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Research Article

Alzheimer’s Disease: Analyzing the Missing Heritability

  • Perry G. Ridge,

    Affiliations: Department of Biology, Brigham Young University, Provo, Utah, United States of America, ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, Utah, United States of America

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  • Shubhabrata Mukherjee,

    Affiliation: Department of Medicine, University of Washington, Seattle, Washington, United States of America

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  • Paul K. Crane,

    Affiliation: Department of Medicine, University of Washington, Seattle, Washington, United States of America

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  • John S. K. Kauwe mail,

    kauwe@byu.edu

    Affiliation: Department of Biology, Brigham Young University, Provo, Utah, United States of America

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  • Alzheimer’s Disease Genetics Consortium

    Membership of the Alzheimer’s Disease Genetics Consortium is provided in the Acknowledgments.

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  • Published: November 07, 2013
  • DOI: 10.1371/journal.pone.0079771

Abstract

Alzheimer’s disease (AD) is a complex disorder influenced by environmental and genetic factors. Recent work has identified 11 AD markers in 10 loci. We used Genome-wide Complex Trait Analysis to analyze >2 million SNPs for 10,922 individuals from the Alzheimer’s Disease Genetics Consortium to assess the phenotypic variance explained first by known late-onset AD loci, and then by all SNPs in the Alzheimer’s Disease Genetics Consortium dataset. In all, 33% of total phenotypic variance is explained by all common SNPs. APOE alone explained 6% and other known markers 2%, meaning more than 25% of phenotypic variance remains unexplained by known markers, but is tagged by common SNPs included on genotyping arrays or imputed with HapMap genotypes. Novel AD markers that explain large amounts of phenotypic variance are likely to be rare and unidentifiable using genome-wide association studies. Based on our findings and the current direction of human genetics research, we suggest specific study designs for future studies to identify the remaining heritability of Alzheimer’s disease.

Introduction

Alzheimer’s disease (AD) is the most common form of dementia. Worldwide estimates of prevalence vary, with estimates of 24 to 35 million people affected [1-3]. Combined with an aging population, prevalence is expected to increase to 1 in 85 people by 2050 [2].

AD is a heterogeneous disease caused by a combination of environmental and genetic factors. The most important risk factor for Alzheimer’s disease is age [1,4]. Environmental risk factors include hypertension, estrogen supplements [5], smoking [6,7], stroke, heart disease, depression, arthritis, and diabetes [8]. In addition, certain lifestyle choices appear to decrease the risk of AD: exercise [9], intellectual stimulation [10], and maintaining a Mediterranean diet (including fish)[11,12].

The genetics of AD are complex. Several genes are known to harbor either causative or risk variants for AD. There are two primary types of AD as defined by age. The first is early-onset, or familial AD, and the second type is late-onset AD (LOAD), sometimes termed sporadic AD. Three genes, APP [13], PSEN1 [14], and PSEN2 [15] are known to harbor many highly penetrant, autosomal dominantly-inherited variants, which lead to early-onset AD but account for only a small fraction of total AD cases.

LOAD accounts for 99% of AD cases and is caused by a more complex underlying genetic architecture. Genome-wide association studies (GWAS) have identified 10 different loci associated with AD (Table 1). Recent applications of next-generation sequencing (NGS) have suggested rare variants play important role and have large effects in the etiology of AD [16-18]. Identifying additional variants will provide information that is integral to the development, evaluation and application of effective therapeutic strategies for AD. Lee et al. [19] used 3,333 cases and 3,924 controls, including 2,699 population-based controls to estimate that common genetic variants account for 24% of variance in AD. They also estimated the contribution of APOE using several proxy SNPs, with varying degrees of LD, with the APOE ε4 allele to estimate the APOE effect at approximately 4%. Here we evaluate the variance in AD status explained by common SNPs and along with all recently identified AD genes, including direct genotyping of the APOE ε2 and ε4 alleles, in 5,708 AD cases and 5,214 clinically ascertained controls. We also suggest strategies for identifying the remaining AD genes.

VariantGeneAbbreviationOdds Ratio
rs429358Apolipoprotein E (ε4 allele)[58]APOE 3.685
rs7412Apolipoprotein E (ε2 allele)[59]APOE 0.621
rs744373Bridging Integrator 1[32]BIN11.166
rs11136000Clusterin[33,60]CLU0.879
rs3764650ATP-binding cassette, sub-family A (ABC1), member 7[31]ABCA71.229
rs3818361Complement component (3b/4b) receptor 1 (Knops blood group)[60]CR11.174
rs3851179Phosphatidylinositol binding clathrin assembly protein[33]PICALM0.879
rs610932Membrane-spanning 4-domains, subfamily A, member 6A[31]MS4A6A0.904
rs3865444CD33 molecule[20,31]CD330.893
rs670139Membrane-spanning 4-domains, subfamily A, member 4E[31]MS4A4E1.079
rs9296559CD2-associated protein[20,31]CD2AP1.117

Table 1. Late-onset Alzheimer’s disease associated genes/variants.

The dbSNP identification number, gene name, gene abbreviation, and odds ratio for each of the top variants from the Alzgene.org meta-analyses. The SNP in CD2AP, rs9349407, is not present in this sample, so we used rs9296559 instead as a proxy. These two SNPs are close together (1,108 base pairs apart) and in very high LD (r2=1).

