Genotype Imputation and Association Analysis of Alzheimer's Disease
After quality control criteria were finalized for each individual and each sample collection (SNPs with call rates of <95% were excluded; Supplementary Note), IMPUTE2 (ref. 42) or MaCH/Minimac43 (link) software (Supplementary Table 2) was used to impute the genotypes of all participants with haplotypes derived from samples of European ancestry in the 1000 Genome Project (2010 interim release based on the sequence data freeze from 4 August 2010 and phased haplotypes from December 2010). In each data set, SNPs with R2 or info score quality estimates of less than 0.3, as indicated by MaCH or IMPUTE2, respectively (with these two quality estimates described to be equivalent), were excluded from analyses. Similarly, SNPs with a MAF of <1% were also excluded. After these procedures, a maximum of 8,133,148 SNPs were retained that were present in at least 1 data set. In each case-control data set, the association of LOAD with genotype dosage was analyzed by a logistic regression model including covariates for age, sex and principal components to adjust for possible population stratification (Supplementary Table 2). For the three CHARGE cohorts with incident Alzheimer’s disease data, Cox proportional hazards models were used. The four consortia used different but analogous software for these analyses (PLINK44 (link), SNPTEST45 (link), ProbABEL46 or R; Supplementary Table 2). Three of these tools were applied to the EADI data set for quality control, and very similar results were observed. After the exclusion of SNPs showing logistic regression coefficient |β| > 5 or P value equal to 0 or 1, the maximum number of SNPs in any data set was 8,131,643. Each consortium uploaded summarized results for each SNP to an internal I-GAP website for access by members of each consortium. SNPs genotyped or imputed in at least 40% of Alzheimer’s disease cases and 40% of control samples were included in the meta-analysis. This threshold represented the best compromise between maximizing the total number of SNPs and maximizing the number of samples in which the given SNP was present. Indeed, analyzing all SNPs available in at least one study could have greatly increased the risk of false positives. On the other hand, studying SNPs only present in all studies could have led to the removal of SNPs of potential interest, even if those SNPs could have reached adequate statistical power in a more limited number of data sets (false negatives). This approach allowed us to increase homogeneity between studies for some SNPs by excluding poor quality data present only in a limited number of data sets of small size. This last selection step led to a final number of 7,055,881 SNPs in stage 1 analysis.
Partial Protocol Preview
This section provides a glimpse into the protocol. The remaining content is hidden due to licensing restrictions, but the full text is available at the following link:
Access Free Full Text.
Lambert J.C., Ibrahim-Verbaas C.A., Harold D., Naj A.C., Sims R., Bellenguez C., Jun G., DeStefano A.L., Bis J.C., Beecham G.W., Grenier-Boley B., Russo G., Thornton-Wells T.A., Jones N., Smith A.V., Chouraki V., Thomas C., Ikram M.A., Zelenika D., Vardarajan B.N., Kamatani Y., Lin C.F., Gerrish A., Schmidt H., Kunkle B., Dunstan M.L., Ruiz A., Bihoreau M.T., Choi S.H., Reitz C., Pasquier F., Hollingworth P., Ramirez A., Hanon O., Fitzpatrick A.L., Buxbaum J.D., Campion D., Crane P.K., Baldwin C., Becker T., Gudnason V., Cruchaga C., Craig D., Amin N., Berr C., Lopez O.L., De Jager P.L., Deramecourt V., Johnston J.A., Evans D., Lovestone S., Letenneur L., Morón F.J., Rubinsztein D.C., Eiriksdottir G., Sleegers K., Goate A.M., Fiévet N., Huentelman M.J., Gill M., Brown K., Kamboh M.I., Keller L., Barberger-Gateau P., McGuinness B., Larson E.B., Green R., Myers A.J., Dufouil C., Todd S., Wallon D., Love S., Rogaeva E., Gallacher J., St George-Hyslop P., Clarimon J., Lleo A., Bayer A., Tsuang D.W., Yu L., Tsolaki M., Bossù P., Spalletta G., Proitsi P., Collinge J., Sorbi S., Sanchez-Garcia F., Fox N.C., Hardy J., Deniz Naranjo M.C., Bosco P., Clarke R., Brayne C., Galimberti D., Mancuso M., Matthews F., Moebus S., Mecocci P., Zompo M.D., Maier W., Hampel H., Pilotto A., Bullido M., Panza F., Caffarra P., Nacmias B., Gilbert J.R., Mayhaus M., Lannfelt L., Hakonarson H., Pichler S., Carrasquillo M.M., Ingelsson M., Beekly D., Alvarez V., Zou F., Valladares O., Younkin S.G., Coto E., Hamilton-Nelson K.L., Gu W., Razquin C., Pastor P., Mateo I., Owen M.J., Faber K.M., Jonsson P.V., Combarros O., O’Donovan M.C., Cantwell L.B., Soininen H., Blacker D., Mead S., Mosley TH J.r., Bennett D.A., Harris T.B., Fratiglioni L., Holmes C., de Bruijn R.F., Passmore P., Montine T.J., Bettens K., Rotter J.I., Brice A., Morgan K., Foroud T.M., Kukull W.A., Hannequin D., Powell J.F., Nalls M.A., Ritchie K., Lunetta K.L., Kauwe J.S., Boerwinkle E., Riemenschneider M., Boada M., Hiltunen M., Martin E.R., Schmidt R., Rujescu D., Wang L.S., Dartigues J.F., Mayeux R., Tzourio C., Hofman A., Nöthen M.M., Graff C., Psaty B.M., Jones L., Haines J.L., Holmans P.A., Lathrop M., Pericak-Vance M.A., Launer L.J., Farrer L.A., van Duijn C.M., Van Broeckhoven C., Moskvina V., Seshadri S., Williams J., Schellenberg G.D, & Amouyel P. (2013). Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nature genetics, 45(12), 1452-1458.
Publication 2013
Alzheimer disease European FreezeGenomeHaplotypes Mach Sample collection Snps
Corresponding Organization : Centre Hospitalier Universitaire de Lille
Other organizations :
Inserm, Erasmus MC, Cardiff University, Medical Research Council, University of Pennsylvania, Université de Lille, Boston University, University of Washington, Dr. John T. Macdonald Foundation, University of Miami, Institut Pasteur de Lille, University of Zurich, Vanderbilt University, University of Iceland, Fondation Jean Dausset-CEPH, Medical University of Graz, Fundació ACE, Columbia University, University of Bonn, Université Paris Cité, Icahn School of Medicine at Mount Sinai, Université de Rouen Normandie, German Center for Neurodegenerative Diseases, Washington University in St. Louis, Queen's University Belfast, University of Pittsburgh, Brigham and Women's Hospital, Harvard University, Rush University Medical Center, King's College London, University of Cambridge, Icelandic Heart Association, VIB-UAntwerp Center for Molecular Neurology, Translational Genomics Research Institute, Trinity College Dublin, University of Nottingham, Queen's Medical Centre, Karolinska Institutet, Stockholm University, Frenchay Hospital, University of Bristol, Occupational Cancer Research Centre, University of Toronto, University Hospital of Wales, Universitat Autònoma de Barcelona, Hospital de Sant Pau, VA Puget Sound Health Care System, Geriatric Research Education and Clinical Center, Aristotle University of Thessaloniki, Fondazione Santa Lucia, MRC Prion Unit, University College London, University of Florence, Hospital Universitario de Gran Canaria Doctor Negrín, UK Dementia Research Institute, Oasi Maria SS, Istituti di Ricovero e Cura a Carattere Scientifico, University of Oxford, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, University of Pisa, MRC Biostatistics Unit, Institut für Medizinische Informatik, Biometrie und Epidemiologie, University of Perugia, University of Cagliari, Goethe University Frankfurt, Casa Sollievo della Sofferenza, Centro de Biología Molecular Severo Ochoa, Universidad Autónoma de Madrid, Consejo Superior de Investigaciones Científicas, University of Bari Aldo Moro, University of Parma, Saarland University, Uppsala University, Children's Hospital of Philadelphia, Jacksonville College, Mayo Clinic in Florida, Central University Hospital of Asturias, Universidad de Navarra, Instituto de Investigación Marqués de Valdecilla, Universidad de Cantabria, Indiana University – Purdue University Indianapolis, University of Eastern Finland, University of Mississippi Medical Center, University of Southampton, Netherlands Consortium for Healthy Ageing, Cedars-Sinai Medical Center, Institut du Cerveau, Sorbonne Université, Centre National de la Recherche Scientifique, National Institute on Aging, Brigham Young University, Baylor Genetics, Baylor College of Medicine, Life & Brain (Germany), Group Health Cooperative, Kaiser Permanente Washington Health Research Institute, Center for Human Genetics, McGill University and Génome Québec Innovation Centre, Centre for Medical Systems Biology, University of Antwerp
Principal components to adjust for possible population stratification
Annotations
Based on most similar protocols
Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.
As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to
get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
