Genome Analysis Wiki - User contributions [en]

RAREMETAL Documentation

2014-01-23T15:15:52Z

Svrieze: /* TABLES */

== Useful Wiki Pages ==

There are several pages in this Wiki that may be useful to RAREMETAL users. Here are links to key pages:

* The [[RAREMETAL|RAREMETAL Home Page]]

* The [[Tutorial:_RAREMETAL|RAREMETAL Quick Start Tutorial]]

* The [[RAREMETAL FAQ]]

* The [[RAREMETAL Command Reference]]

* The [[RAREMETALWORKER|RAREMETALWORKER documentation]]

The [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''] tool for rare-variant association analysis can also generate output compatible with RAREMETAL.

== Key Features ==
'''RAREMETAL''' has the following features:
* '''RAREMETAL''' performs gene-based or region-based meta analysis using Burden tests with the following methods: CMC_counts, Madsen-Browning, SKAT, and Variable Threshold.
* '''RAREMETAL''' performs single variant metal-analysis by default.
* '''RAREMETAL''' allows customized groups of variants to be tested.
* '''RAREMETAL''' allows conditional analysis to be performed in both gene-level meta-analysis and single variants meta-analysis.
* '''RAREMETAL''' generate QQ plots and manhattan plots by default.

== Brief Description ==

'''RAREMETAL''' is a computationally efficient tool for meta-analysis of rare variants using sequencing or genotyping array data. '''RAREMETAL''' takes summary statistics and LD matrices generated by [[Rare-Metal-Worker|'''RAREMETALWORKER''']] or [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''], handles related and unrelated individuals, and supports both single variant and burden meta-analysis. '''RAREMETAL''' generates high quality plots by default and has options that allow users to build reports at different levels.

'''RAREMETAL''' is developed by Shuang Feng, Dajiang Liu and Gonçalo Abecasis. A R-package written by Dajiang Liu using the same methodology is [[RareMetals|'''available''']].

== Approach ==

The key idea behind meta-analysis with RAREMETAL is that various gene-level test statistics can be reconstructed from single variant score statistics and that, when the linkage disequilibrium relationships between variants are known, the distribution of these gene-level statistics can be derived and used to evaluate signifi-cance. Single variant statistics are calculated using the Cochran-Mantel-Haenszel method. The main formulae are tabulated in the following:

{| border="1" cellpadding="5" cellspacing="0" align="center"
|+'''Formulae for RAREMETAL'''
! scope="col" width="120pt" | Test
! scope="col" width="50pt" | Statistics
! scope="col" width="225pt" | Null Distribution
! scope="col" width="225pt" | Notation
|-
| Single Variant || <math>T=\sum_{i=1}^n {U_i}\bigg/\sqrt{\sum_{i=1}^n{V_i}}</math> || <math>T\sim\mathbf{N}(0,1)</math> ||<math> U_i \text{ is the score statistic from study }i;</math><math> V_i \text{ is the variance of } U_i.</math>
|-
| un-weighted Burden || <math>T_b=\sum_{i=1}^n{\mathbf{U_i}}\Big/\sqrt{\sum_{i=1}^n{\mathbf{V_i}}}</math> || <math>T_b\sim\mathbf{N}(0,1)</math> ||<math> \mathbf{U_i}\text{ is the vector of score statistics from study }i, or </math> <math> \mathbf{U_i}=\{U_{i1},...,U_{im}\};</math> <math>\mathbf{V_i} \text{ is the covariance of } \mathbf{U_i}.</math>
|-
| Weighted Burden || <math>T_{wb}=\mathbf{w^T}\sum_{i=1}^n{\mathbf{U_i}}\bigg/\sqrt{\mathbf{w^T}\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\mathbf{w}}</math> || <math>T_{wb}\sim\mathbf{N}(0,1)</math> || <math> \mathbf{w^T}=\{w_1,w_2,...,w_m\}^T \text{ is the weight vector.}</math>
|-style="height: 50pt;"
| VT || <math>T_{VT}=\max(T_{b\left(f_1\right)},T_{b\left(f_2\right)},\dots,T_{b\left(f_m\right)}),\text{ where}</math><math>T_{b\left(f_j\right)}=\boldsymbol{\phi}_{f_j}^\mathbf{T}\sum_{i=1}^n{\mathbf{U_i}}\bigg/\sqrt{\boldsymbol{\phi}_{f_j}^\mathbf{T}\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_j}} </math> ||<math> \left(T_{b\left(f_1\right)},T_{b\left(f_2\right)},\dots,T_{b\left(f_m\right)}\right)</math><math>\sim\mathbf{MVN}\left(\mathbf{0},\boldsymbol{\Omega}\right)\text{,} </math><math>\text{where }\boldsymbol{\Omega_{ij}}=\frac{\boldsymbol{\phi}_{f_i}^T\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_j}}{\sqrt{\boldsymbol{\phi}_{f_i}^T\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_i}}\sqrt{\boldsymbol{\phi}_{f_j}^T\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_j}}}</math> || <math> \boldsymbol{\phi}_{f_j}\text{ is a vector of } 0 \text{s and } 1\text{s,} </math> <math>\text{indicating the inclusion of a variant using threshold }f_j; </math>
|-
| SKAT || <math>\mathbf{Q}=\left(\sum_{i=1}^n{\mathbf{U_i^T}}\right) \mathbf{W}\left(\sum_{i=1}^n{\mathbf{U_i}}\right)</math> ||<math>\mathbf{Q}\sim\sum_{i=1}^m{\lambda_i\chi_{1,i}^2},\text{ where}</math> <math>\left(\lambda_1,\lambda_2,\dots,\lambda_m\right)\text{ are eigen values of}</math><math>\left(\sum_{i=1}^n{\mathbf{V_i}}\right)^\frac{1}{2}\mathbf{W}\left(\sum_{i=1}^n{\mathbf{V_i}}\right)^\frac{1}{2}</math> || <math>\mathbf{W}\text{ is a diagonal matrix of weights.}</math>
|}