Methods

Dataset

We used the Alzheimer’s Disease Genetics Consortium (ADGC) dataset described in Naj et al. [20] for our analyses. Samples were genotyped using Affymetrix and Illumina SNP chips. Quality control of the imputed data was performed as described by Naj et al. 2011 [20]. Briefly, markers with a minor allele frequency of less than 1% and deviation from HWE where P<10-6 were removed. To have a common set of SNPs across all samples, imputation to HapMap phase 2 (release 22)[21] was performed using MaCH [22] and strand ambiguous SNPs were removed, resulting in a rectangular dataset with 2,042,114 SNPs. Only SNPs imputed with R2 ≥ 0.50 were included in the dataset. We added an additional two SNPs, rs7412 and rs429358, corresponding to APOE ε2 and ε4, respectively.

We used a compiled dataset of directly genotyped SNPs common to all 15 studies to assess cryptic relatedness and calculate principal components to account for population-specific variations in allele distribution. We excluded strand ambiguous SNPs, resulting in a rectangular dataset with 21,880 directly observed (not imputed) SNPs in common across all the studies. We filtered SNPs with pairwise LD (r2) < 0.20, resulting in a dataset with 17,054 SNPs. We used both PLINK [23] and KING-ROBUST [24] for relatedness analysis. KING-ROBUST provided unbiased kinship coefficient estimates for related individuals in our dataset. We excluded up to 3rd degree relatives (kinship >= 0.0442) for a final dataset containing 19,692 individuals.

Of the 19,692 individuals in the original dataset we analyzed a subset of 10,922 individuals who had complete data for the 11 markers listed in Table 1, AD case-control status, age, sex, and 10 principal components from the population stratification analysis (missingness rates for each of the covariates and case-control status are reported in Table S1). Basic demographic information for the 10,922 individuals in the subset of the dataset used in this study is presented in Table 2. We collected chromosome length and number of genes per chromosome from the Vega database [25].

CountCases / ControlsAge (Cases / Controls)
Male44892378/211174.8 (73.6 / 76.1)
Female64333330/310375.3 (74.9 / 75.7)
Total109225708/521475.1 (74.3 / 75.9)

Table 2. Demographic information for individuals in the analysis dataset.

We report total individuals, sex, case-control status, and average age for the 10,922 individuals analyzed in this report.

Genetic Analyses

We used Genome-wide Complex Trait Analysis (GCTA)[26], a tool that implements the methods described in Yang et al. [27], Lee et al. [28], and Yang et al. [29] to estimate the phenotypic variance explained by known AD genes and tagged by SNPs on the SNP arrays. Briefly, GCTA uses a mixed linear model and treats the effects of SNPs as random effects, effectively testing all the SNPs together for effect (in contrast to GWAS, which considers each SNP individually). We used age, sex, and 10 principal components as covariates. For the analyses in which we examined unexplained phenotypic variance, we also controlled for the 11 known AD markers (Table 1). The 11 known AD markers are the AlzGene.org top hits and are the markers with replicable evidence for association with AD. Each of these markers is present in our dataset except rs9349407 in CD2AP. As proxy we used rs9296559, which is in very high LD with rs9349407 (r2=1). We specified a population prevalence of LOAD at 0.13 [30].

Ethics Statement

All study procedures were approved by the Institutional Review Boards of Brigham Young University and the University of Washington.

Results

We estimated the variance in AD case-control status focusing first on the 11 known AD markers (Table 1). Together these markers account for 7.8% (standard error 0.03) of the phenotypic variance (Table 3). Next, we estimated the explained phenotypic variance for each chromosome (Figure 1). Chromosome 19 accounts for the highest proportion of phenotypic variance.

SNP SetGenetic Variance (Standard Error)% Phenotypic Variance (Standard Error)
All SNPs0.071 (0.0072)33.12% (0.033)
APOE ε2/ε40.020 (0.0066)5.92% (0.033)
All known AD markers (Table 1)0.025 (0.0066)7.78% (0.033)
All SNPs excluding known AD markers0.046 (0.0060)25.34% (0.033)

Table 3. Summary of genetic and phenotypic variance measurements.

In this table we summarize our results, showing genetic and percent phenotypic variance for four different subsets of SNPs: all SNPs in the ADGC dataset, the two APOE alleles (ε2 and ε4), all known AD markers (as listed in Table 1), and all SNPs excluding those in Table 1.
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Figure 1. Unexplained Alzheimer’s disease variance, by chromosome.

In this figure we show phenotypic variance, by chromosome, explained by all SNPs. Error bars correspond to standard error.

doi:10.1371/journal.pone.0079771.g001

In all, the 2,042,116 SNPs in the HapMap imputed ADGC dataset explain 33.1% of phenotypic variance (genetic variance of 0.0711, standard error 0.0072). The APOE ε2 and ε4 alleles account for 5.9% (standard error 0.03) of the phenotypic variance (Table 3). The other 9 known high frequency SNPs identified in GWAS explain an additional 1.9% (standard error 0.03)(Table 3). After controlling for these 11 markers, an additional 25.3% of the total phenotypic variance (genetic variance of 0.046, standard error 0.006) is explained with as-yet unidentified variants (Table 3). The remaining phenotypic variance explained by each chromosome after controlling for the 11 known markers is shown in Figure 2. SNPs on chromosomes 1, 4, 5 and 17 account for the largest percentage of remaining unexplained phenotypic variance compared to other chromosomes, each accounting for more than 2% (Figure 2). Chromosomes 9, 14, and 21 account for the least (<0.0001% each); however, there is unexplained phenotypic variance on all the autosomes. There is no relationship between explained variance and chromosome length (p-value = 0.8), or number of genes per chromosome (p-value = 0.7).

