B1001 - EAGLE consortiums Internalizing Behaviour GWAS meta-analysis - 25/05/2010

B number: 
B1001
Principal applicant name: 
Dr Henning Tiemeier (Erasmus University Medical Center, Rottterdam, the Netherlands, Europe)
Co-applicants: 
Fleur Velders (Not used 0, Not used 0), Camilla Stoltenberg (Not used 0, Not used 0), Dr Beate Glaser (Not used 0, Not used 0)
Title of project: 
EAGLE consortium's Internalizing Behaviour GWAS meta-analysis
Proposal summary: 

Concept: Measurement Person Source Time points

Internalizing behaviour SDQ emotional symptoms mother report Questionnaire 4-13yrs.

continuous measurement

SMFQ mother report Questionnaire 10-16yrs.

continuous measurement

EAGLE-WITHIN-COHORT-ANALYSIS-PLAN-Internalizingbehaviour-170310

In this document:

1. Traits of interest

2. Participating studies

3. Number of subjects

4. Genotyping + Imputation

5. Model of association.

6. Data exchange

7. Analysis plan

8. Secondary analysis

9. Proposed deadline

1. Traits of interest

First round: Internalizing behaviour at preschool age (0-7yrs.) continuous measure (int_cont_preschool)

Second round (data added to first round): Internalizing behaviour at school age (8-17yrs.) continuous measure (int_cont_school)

2. Participating studies (as of 10-09-2009)

Fleur Velders, Henning Tiemeier (Generation R),

Joachim Heinrich (LISA)

Beate Glaser, George Davey Smith (Alspac),

Elena Hyponen, Chris Power (1958BC),

Sandra Louis, Lyell Palmer (RAINE),

Pal Suren, Camilla Stoltenberg (MOBA).

3. Projected number of subjects:

4. Genotyping + Imputation

* Genotyped SNPs (Affymetrix, Illumina, Perlegen)

* Imputation HapMap Phase II CEU SNPs. Preferred release 22 of HapMap, build 36. Predefined marker filters to apply before imputation (HWEPgreater than 10-6, MAFgreater than 0.01, SNP-callgreater than 95%). Suggestion left open to cohorts to apply specific filters but should be reported.

* Analyse all SNPs, no filtering on call rate/HWE/MAF/imputation quality (QC metrics to be reported, and filtering will be done at meta-analysis stage)

5. Model of association

* Additive model (SNP coded as allele dose from 0 to 2), which accounts for genotype imputation uncertainty by use of linear regression onto estimated dose (as included in MACHQTL, ProbABEL, SNPTEST), adjusting for population structure and covariates.

6. Data exchange

* See separate RESULTS_FORMAT file for details of results file formatting and file naming.

* Summary statistics to be uploaded to the AIMS system (available?) (link will be provided)

* Only summary statistics will be transferred, not individual level genotype or phenotype data.

7. Analysis outline

* First round: int_cont_preschool

* Second reound: int_cont_school all items:

Internalizing behaviour:

CBCL: broad band scale internalizing problems

SDQ: subscales internalizing behaviour

Rutter: subscales internalizing behaviour

* how to deal with missings? Suggestions:

o allow 25% mssings per scale

o minimum of 10 items per scale

o cohort specific

* Transformation of outcome variable:

o intbeh Z-score = calculate a Z-score

= standardised score

= [value-mean]/standard deviation

(based on the mean and SD internalizing problems score in the sample)

* Model:

o Z-score = SNP + sex + age

- SNP = child genotype

- Sex: coded 1 for male, 2 for female

- Age: at measurement

* Exclusions:

o One of sibling twins or correction

o Non-Caucasian

8. Secondary analyses (for later discussion)

* Dichotomous measurements

9. Deadline

* The proposed deadline for uploading = end of May 2010.

Date proposal received: 
Tuesday, 25 May, 2010
Date proposal approved: 
Tuesday, 25 May, 2010
Keywords: 
Depression, Genetics, Mental Health
Primary keyword: