests were used to test the association of AD risk with individual variants and genes in subgroups of APOE ε4 carriers and noncarriers. Results with P ≤ 1 × 10-5 were further evaluated in the replication data sets and combined by meta-analysis.

Results: Among 3145 patients with AD and 4213 controls lacking ε4 (mean [SD] age, 83.4 [7.6] years; 4363 [59.3.%] women), novel genome-wide significant associations were obtained in the discovery sample with rs536940594 in AC099552 (odds ratio [OR], 88.0; 95% CI, 9.08-852.0; P = 2.22 × 10-7) and rs138412600 in GPAA1 (OR, 1.78; 95% CI, 1.44-2.2; meta-P = 7.81 × 10-8). GPAA1 was also associated with expression in the brain of GPAA1 (β = -0.08; P = .03) and its repressive transcription factor, FOXG1 (β = 0.13; P = .003), and global cognition function (β = -0.53; P = .009). Significant gene-wide associations (threshold P ≤ 6.35 × 10-7) were observed for OR8G5 (P = 4.67 × 10-7), IGHV3-7 (P = 9.75 × 10-16), and SLC24A3 (P = 2.67 × 10-12) in 2377 patients with AD and 706 controls with ε4 (mean [SD] age, 75.2 [9.6] years; 1668 [54.1%] women).

Conclusions And Relevance: The study identified multiple possible novel associations for AD with individual and aggregated rare variants in groups of individuals with and without APOE ε4 alleles that reinforce known and suggest additional pathways leading to AD.
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http://dx.doi.org/10.1001/jamaneurol.2019.1456DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563544PMC
September 2019

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.

Nat Genet 2019 03 28;51(3):414-430. Epub 2019 Feb 28.

Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades-Universitat Internacional de Catalunya, Barcelona, Spain.

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.
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http://dx.doi.org/10.1038/s41588-019-0358-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6463297PMC
March 2019

Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting.

Neurology 2019 Jan 16. Epub 2019 Jan 16.

Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.

Methods: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.

Results: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, = 1.77 × 10; and LINC00539/ZDHHC20, = 5.82 × 10. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits ( value for BI, = 9.38 × 10; = 5.23 × 10 for hypertension), smoking ( = 4.4 × 10; = 1.2 × 10), diabetes ( = 1.7 × 10; = 2.8 × 10), previous cardiovascular disease ( = 1.0 × 10; = 2.3 × 10), stroke ( = 3.9 × 10; = 3.2 × 10), and MRI-defined white matter hyperintensity burden ( = 1.43 × 10; = 3.16 × 10), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI ( ≤ 0.0022), without indication of directional pleiotropy.

Conclusion: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.
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http://dx.doi.org/10.1212/WNL.0000000000006851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369905PMC
January 2019

Genetically elevated high-density lipoprotein cholesterol through the cholesteryl ester transfer protein gene does not associate with risk of Alzheimer's disease.

Alzheimers Dement (Amst) 2018 22;10:595-598. Epub 2018 Sep 22.

NHLBI's Framingham Heart Study, Framingham, MA, USA.

Introduction: There is conflicting evidence whether high-density lipoprotein cholesterol (HDL-C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein () locus is associated with altered HDL-C. We aimed to assess AD risk by genetically predicted HDL-C.

Methods: Ten single nucleotide polymorphisms within the locus predicting HDL-C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR-Egger.

Results: Based on 10 single nucleotide polymorphisms distinctly predicting HDL-C in the locus, we found that HDL-C was not associated with risk of AD ( > .7).

Discussion: Our study does not support the role of HDL-C on risk of AD through HDL-C altered by . This study does not rule out other mechanisms by which HDL-C affects risk of AD.
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http://dx.doi.org/10.1016/j.dadm.2018.08.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215982PMC
September 2018

Genetic Interaction with Plasma Lipids on Alzheimer's Disease in the Framingham Heart Study.

J Alzheimers Dis 2018 ;66(3):1275-1282

NHLBI's Framingham Heart Study, Framingham, MA, USA.

Epidemiological and genetic studies have pointed to the role of cholesterol in Alzheimer's disease (AD). We explored the interaction of a genetic risk score (GRS) of AD risk alleles with mid-life plasma lipid levels (LDL-C, HDL-C, and triglycerides) on risk for AD in the Framingham Heart Study (FHS). Mid-life (between the ages of 40-60 years old) lipid levels were obtained from individuals in the FHS Original and Offspring cohorts (157 cases and 2,882 controls) with genetic data and AD status available. Cox proportional hazards regression was performed to test the interaction between mid-life lipid levels and an AD GRS, as well as the individual contributing SNPs, on risk of incident AD adjusting for age, sex, and cohort. We found a significant interaction between a GRS of AD loci and log triglyceride levels on risk of clinical AD (p = 0.006), but no interaction of the GRS with HDL-C (p = 0.458) or LDL-C (p = 0.366). We then tested the interaction between the individual SNPs contributing to the GRS and log triglycerides. We found two SNPs that had interactions with triglycerides on AD risk that reached a p-value < 0.05 (rs11218343 and APOEɛ4). The association between some AD SNPs and risk of AD may be modified by triglyceride levels. Furthermore, sequential testing of a GRS with a set of traits on disease followed by testing individual SNPs for interaction provides a framework for narrowing the associations that need to be tested for interaction analyses. Replication is needed to confirm these findings.
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http://dx.doi.org/10.3233/JAD-180751DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460910PMC
November 2019

Integrative methylation score to identify epigenetic modifications associated with lipid changes resulting from fenofibrate treatment in families.

BMC Proc 2018 17;12(Suppl 9):28. Epub 2018 Sep 17.

2National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, 73 Mount Wayte Avenue, Framingham, MA 01702 USA.

Epigenome-wide association studies (EWAS) have traditionally focused on the association test of single epigenetic markers with complex traits. However, it is possible that multiple cytosine-phosphate-guanine (CpG) sites at the same locus could jointly exert their effects on human traits. Therefore, a region-based test that combines multiple markers could be more powerful. We used 2 different region-based tests to investigate the association between changes in DNA methylation and drug response, including the median methylation level test (MMLT) and sequence kernel association test (SKAT). No genes were found to be significantly associated with the drug response (for triglycerides, the false discovery rate ranged from 0.855 to 0.999; for high-density lipoprotein cholesterol, and the false discovery rate ranged from 0.584 to 0.915). Further evidence is needed to explore potential application of gene-level methylation association analysis.
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http://dx.doi.org/10.1186/s12919-018-0125-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157127PMC
September 2018

Network analysis of drug effect on triglyceride-associated DNA methylation.

BMC Proc 2018 17;12(Suppl 9):27. Epub 2018 Sep 17.

1Department of Biostatistics, Boston University, 801 Massachusetts Avenue 3rd Floor, Boston, MA 02118 USA.

Background: DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites.

Methods: We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphate-guanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules.

Results: Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Top-enriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways ( values < 0.005). One module in particular included an enrichment of lipid-related pathways among the top results.

Conclusions: The same 3 modules, which were differentially methylated between pre- and posttreatment, were identified using both WGCNA module-preservation and GHD methods. Pathway analysis revealed that triglyceride-associated modules contain groups of genes that are involved in lipid signaling and metabolism. These 3 modules may provide insight into the effect of fenofibrate on changes in triglyceride levels and these methylation sites.
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http://dx.doi.org/10.1186/s12919-018-0130-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157190PMC
September 2018

Whole exome sequencing study identifies novel rare and common Alzheimer's-Associated variants involved in immune response and transcriptional regulation.

Mol Psychiatry 2020 08 14;25(8):1859-1875. Epub 2018 Aug 14.

McDonnell Genome Institute, Washington University, St. Louis, MO, USA.

The Alzheimer's Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.
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http://dx.doi.org/10.1038/s41380-018-0112-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6375806PMC
August 2020

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging.

Stroke 2018 08;49(8):1812-1819

Department of Biochemistry (D.W.B., N.D.P.), Wake Forest School of Medicine, Winston-Salem, NC.

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P=4.5×10) partially independent of known common signal ( P=1.4×10). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; P=1.9×10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P=2.8×10). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.
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http://dx.doi.org/10.1161/STROKEAHA.118.020689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202149PMC
August 2018

Quality control and integration of genotypes from two calling pipelines for whole genome sequence data in the Alzheimer's disease sequencing project.

Genomics 2019 07 29;111(4):808-818. Epub 2018 May 29.

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA.

The Alzheimer's Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed "consensus calling," to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.
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http://dx.doi.org/10.1016/j.ygeno.2018.05.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397097PMC
July 2019

Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes.

Nat Genet 2018 04 12;50(4):524-537. Epub 2018 Mar 12.

Institute of Cardiovascular Research, Royal Holloway University of London, London, UK, and Ashford and St Peters Hospital, Surrey, UK.

Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.
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http://dx.doi.org/10.1038/s41588-018-0058-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5968830PMC
April 2018

Whole genome sequence analyses of brain imaging measures in the Framingham Study.

Neurology 2018 01 27;90(3):e188-e196. Epub 2017 Dec 27.

From the Department of Epidemiology (C.S., L.A.C., A.S.B., A.L.D., J.D.), Boston University School of Public Health; Boston University and the NHLBI's Framingham Heart Study (C.L.S., A.N.P., L.A.C., R.S.V., A.S.B., A.L.D., J.D., S.S.); Departments of Neurology (C.L.S., A.S.B., A.L.D., S.S.) and Cardiology, Preventive Medicine & Epidemiology (R.S.V.), Boston University School of Medicine, Boston, MA; Department of Neurology and Center for Neuroscience (C.D.), University of California at Davis; Department of Physiology and Biophysics (J.G.W.), University of Mississippi Medical Center, Jackson; Cardiovascular Health Research Unit (J.C.B.), Department of Medicine, University of Washington, Seattle; and Institute of Molecular Medicine (M.F.), University of Texas Health Science Center, Houston.

Objective: We sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans-Omics for Precision Medicine Program.

Methods: We performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene-based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants.

Results: We detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, = 10) and in 16q23 for hippocampal volume (MAF 0.05, = 2.7 × 10). Previously identified associations in 12q24 for hippocampal volume (rs7294919, = 4.4 × 10) and in 17q25 for WMH (rs7214628, = 2.0 × 10) were confirmed. Gene-based tests detected associations ( ≤ 2.3 × 10) in new loci for cerebral (5q13, 8p12, 9q31, 13q12-q13, 15q24, 17q12, 19q13) and hippocampal volumes (2p12) and WMH (3q13, 4p15) including Alzheimer disease- () and Parkinson disease-associated genes (). Pathway analyses evidenced enrichment of associated genes in immunity, inflammation, and Alzheimer disease and Parkinson disease pathways.

Conclusions: Whole genome sequence-wide search reveals intriguing new loci associated with brain measures. Replication of novel loci is needed to confirm these findings.
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http://dx.doi.org/10.1212/WNL.0000000000004820DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5772158PMC
January 2018

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease.

Nat Genet 2017 09 17;49(9):1373-1384. Epub 2017 Jul 17.

Boston University School of Medicine, Boston, Massachusetts, USA.

We identified rare coding variants associated with Alzheimer's disease in a three-stage case-control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 × 10) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 × 10) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 × 10, odds ratio (OR) = 0.68, minor allele frequency (MAF) = 0.0059, MAF = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 × 10, OR = 1.43, MAF = 0.011, MAF = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 × 10, OR = 1.67, MAF = 0.0143, MAF = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.
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http://dx.doi.org/10.1038/ng.3916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5669039PMC
September 2017

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

BMC Bioinformatics 2017 Feb 6;18(1):91. Epub 2017 Feb 6.

Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, Massachusetts, USA.

Background: Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates.

Results: When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression.