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Figure 2. Unexplained Alzheimer’s disease variance, by chromosome, excluding known Alzheimer’s disease markers.

This figure is the same as Figure 1 except we have removed variance explained by known Alzheimer’s disease markers. Error bars correspond to standard error.

doi:10.1371/journal.pone.0079771.g002

Discussion

A clear understanding of the genetic architecture of Alzheimer’s disease provides the foundation of information needed to cure this terrible disease. While many large GWAS for AD have been performed and several replicable loci have been identified (as referenced in Table 1), relatively little phenotypic variance is explained by these variants. Our estimates of phenotype variance explained by common genetic variants and by the APOE locus are higher than those of Lee et al. [19]. We estimated total phenotypic variance explained by common SNPs to be ~33%. In contrast Lee et al. [19] estimated ~24%. In our study we used genotyped and HapMap imputed SNPs, whereas Lee et al. [19] used only directly genotyped SNPs. Inclusion of imputed SNPs improved heritability estimates and suggests that imputed SNPs should be included in such studies. In addition to using imputed variants, our dataset was larger and our controls were clinically ascertained. Differences in the estimates for APOE (~6% in this study compared to ~4% in Lee et al. [19]) could be due to these same characteristics as well as the direct genotyping of the APOE ε2 and ε4 alleles in our samples as opposed to the use of proxy markers. Regardless, both studies provide evidence that a considerable amount of variance in AD is explained by yet unidentified genetic variation. Together these results show that there is clearly much work to be done if we are to solve the genetic architecture of AD. GWAS with sample sizes performed to date are able to identify common variants with moderate to small effect sizes. Results of GWAS in AD and other conditions suggest there may be a large number of such variants and that additional variants of this type can be identified by increasing sample sizes. However, additional loci detected with the GWAS strategy will likely have effects either similar to or smaller than SNPs already identified. The range of SNPs identifiable by current GWAS [20,31-36] is marked on Figure 3 by the large box bordered by dots (the GWAS search space), with recent GWAS hits inside the labeled oval. The GWAS being conducted by the International Genomics of Alzheimer’s Project represents a substantial increase in sample size and will undoubtedly identify additional common loci with small effects on AD risk. Nevertheless, it is unlikely that many common variants of even modest effect size remain to be identified.

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Figure 3. Variant search space.

Real and hypothetical variants are graphed by effect size (y-axis) and population frequency (x-axis). Known Alzheimer’s disease SNPs are blue circles and hypothetical SNPs are red circles. The large box on the right outlined with dots, is the GWAS search space and the smaller box on the left, outlined with dashes, is the next-generation sequencing search space. Known Alzheimer’s disease SNPs are those found in Table 1 as well as APP and TREM2, which are both labeled on the graph.

doi:10.1371/journal.pone.0079771.g003

There are still many AD variants that remain to be identified, however, and these variants exist on every autosome (Figure 2). Variants with large effects are almost certainly present in very low frequencies or they would have been identified in GWAS. While such variants are unlikely to be detected using traditional GWAS due to limitations of r2 based “tagging” for alleles with different frequencies [37] the current analysis allows for high D’ values between common alleles and rare variants of large effect to contribute to the explained variance. These rare variants of large effect appear in the smaller box bordered with dashes in Figure 3. To date, identified alleles of this type have clear functional effects and large effect sizes compared to associated alleles from GWAS. Detecting rare variants of large effect requires different experimental designs than GWAS such as sequencing causal loci. Exome chip array studies target known variation in coding regions, even those of very low frequency; this may prove a promising and economical approach. However, accurately genotyping variants of less than 1-2% using these arrays is quite challenging, and for variants that are present below these frequencies other approaches are required.

Two seemingly contradictory hypotheses exist about the architecture of complex disease: the common disease/common variant hypothesis and the multiple rare variant hypothesis. In the first, many common variants of small effect size collectively explain disease risk, while in the second, rare variants, some with large effect and high penetrance, explain disease risk. However, as suggested by Singleton et al. [38] these two hypotheses are not mutually exclusive and the genetics of complex diseases are likely a hybrid of the two. Singleton et al. [38] suggests that both common and rare variants that increase or decrease disease risk are likely to be found in the same loci and coined the phrase “pleomorphic risk loci”. To date, AD genetics research has largely focused on common variants that influence disease risk, likely due to technological and financial constraints. However, the advent of next generation sequencing (NGS) and falling costs of this technology have made it possible to expand AD research to include searching for rare variants. Recently, this technology was used to identify a functional variant that protects against Alzheimer’s disease in the amyloid precursor protein (APP)[17]. Additionally, two groups recently used NGS to identify additional, likely functional, variants associated with AD in the triggering receptor expressed on myeloid 2 (TREM2) gene [16,18]. The TREM2 variant is present in about 1% of the general population and has a high odds ratio (2.9 to 5.1 depending on the dataset). Likewise, the APP variant is extremely rare (frequency of 0.00038), but confers a large protective effect on carriers. Larger scale applications of this technology and careful study design are likely to identify additional variants and further explain the remaining phenotypic variance in AD.

Family-based studies are also an effective application of NGS. These studies require carefully ascertained families and accurate pedigree data and can be used to identify high effect, low frequency variants (located in the box with longer dashes in Figure 3). Family-based studies are especially powerful because large effect, low frequency disease-causing (or disease-modifying) sequence features, some of which may be unique to a single family, are likely to segregate, at least partially, with disease status. These approaches have not yet been extensively applied in AD research. Nevertheless, family-based studies utilizing large-scale genome or exome sequencing have recently been used to identify disease-causing variants in several Mendelian [39-41] and complex disorders [42,43].

It is also possible that gene-gene interactions account for much of the unexplained variance in AD status [44]. These interactions are widespread and common [45,46] and approaches to understand the effects of epistatic interactions exist and continue to mature [44,47]. Several interesting candidate interactions have been identified and Ebbert et al. 2013 (accepted) recently demonstrated that allowing interactions improves the diagnostic utility of the known AD markers. Unfortunately, the complexity of this problem and the extremely large samples sizes required to perform agnostic screens for gene-gene interactions make it very difficult to conduct effective screens for these effects.

AD is a highly complex disease with substantial genetic and environmental components. Our results suggest that genetic variance accounts for ~30% of phenotypic variance, but over 75% of this phenotypic variance remains unexplained by currently identified AD genes. Future AD genetics research must leverage larger samples and novel technologies such as NGS to identify rare, high penetrant variation and gene-gene interactions that are likely to explain the remaining genetic and phenotypic variance in AD.

Genetic research in AD has followed roughly the same model as the study of other complex diseases; largely focusing on the identification of common variants of modest effect using association studies. Scientist in many disease fields have successfully identified numerous associated variants (this is a small representative sample [48-57]). The transition from a focus on common variants to a focus on the identification of low frequency variants is now underway. These rare, functionally relevant markers are often more easily characterized than common variants of small effect. This will lead to strong and testable hypotheses for the development of therapeutics, thus accelerating the progress toward effective prevention and treatment.

Supporting Information

Table S1.

Missingness rates for covariates and case-control status. The Alzheimer’s Disease Genetics Consortium dataset consists of 19,692 total individuals. We removed any individuals missing any of the covariates (listed here) or case-control status (included in this table).

doi:10.1371/journal.pone.0079771.s001

(DOCX)

Acknowledgments

We would like to thank Mark T.W. Ebbert for his insightful comments on the manuscript.

Alzheimer’s Disease Genetics Consortium

Biological samples and associated phenotypic data used in primary data analysis were stored at the Principal Investigator’s institutions, and at the National Cell Repository for Alzheimer’s Disease (NCRAD), at the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania, and the NIA Alzheimer’s Disease Genetics Consortium Data Storage Site at the University of Pennsylvania.

The members of the Alzheimer’s Disease Genetics Consortium are: Marilyn S. Albert1, Roger L. Albin2-4, Liana G. Apostolova5, Steven E. Arnold6, Clinton T. Baldwin7, Robert Barber8, Michael M. Barmada9, Lisa L. Barnes10, 11, Thomas G. Beach12, Gary W. Beecham13, 14, Duane Beekly15, David A. Bennett10, 16, Eileen H. Bigio17, Thomas D. Bird18, Deborah Blacker19,20, Bradley F. Boeve21, James D. Bowen22, Adam Boxer23, James R. Burke24, Joseph D. Buxbaum25, 26, 27, Nigel J. Cairns28, Laura B. Cantwell29, Chuanhai Cao30, Chris S. Carlson31, Regina M. Carney13, Minerva M. Carrasquillo33, Steven L. Carroll34, Helena C. Chui35, David G. Clark36, Jason Corneveaux37, Paul K. Crane38, David H. Cribbs39, Elizabeth A. Crocco40, Carlos Cruchaga41, Philip L. De Jager42,43, Charles DeCarli44, Steven T. DeKosky45, F. Yesim Demirci9, Malcolm Dick46, Dennis W. Dickson33, Ranjan Duara47, Nilufer Ertekin-Taner33,48, Denis Evans49, Kelley M. Faber50, Kenneth B. Fallon34, Martin R. Farlow51, Lindsay A Farrer7,52,76,77,83, Steven Ferris53, Tatiana M. Foroud50, Matthew P. Frosch54, Douglas R. Galasko55, Mary Ganguli56, Marla Gearing57,58, Daniel H. Geschwind59, Bernardino Ghetti60, John R. Gilbert13,14, Sid Gilman2, Jonathan D. Glass61, Alison M. Goate41, Neill R. Graff-Radford33,48, Robert C. Green62, John H. Growdon63, Jonathan L. Haines64, 65, Hakon Hakonarson66, Kara L. Hamilton-Nelson13, Ronald L. Hamilton67, John Hardy68, Lindy E. Harrell36, Elizabeth Head69, Lawrence S. Honig70, Matthew J. Huentelman37, Christine M. Hulette71, Bradley T. Hyman63, Gail P. Jarvik72,73, Gregory A. Jicha74, Lee-Way Jin75, Gyungah Jun7,76,77, M. Ilyas Kamboh9,78, Anna Karydas23, John S.K. Kauwe79, Jeffrey A. Kaye80,81, Ronald Kim82, Edward H. Koo55, Neil W. Kowall83,84, Joel H. Kramer85, Patricia Kramer80,86, Walter A. Kukull87, Frank M. LaFerla88, James J. Lah61, Eric B. Larson38,89, James B. Leverenz90, Allan I. Levey61, Ge Li91, Andrew P. Lieberman92, Chiao-Feng Lin29, Oscar L. Lopez78, Kathryn L. Lunetta76, Constantine G. Lyketsos93, Wendy J. Mack94, Daniel C. Marson36, Eden R. Martin13,14, Frank Martiniuk95, Deborah C. Mash96, Eliezer Masliah55,97, Richard Mayeux70, 109, 110, Wayne C. McCormick38, Susan M. McCurry98, Andrew N. McDavid31, Ann C. McKee83,84, Marsel Mesulam99, Bruce L. Miller23, Carol A. Miller100, Joshua W. Miller75, Thomas J. Montine90, John C. Morris28, 101, Jill R. Murrell50, 60, Amanda J. Myers40, Adam C. Naj13, John M. Olichney44, Vernon S. Pankratz102, Joseph E. Parisi103,104, Margaret A. Pericak-Vance13, 14, Elaine Peskind91, Ronald C. Petersen21, Aimee Pierce39, Wayne W. Poon46, Huntington Potter30, Joseph F. Quinn80, Ashok Raj30, Murray Raskind91, Eric M. Reiman37,105-107, Barry Reisberg53,108, Christiane Reitz70,109,110, John M. Ringman5, Erik D. Roberson36, Ekaterina Rogaeva111, Howard J. Rosen23, Roger N. Rosenberg112, Mary Sano26, Andrew J. Saykin50,113, Gerard D. Schellenberg29, Julie A. Schneider10,114, Lon S. Schneider35,115, William W. Seeley23, Amanda G. Smith30, Joshua A. Sonnen90, Salvatore Spina60, Peter St George-Hyslop111,116, Robert A. Stern83, Rudolph E. Tanzi63, John Q. Trojanowski29, Juan C. Troncoso117, Debby W. Tsuang91, Otto Valladares29, Vivianna M. Van Deerlin29, Linda J. Van Eldik118, Badri N. Vardarajan7, Harry V. Vinters5,119, Jean Paul Vonsattel120, Li-San Wang29, Sandra Weintraub99, Kathleen A. Welsh-Bohmer24, 121, Jennifer Williamson70, Randall L. Woltjer122, Clinton B. Wright123, Steven G. Younkin33, Chang-En Yu38, Lei Yu10