Conclusions: We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.
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http://dx.doi.org/10.1186/s12859-017-1498-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5294900PMC
February 2017

Novel genetic loci underlying human intracranial volume identified through genome-wide association.

Nat Neurosci 2016 12 3;19(12):1569-1582. Epub 2016 Oct 3.

Brain Center Rudolf Magnus, Department of Psychiatry, UMC Utrecht, Utrecht, the Netherlands.

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρ = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.
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http://dx.doi.org/10.1038/nn.4398DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5227112PMC
December 2016

Rare Functional Variant in TM2D3 is Associated with Late-Onset Alzheimer's Disease.

PLoS Genet 2016 Oct 20;12(10):e1006327. Epub 2016 Oct 20.

Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom.

We performed an exome-wide association analysis in 1393 late-onset Alzheimer's disease (LOAD) cases and 8141 controls from the CHARGE consortium. We found that a rare variant (P155L) in TM2D3 was enriched in Icelanders (~0.5% versus <0.05% in other European populations). In 433 LOAD cases and 3903 controls from the Icelandic AGES sub-study, P155L was associated with increased risk and earlier onset of LOAD [odds ratio (95% CI) = 7.5 (3.5-15.9), p = 6.6x10-9]. Mutation in the Drosophila TM2D3 homolog, almondex, causes a phenotype similar to loss of Notch/Presenilin signaling. Human TM2D3 is capable of rescuing these phenotypes, but this activity is abolished by P155L, establishing it as a functionally damaging allele. Our results establish a rare TM2D3 variant in association with LOAD susceptibility, and together with prior work suggests possible links to the β-amyloid cascade.
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http://dx.doi.org/10.1371/journal.pgen.1006327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072721PMC
October 2016

Novel microRNA discovery using small RNA sequencing in post-mortem human brain.

BMC Genomics 2016 10 4;17(1):776. Epub 2016 Oct 4.

Department of Neurology, Boston University School of Medicine, Boston, USA.

Background: MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression mainly through translational repression of target mRNA molecules. More than 2700 human miRNAs have been identified and some are known to be associated with disease phenotypes and to display tissue-specific patterns of expression.

Methods: We used high-throughput small RNA sequencing to discover novel miRNAs in 93 human post-mortem prefrontal cortex samples from individuals with Huntington's disease (n = 28) or Parkinson's disease (n = 29) and controls without neurological impairment (n = 36). A custom miRNA identification analysis pipeline was built, which utilizes miRDeep* miRNA identification and result filtering based on false positive rate estimates.

Results: Ninety-nine novel miRNA candidates with a false positive rate of less than 5 % were identified. Thirty-four of the candidate miRNAs show sequence similarity with known mature miRNA sequences and may be novel members of known miRNA families, while the remaining 65 may constitute previously undiscovered families of miRNAs. Nineteen of the 99 candidate miRNAs were replicated using independent, publicly-available human brain RNA-sequencing samples, and seven were experimentally validated using qPCR.

Conclusions: We have used small RNA sequencing to identify 99 putative novel miRNAs that are present in human brain samples.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5050850PMC
http://dx.doi.org/10.1186/s12864-016-3114-3DOI Listing
October 2016

Evaluation of a Genetic Risk Score to Improve Risk Prediction for Alzheimer's Disease.

J Alzheimers Dis 2016 06;53(3):921-32

Lille University, Inserm, Lille University Hospital, Institut Pasteur de Lille, U1167 - RID-AGE - Risk factors and molecular determinants of aging-related diseases; Labex Distalz, Lille, France.

Effective prevention of Alzheimer's disease (AD) requires the development of risk prediction tools permitting preclinical intervention. We constructed a genetic risk score (GRS) comprising common genetic variants associated with AD, evaluated its association with incident AD and assessed its capacity to improve risk prediction over traditional models based on age, sex, education, and APOEɛ4. In eight prospective cohorts included in the International Genomics of Alzheimer's Project (IGAP), we derived weighted sum of risk alleles from the 19 top SNPs reported by the IGAP GWAS in participants aged 65 and older without prevalent dementia. Hazard ratios (HR) of incident AD were estimated in Cox models. Improvement in risk prediction was measured by the difference in C-index (Δ-C), the integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI>0). Overall, 19,687 participants at risk were included, of whom 2,782 developed AD. The GRS was associated with a 17% increase in AD risk (pooled HR = 1.17; 95% CI =   [1.13-1.21] per standard deviation increase in GRS; p-value =  2.86×10-16). This association was stronger among persons with at least one APOEɛ4 allele (HRGRS = 1.24; 95% CI =   [1.15-1.34]) than in others (HRGRS = 1.13; 95% CI =   [1.08-1.18]; pinteraction = 3.45×10-2). Risk prediction after seven years of follow-up showed a small improvement when adding the GRS to age, sex, APOEɛ4, and education (Δ-Cindex =  0.0043 [0.0019-0.0067]). Similar patterns were observed for IDI and NRI>0. In conclusion, a risk score incorporating common genetic variation outside the APOEɛ4 locus improved AD risk prediction and may facilitate risk stratification for prevention trials.
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http://dx.doi.org/10.3233/JAD-150749DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036102PMC
June 2016

Six Novel Loci Associated with Circulating VEGF Levels Identified by a Meta-analysis of Genome-Wide Association Studies.

PLoS Genet 2016 Feb 24;12(2):e1005874. Epub 2016 Feb 24.

UMR INSERM U1122, IGE-PCV "Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire", Faculté de Pharmacie, Université de Lorraine, Nancy, France.

Vascular endothelial growth factor (VEGF) is an angiogenic and neurotrophic factor, secreted by endothelial cells, known to impact various physiological and disease processes from cancer to cardiovascular disease and to be pharmacologically modifiable. We sought to identify novel loci associated with circulating VEGF levels through a genome-wide association meta-analysis combining data from European-ancestry individuals and using a dense variant map from 1000 genomes imputation panel. Six discovery cohorts including 13,312 samples were analyzed, followed by in-silico and de-novo replication studies including an additional 2,800 individuals. A total of 10 genome-wide significant variants were identified at 7 loci. Four were novel loci (5q14.3, 10q21.3, 16q24.2 and 18q22.3) and the leading variants at these loci were rs114694170 (MEF2C, P = 6.79 x 10(-13)), rs74506613 (JMJD1C, P = 1.17 x 10(-19)), rs4782371 (ZFPM1, P = 1.59 x 10(-9)) and rs2639990 (ZADH2, P = 1.72 x 10(-8)), respectively. We also identified two new independent variants (rs34528081, VEGFA, P = 1.52 x 10(-18); rs7043199, VLDLR-AS1, P = 5.12 x 10(-14)) at the 3 previously identified loci and strengthened the evidence for the four previously identified SNPs (rs6921438, LOC100132354, P = 7.39 x 10(-1467); rs1740073, C6orf223, P = 2.34 x 10(-17); rs6993770, ZFPM2, P = 2.44 x 10(-60); rs2375981, KCNV2, P = 1.48 x 10(-100)). These variants collectively explained up to 52% of the VEGF phenotypic variance. We explored biological links between genes in the associated loci using Ingenuity Pathway Analysis that emphasized their roles in embryonic development and function. Gene set enrichment analysis identified the ERK5 pathway as enriched in genes containing VEGF associated variants. eQTL analysis showed, in three of the identified regions, variants acting as both cis and trans eQTLs for multiple genes. Most of these genes, as well as some of those in the associated loci, were involved in platelet biogenesis and functionality, suggesting the importance of this process in regulation of VEGF levels. This work also provided new insights into the involvement of genes implicated in various angiogenesis related pathologies in determining circulating VEGF levels. The understanding of the molecular mechanisms by which the identified genes affect circulating VEGF levels could be important in the development of novel VEGF-related therapies for such diseases.
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http://dx.doi.org/10.1371/journal.pgen.1005874DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4766012PMC
February 2016

Evaluation of power of the Illumina HumanOmni5M-4v1 BeadChip to detect risk variants for human complex diseases.