1Department of Neurology, Johns Hopkins University, Baltimore, Maryland, 2Department of Neurology, University of Michigan, Ann Arbor, Michigan, 3Geriatric Research, Education and Clinical Center (GRECC), VA Ann Arbor Healthcare System (VAAAHS), Ann Arbor, Michigan, 4Michigan Alzheimer Disease Center, Ann Arbor, Michigan, 5Department of Neurology, University of California Los Angeles, Los Angeles, California, 6Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, 7Department of Medicine (Genetics Program), Boston University, Boston, Massachusetts, 8Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, Texas, 9Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, 10Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, 11Department of Behavioral Sciences, Rush University Medical Center, Chicago, Illinois, 12Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona, 13The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, 14Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, 15National Alzheimer's Coordinating Center, University of Washington, Seattle, Washington, 16Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, 17Department of Pathology, Northwestern University, Chicago, Illinois, 18Department of Neurology, University of Washington, Seattle, Washington, 19Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, 20Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, 21Department of Neurology, Mayo Clinic, Rochester, Minnesota, 22Swedish Medical Center, Seattle, Washington, 23Department of Neurology, University of California San Francisco, San Francisco, California, 24Department of Medicine, Duke University, Durham, North Carolina, 25Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, 26Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, 27Departments of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, 28Department of Pathology and Immunology, Washington University, St. Louis, Missouri, 29Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, 30USF Health Byrd Alzheimer's Institute, University of South Florida, Tampa, Florida, 31Fred Hutchinson Cancer Research Center, Seattle, Washington, 32Department of Psychiatry, Vanderbilt University, Nashville, Tennessee, 33Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, 34Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, 35Department of Neurology, University of Southern California, Los Angeles, California, 36Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, 37Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, 38Department of Medicine, University of Washington, Seattle, Washington, 39Department of Neurology, University of California Irvine, Irvine, California, 40Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, Florida, 41Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, Missouri, 42Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Department of Neurology & Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, 43Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, 44Department of Neurology, University of California Davis, Sacramento, California, 45University of Virginia School of Medicine, Charlottesville, Virginia, 46Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, California, 47Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida, 48Department of Neurology, Mayo Clinic, Jacksonville, Florida, 49Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, 50Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, 51Department of Neurology, Indiana University, Indianapolis, Indiana, 52Department of Epidemiology, Boston University, Boston, Massachusetts, 53Department of Psychiatry, New York University, New York, New York, 54C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts, 55Department of Neurosciences, University of California San Diego, La Jolla, California, 56Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, 57Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, 58Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia, 59Neurogenetics Program, University of California Los Angeles, Los Angeles, California, 60Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, Indiana, 61Department of Neurology, Emory University, Atlanta, Georgia, 62Division of Genetics, Department of Medicine and Partners Center for Personalized Genetic Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, 63Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, 64Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, 65Vanderbilt Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, 66Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, 67Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania, 68Institute of Neurology, University College London, Queen Square, London, 69Sanders-Brown Center on Aging, Department of Molecular and Biomedical Pharmacology, University of Kentucky, Lexington, Kentucky, 70Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Neurology, Columbia University, New York, New York, 71Department of Pathology, Duke University, Durham, North Carolina, 72Department of Genome Sciences, University of Washington, Seattle, Washington, 73Department of Medicine (Medical Genetics), University of Washington, Seattle, Washington, 74Sanders-Brown Center on Aging, Department Neurology, University of Kentucky, Lexington, Kentucky, 75Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, 76Department of Biostatistics, Boston University, Boston, Massachusetts, 77Department of Ophthalmology, Boston University, Boston, Massachusetts, 78University of Pittsburgh Alzheimer's Disease Research Center, Pittsburgh, Pennsylvania, 79Department of Biology, Brigham Young University, Provo, Utah, 80Department of Neurology, Oregon Health & Science University, Portland, Oregon, 81Department of Neurology, Portland Veterans Affairs Medical Center, Portland, Oregon, 82Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California, 83Department of Neurology, Boston University, Boston, Massachusetts, 84Department of Pathology, Boston University, Boston, Massachusetts, 85Department of Neuropsychology, University of California San Francisco, San Francisco, California, 86Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, 87Department of Epidemiology, University of Washington, Seattle, Washington, 88Department of Neurobiology and Behavior, University of California Irvine, Irvine, California, 89Group Health Research Institute, Group Health, Seattle, Washington, 90Department of Pathology, University of Washington, Seattle, Washington, 91Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, 92Department of Pathology, University of Michigan, Ann Arbor, Michigan, 93Department of Psychiatry, Johns Hopkins University, Baltimore, Maryland, 94Department of Preventive Medicine, University of Southern California, Los Angeles, California, 95Department of Medicine - Pulmonary, New York University, New York, New York, 96Department of Neurology, University of Miami, Miami, Florida, 97Department of Pathology, University of California San Diego, La Jolla, California, 98School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, Washington, 99Cognitive Neurology and Alzheimer's Disease Center, Northwestern University, Chicago, Illinois, 100Department of Pathology, University of Southern California, Los Angeles, California, 101Department of Neurology, Washington University, St. Louis, Missouri, 102Department of Biostatistics, Mayo Clinic, Rochester, Minnesota, 103Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota, 104Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, 105Arizona Alzheimer’s Consortium, Phoenix, Arizona, 106Department of Psychiatry, University of Arizona, Phoenix, Arizona, 107Banner Alzheimer's Institute, Phoenix, Arizona, 108Alzheimer's Disease Center, New York University, New York, New York, 109Gertrude H. Sergievsky Center, Columbia University, New York, New York, 110Department of Neurology, Columbia University, New York, New York, 111Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, 112Department of Neurology, University of Texas Southwestern, Dallas, Texas, 113Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, 114Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, Illinois, 115Department of Psychiatry, University of Southern California, Los Angeles, California, 116Cambridge Institute for Medical Research and Department of Clinical Neurosciences, University of Cambridge, Cambridge, 117Department of Pathology, Johns Hopkins University, Baltimore, Maryland, 118Sanders-Brown Center on Aging, Department of Anatomy and Neurobiology, University of Kentucky, Lexington, Kentucky, 119Department of Pathology & Laboratory Medicine, University of California Los Angeles, Los Angeles, California, 120Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Pathology, Columbia University, New York, New York, 121Department of Psychiatry & Behavioral Sciences, Duke University, Durham, North Carolina, 122Department of Pathology, Oregon Health & Science University, Portland, Oregon, 123Evelyn F. McKnight Brain Institute, Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida

Author Contributions

Conceived and designed the experiments: PGR JSKK. Performed the experiments: PGR. Analyzed the data: PGR SM PC JSKK. Contributed reagents/materials/analysis tools: SM ADGC. Wrote the manuscript: PGR JSKK.

References

  1. 1. Querfurth HW, LaFerla FM (2010) Alzheimer's disease. N Engl J Med 362: 329-344. doi:10.1056/NEJMra0909142. PubMed: 20107219.
  2. 2. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM (2007) Forecasting the global burden of Alzheimer's disease. Alzheimers Dement 3: 186-191. doi:10.1016/j.jalz.2007.04.381. PubMed: 19595937.
  3. 3. Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D et al. (2011) Alzheimer's disease. Lancet. doi: 10.1016/s0140-6736(10)61349-9
  4. 4. Herrup K (2010) Reimagining Alzheimer's disease--an age-based hypothesis. J Neurosci 30: 16755-16762. doi:10.1523/JNEUROSCI.4521-10.2010. PubMed: 21159946.
  5. 5. Patterson C, Feightner JW, Garcia A, Hsiung GY, MacKnight C et al. (2008) Diagnosis and treatment of dementia: 1. Risk assessment and primary prevention of Alzheimer disease. CMAJ 178: 548-556. doi:10.1503/cmaj.070796. PubMed: 18299540.
  6. 6. Cataldo JK, Prochaska JJ, Glantz SA (2010) Cigarette smoking is a risk factor for Alzheimer's Disease: an analysis controlling for tobacco industry affiliation. J Alzheimers Dis 19: 465-480. PubMed: 20110594.
  7. 7. Almeida OP, Hulse GK, Lawrence D, Flicker L (2002) Smoking as a risk factor for Alzheimer's disease: contrasting evidence from a systematic review of case-control and cohort studies. Addiction 97: 15-28. doi:10.1046/j.1360-0443.2002.00016.x. PubMed: 11895267.
  8. 8. Lindsay J, Laurin D, Verreault R, Hébert R, Helliwell B et al. (2002) Risk factors for Alzheimer's disease: a prospective analysis from the Canadian Study of Health and Aging. Am J Epidemiol 156: 445-453. doi:10.1093/aje/kwf074. PubMed: 12196314.
  9. 9. Podewils LJ, Guallar E, Kuller LH, Fried LP, Lopez OL et al. (2005) Physical activity, APOE genotype, and dementia risk: findings from the Cardiovascular Health Cognition Study. Am J Epidemiol 161: 639-651. doi:10.1093/aje/kwi092. PubMed: 15781953.
  10. 10. Wang HX, Karp A, Winblad B, Fratiglioni L (2002) Late-life engagement in social and leisure activities is associated with a decreased risk of dementia: a longitudinal study from the Kungsholmen project. Am J Epidemiol 155: 1081-1087. doi:10.1093/aje/155.12.1081. PubMed: 12048221.
  11. 11. Scarmeas N, Stern Y, Mayeux R, Luchsinger JA (2006) Mediterranean diet, Alzheimer disease, and vascular mediation. Arch Neurol 63: 1709-1717. doi:10.1001/archneur.63.12.noc60109. PubMed: 17030648.
  12. 12. Patterson C, Feightner J, Garcia A, MacKnight C (2007) General risk factors for dementia: a systematic evidence review. Alzheimers Dement 3: 341-347. doi:10.1016/j.jalz.2007.07.001. PubMed: 19595956.
  13. 13. Goate A, Chartier-Harlin MC, Mullan M, Brown J, Crawford F et al. (1991) Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer's disease. Nature 349: 704-706. doi:10.1038/349704a0. PubMed: 1671712.
  14. 14. Sherrington R, Rogaev EI, Liang Y, Rogaeva EA, Levesque G et al. (1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer's disease. Nature 375: 754-760. doi:10.1038/375754a0. PubMed: 7596406.
  15. 15. Levy-Lahad E, Wasco W, Poorkaj P, Romano DM, Oshima J et al. (1995) Candidate gene for the chromosome 1 familial Alzheimer's disease locus. Science 269: 973-977. doi:10.1126/science.7638622. PubMed: 7638622.
  16. 16. Guerreiro R, Wojtas A, Bras J, Carrasquillo M, Rogaeva E et al. (2012) TREM2 Variants in Alzheimer's Disease. N Engl J Med, 368: 117–27. PubMed: 23150934.
  17. 17. Jonsson T, Atwal JK, Steinberg S, Snaedal J, Jonsson PV et al. (2012) A mutation in APP protects against Alzheimer's disease and age-related cognitive decline. Nature 488: 96-99. doi:10.1038/nature11283. PubMed: 22801501.
  18. 18. Jonsson T, Stefansson H, Ph DS; Jonsdottir I, Jonsson PV et al. (2012) Variant of TREM2 Associated with the Risk of Alzheimer's Disease. N Engl J Med, 368: 107–16. PubMed: 23150908. PubMed: 23150908.