Eur J Hum Genet 2016 07 18;24(7):1029-34. Epub 2015 Nov 18.

Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Although emerging sequencing technologies can characterize all genetic variants, the cost is still high. Illumina released the HumanOmni5M-4v1 (Omni5) genotype array with ~4.3M assayed SNPs, a much denser array compared with other available arrays. The Omni5 balances both cost and array density. In this article, we illustrate the power of Omni5 to detect genetic associations. The Omni5 includes variants with a wide range of minor allele frequencies down to <1%. The theoretical power calculation examples indicate the increased power of the Omni5 array compared with other arrays with lower density when evaluating associations with some known loci, although there are exceptions. We further evaluate the genetic associations between known loci and several quantitative traits in the Framingham Heart Study: femoral neck bone mineral density, lumbar spine bone mineral density and hippocampal volume. Finally, we search genome wide for novel associations using the Omni5 genotypes. We compare our association results from Affymetrix 500K+MIPS 50K arrays and two imputed data sets: (1) HapMap Phase II and (2) 1000 Genomes reference panel. We observed increased evidence for genotype-phenotype associations with smaller P-values for selected known loci using the Omni5 genotypes. With limited sample sizes, we identify novel variants with genome-wide significant P-values. Our observations support the notion that dense genotyping using the Omni5 can be powerful in detecting novel associated variants. Comparison with imputed data with higher density also suggests that imputation helps but cannot replace genotyping, especially when imputation quality is low.
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http://dx.doi.org/10.1038/ejhg.2015.244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070895PMC
July 2016

Genome-wide linkage analyses of non-Hispanic white families identify novel loci for familial late-onset Alzheimer's disease.

Alzheimers Dement 2016 Jan 11;12(1):2-10. Epub 2015 Sep 11.

John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA; Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA. Electronic address:

Introduction: Few high penetrance variants that explain risk in late-onset Alzheimer's disease (LOAD) families have been found.

Methods: We performed genome-wide linkage and identity-by-descent (IBD) analyses on 41 non-Hispanic white families exhibiting likely dominant inheritance of LOAD, and having no mutations at known familial Alzheimer's disease (AD) loci, and a low burden of APOE ε4 alleles.

Results: Two-point parametric linkage analysis identified 14 significantly linked regions, including three novel linkage regions for LOAD (5q32, 11q12.2-11q14.1, and 14q13.3), one of which replicates a genome-wide association LOAD locus, the MS4A6A-MS4A4E gene cluster at 11q12.2. Five of the 14 regions (3q25.31, 4q34.1, 8q22.3, 11q12.2-14.1, and 19q13.41) are supported by strong multipoint results (logarithm of odds [LOD*] ≥1.5). Nonparametric multipoint analyses produced an additional significant locus at 14q32.2 (LOD* = 4.18). The 1-LOD confidence interval for this region contains one gene, C14orf177, and the microRNA Mir_320, whereas IBD analyses implicates an additional gene BCL11B, a regulator of brain-derived neurotrophic signaling, a pathway associated with pathogenesis of several neurodegenerative diseases.

Discussion: Examination of these regions after whole-genome sequencing may identify highly penetrant variants for familial LOAD.
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http://dx.doi.org/10.1016/j.jalz.2015.05.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717829PMC
January 2016

Rare and Coding Region Genetic Variants Associated With Risk of Ischemic Stroke: The NHLBI Exome Sequence Project.

JAMA Neurol 2015 Jul;72(7):781-8

Center for Public Health Genomics, University of Virginia, Charlottesville.

Importance: Stroke is the second leading cause of death and the third leading cause of years of life lost. Genetic factors contribute to stroke prevalence, and candidate gene and genome-wide association studies (GWAS) have identified variants associated with ischemic stroke risk. These variants often have small effects without obvious biological significance. Exome sequencing may discover predicted protein-altering variants with a potentially large effect on ischemic stroke risk.

Objective: To investigate the contribution of rare and common genetic variants to ischemic stroke risk by targeting the protein-coding regions of the human genome.

Design, Setting, And Participants: The National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP) analyzed approximately 6000 participants from numerous cohorts of European and African ancestry. For discovery, 365 cases of ischemic stroke (small-vessel and large-vessel subtypes) and 809 European ancestry controls were sequenced; for replication, 47 affected sibpairs concordant for stroke subtype and an African American case-control series were sequenced, with 1672 cases and 4509 European ancestry controls genotyped. The ESP's exome sequencing and genotyping started on January 1, 2010, and continued through June 30, 2012. Analyses were conducted on the full data set between July 12, 2012, and July 13, 2013.

Main Outcomes And Measures: Discovery of new variants or genes contributing to ischemic stroke risk and subtype (primary analysis) and determination of support for protein-coding variants contributing to risk in previously published candidate genes (secondary analysis).

Results: We identified 2 novel genes associated with an increased risk of ischemic stroke: a protein-coding variant in PDE4DIP (rs1778155; odds ratio, 2.15; P = 2.63 × 10(-8)) with an intracellular signal transduction mechanism and in ACOT4 (rs35724886; odds ratio, 2.04; P = 1.24 × 10(-7)) with a fatty acid metabolism; confirmation of PDE4DIP was observed in affected sibpair families with large-vessel stroke subtype and in African Americans. Replication of protein-coding variants in candidate genes was observed for 2 previously reported GWAS associations: ZFHX3 (cardioembolic stroke) and ABCA1 (large-vessel stroke).

Conclusions And Relevance: Exome sequencing discovered 2 novel genes and mechanisms, PDE4DIP and ACOT4, associated with increased risk for ischemic stroke. In addition, ZFHX3 and ABCA1 were discovered to have protein-coding variants associated with ischemic stroke. These results suggest that genetic variation in novel pathways contributes to ischemic stroke risk and serves as a target for prediction, prevention, and therapy.
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http://dx.doi.org/10.1001/jamaneurol.2015.0582DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673986PMC
July 2015

Polygenic Overlap Between C-Reactive Protein, Plasma Lipids, and Alzheimer Disease.

Circulation 2015 Jun 10;131(23):2061-2069. Epub 2015 Apr 10.

Departments of Medicine (Biomedical Genetics), Neurology, Ophthalmology, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA.

Background: Epidemiological findings suggest a relationship between Alzheimer disease (AD), inflammation, and dyslipidemia, although the nature of this relationship is not well understood. We investigated whether this phenotypic association arises from a shared genetic basis.

Methods And Results: Using summary statistics (P values and odds ratios) from genome-wide association studies of >200 000 individuals, we investigated overlap in single-nucleotide polymorphisms associated with clinically diagnosed AD and C-reactive protein (CRP), triglycerides, and high- and low-density lipoprotein levels. We found up to 50-fold enrichment of AD single-nucleotide polymorphisms for different levels of association with C-reactive protein, low-density lipoprotein, high-density lipoprotein, and triglyceride single-nucleotide polymorphisms using a false discovery rate threshold <0.05. By conditioning on polymorphisms associated with the 4 phenotypes, we identified 55 loci associated with increased AD risk. We then conducted a meta-analysis of these 55 variants across 4 independent AD cohorts (total: n=29 054 AD cases and 114 824 healthy controls) and discovered 2 genome-wide significant variants on chromosome 4 (rs13113697; closest gene, HS3ST1; odds ratio=1.07; 95% confidence interval=1.05-1.11; P=2.86×10(-8)) and chromosome 10 (rs7920721; closest gene, ECHDC3; odds ratio=1.07; 95% confidence interval=1.04-1.11; P=3.38×10(-8)). We also found that gene expression of HS3ST1 and ECHDC3 was altered in AD brains compared with control brains.

Conclusions: We demonstrate genetic overlap between AD, C-reactive protein, and plasma lipids. By conditioning on the genetic association with the cardiovascular phenotypes, we identify novel AD susceptibility loci, including 2 genome-wide significant variants conferring increased risk for AD.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.115.015489DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4677995PMC
June 2015
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