  19. 19. Lee SH, Harold D, Nyholt DR, Goddard ME, Zondervan KT et al. (2013) Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis. Hum Mol Genet 22: 832-841. doi:10.1093/hmg/dds491. PubMed: 23193196.
  20. 20. Naj AC, Jun G, Beecham GW, Wang LS, Vardarajan BN et al. (2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat Genet 43: 436-441. doi:10.1038/ng.801. PubMed: 21460841.
  21. 21. International HapMap Consortium (2003) The International HapMap Project. Nature 426: 789-796. doi:10.1038/nature02168. PubMed: 14685227.
  22. 22. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816-834. doi:10.1002/gepi.20533. PubMed: 21058334.
  23. 23. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559-575. doi:10.1086/519795. PubMed: 17701901.
  24. 24. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M et al. (2010) Robust relationship inference in genome-wide association studies. Bioinformatics 26: 2867-2873. doi:10.1093/bioinformatics/btq559. PubMed: 20926424.
  25. 25. Wilming LG, Gilbert JG, Howe K, Trevanion S, Hubbard T et al. (2008) The vertebrate genome annotation (Vega) database. Nucleic Acids Res 36: D753-D760. PubMed: 18003653.
  26. 26. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88: 76-82. doi:10.1016/j.ajhg.2010.11.011. PubMed: 21167468.
  27. 27. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565-569. doi:10.1038/ng.608. PubMed: 20562875.
  28. 28. Lee SH, Wray NR, Goddard ME, Visscher PM (2011) Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet 88: 294-305. doi:10.1016/j.ajhg.2011.02.002. PubMed: 21376301.
  29. 29. Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N et al. (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43: 519-525. doi:10.1038/ng.823. PubMed: 21552263.
  30. 30. Association; As (2012) lzheimer's Association Annual Report: Alzheimer's disease Facts and Figures.
  31. 31. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC et al. (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease. Nat Genet 43: 429-435. doi:10.1038/ng.803. PubMed: 21460840.
  32. 32. Biffi A, Anderson CD, Desikan RS, Sabuncu M, Cortellini L et al. (2010) Genetic variation and neuroimaging measures in Alzheimer disease. Arch Neurol 67: 677-685. doi:10.1001/archneurol.2010.108. PubMed: 20558387.
  33. 33. Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A et al. (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet 41: 1088-1093. doi:10.1038/ng.440. PubMed: 19734902.
  34. 34. Hu X, Pickering E, Liu YC, Hall S, Fournier H et al. (2011) Meta-analysis for genome-wide association study identifies multiple variants at the BIN1 locus associated with late-onset Alzheimer's disease. PLOS ONE 6: e16616. doi:10.1371/journal.pone.0016616. PubMed: 21390209.
  35. 35. Carrasquillo MM, Belbin O, Hunter TA, Ma L, Bisceglio GD et al. (2010) Replication of CLU, CR1, and PICALM associations with alzheimer disease. Arch Neurol 67: 961-964. doi:10.1001/archneurol.2010.147. PubMed: 20554627.
  36. 36. Corneveaux JJ, Myers AJ, Allen AN, Pruzin JJ, Ramirez M et al. (2010) Association of CR1, CLU and PICALM with Alzheimer's disease in a cohort of clinically characterized and neuropathologically verified individuals. Hum Mol Genet 19: 3295-3301. doi:10.1093/hmg/ddq221. PubMed: 20534741.
  37. 37. Wray NR (2005) Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin Res Hum Genet Off J International Society For Twin Studies 8: 87-94. doi:10.1375/twin.8.2.87. PubMed: 15901470.
  38. 38. Singleton A, Hardy J (2011) A generalizable hypothesis for the genetic architecture of disease: pleomorphic risk loci. Hum Mol Genet 20: R158-R162. doi:10.1093/hmg/ddr358. PubMed: 21875901.
  39. 39. Rope AF, Wang K, Evjenth R, Xing J, Johnston JJ et al. (2011) Using VAAST to identify an X-linked disorder resulting in lethality in male infants due to N-terminal acetyltransferase deficiency. Am J Hum Genet 89: 28-43. doi:10.1016/j.ajhg.2011.05.017. PubMed: 21700266.
  40. 40. Choi M, Scholl UI, Ji W, Liu T, Tikhonova IR et al. (2009) Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci U S A 106: 19096-19101. doi:10.1073/pnas.0910672106. PubMed: 19861545.
  41. 41. Roach JC, Glusman G, Smit AF, Huff CD, Hubley R et al. (2010) Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328: 636-639. doi:10.1126/science.1186802. PubMed: 20220176.
  42. 42. Krebs CE, Paisan-Ruiz C (2012) The use of next-generation sequencing in movement disorders. Front Genet 3: 75. PubMed: 22593763.
  43. 43. Kilpinen H, Barrett JC (2013) How next-generation sequencing is transforming complex disease genetics. Trends Genet TIG 29: 23-30. doi:10.1016/j.tig.2012.10.001. PubMed: 23103023.
  44. 44. Turton JC, Bullock J, Medway C, Shi H, Brown K et al. (2011) Investigating statistical epistasis in complex disorders. J Alzheimers Dis JAD 25: 635-644. PubMed: 21483092.
  45. 45. Mackay TF (2004) The genetic architecture of quantitative traits: lessons from Drosophila. Curr Opin Genet Dev 14: 253-257. doi:10.1016/j.gde.2004.04.003. PubMed: 15172667.
  46. 46. Shao H, Burrage LC, Sinasac DS, Hill AE, Ernest SR et al. (2008) Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc Natl Acad Sci U S A 105: 19910-19914. doi:10.1073/pnas.0810388105. PubMed: 19066216.
  47. 47. Combarros O, Cortina-Borja M, Smith AD, Lehmann DJ (2009) Epistasis in sporadic Alzheimer's disease. Neurobiol Aging 30: 1333-1349. doi:10.1016/j.neurobiolaging.2007.11.027. PubMed: 18206267.
  48. 48. Ferreira MA, Matheson MC, Duffy DL, Marks GB, Hui J et al. (2011) Identification of IL6R and chromosome 11q13.5 as risk loci for asthma. Lancet 378: 1006-1014. doi:10.1016/S0140-6736(11)60874-X. PubMed: 21907864.
  49. 49. Wain LV, Verwoert GC, O'Reilly PF, Shi G, Johnson T et al. (2011) Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat Genet 43: 1005-1011. doi:10.1038/ng.922. PubMed: 21909110.
  50. 50. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP et al. (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491: 119-124. doi:10.1038/nature11582. PubMed: 23128233.
  51. 51. Lill CM, Roehr JT, McQueen MB, Kavvoura FK, Bagade S et al. (2012) Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: The PDGene database. PLOS Genet 8: e1002548. PubMed: 22438815.
  52. 52. Wen W, Cho YS, Zheng W, Dorajoo R, Kato N et al. (2012) Meta-analysis identifies common variants associated with body mass index in east Asians. Nat Genet 44: 307-311. doi:10.1038/ng.1087. PubMed: 22344219.
  53. 53. Haiman CA, Chen GK, Vachon CM, Canzian F, Dunning A et al. (2011) A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet 43: 1210-1214. doi:10.1038/ng.985. PubMed: 22037553.
  54. 54. Psychiatric GWAS Consortium Bipolar Disorder Working Group (2011) Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 43: 977-983. doi:10.1038/ng.943. PubMed: 21926972.
  55. 55. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD et al. (2011) Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478: 103-109. doi:10.1038/nature10405. PubMed: 21909115.
  56. 56. Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C et al. (2012) A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet 44: 526-531. doi:10.1038/ng.2247. PubMed: 22484627.
  57. 57. Jenuth JP (2000) The NCBI. Publicly available tools and resources on the Web. Methods Mol Biol 132: 301-312. PubMed: 10547843.
  58. 58. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC et al. (1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 261: 921-923. doi:10.1126/science.8346443. PubMed: 8346443.
  59. 59. Corder EH, Saunders AM, Risch NJ, Strittmatter WJ, Schmechel DE et al. (1994) Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat Genet 7: 180-184. doi:10.1038/ng0694-180. PubMed: 7920638.
  60. 60. Lambert JC, Heath S, Even G, Campion D, Sleegers K et al. (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat Genet 41: 1094-1099. doi:10.1038/ng.439. PubMed: 19734903.