== Download and Installation ==
* University of Michigan CSG users can go to the following:
/net/fantasia/home/sfengsph/code/Rare-Metal/raremetal/bin/raremetal

=== Where to Download ===
* The software package for Linux and Mac (source code included) can be downloaded here: [[Media:Raremetal.0.4.0.tar.gz ‎|'''RAREMETAL DOWNLOAD''']]

=== How to Compile ===
* Save it to your local path and decompress using the following command:
tar xvzf raremetal.0.4.0.tar.gz
* Go to raremetal_0.4.0/raremetal/src and type the following command to compile:
make

=== How to Execute ===
* Go to raremetal_0.4.0/raremetal/bin and use the following:
./raremetal
* For example usage, please refer to [[http://genome.sph.umich.edu/wiki/Rare-Metal#Example_Usage example command lines]]

== Basic Usage Instructions ==

'''RAREMETAL''' is a command line tool. It is typically run from a Linux or Unix prompt by invoking the command <code>raremetal</code>. In the following are descriptions of basic usage for meta analysis. A detailed [[Tutorial:_RareMETAL|'''TUTORIAL''']] with toy data are also available.

==== Prepare Input Files====
'''RAREMETAL''' requires the following basic input files: summary statistics and covariance matrices of score statistics generated by '''RAREMETALWORKER''' or [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''], a file with list of studies to be included and a group file if gene-level meta-analysis is expected.

=====Summary Statistics=====
Files containing summary statistics and LD matrices generated by '''RAREMETALWORKER''' should be compressed and [http://samtools.sourceforge.net/tabix.shtml '''tabix'''] indexed using the following commands:

bgzip study1.singlevar.score.txt
tabix -s 1 -b 2 -e 2 -c "#" study1.singlevar.score.txt.gz
bgzip study1.singlevar.cov.txt
tabix -s 1 -b 2 -e 2 -c "#" study1.singlevar.cov.txt.gz

Files containing summary statistics and LD matrices generated by '''rvtests''' should be compressed and [http://samtools.sourceforge.net/tabix.shtml '''tabix'''] indexed using the following commands:

bgzip study1.MetaScore.assoc
tabix -s 1 -b 2 -e 2 -S 1 study1.MetaScore.assoc.gz
tabix -s 1 -b 2 -e 2 -S 1 study1.MetaCov.assoc.gz

=====List of Studies=====
* --summaryFiles option is crucial for '''RAREMETAL''' to work. Ignoring this option would lead to FATAL ERROR and '''RAREMETAL''' would stop.
* The file should contain the path and prefix of the studies you want to include.
* If there is one or more studies that you want to excluded from your list, but want to save some effort of generating a new file, you can put a "#" in front of the line of record. '''RAREMETAL''' would automatically exclude that study from meta analysis. An example list of summary file is in the following:

/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/TwinsUK/TwinsUK.TG.singlevar.score.txt.gz
#/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/HUNT/RareMetalWorker/HUNT_MI_case.TG.singlevar.score.txt.gz

* When gene-level analysis is requested, --covFiles option should be used to specify the covariance files. An example file is:

/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/TwinsUK/TwinsUK.TG.singlevar.cov.txt.gz
#/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/HUNT/RareMetalWorker/HUNT_MI_case.TG.singlevar.cov.txt.gz

* The above example study name file guides '''RAREMETAL''' to look for summary statistics from TwinsUK study only, because "HUNT" study is commented out. The following two files are needed for '''RAREMETAL''' to perform further analysis together with their tabix index file are needed.

=====Group Rare Variants=====

====== From a Group File ======
* Grouping methods are only necessary when doing gene-based or group-based burden tests in meta-analysis.
* If none of the grouping method is specified, then only single variant meta-analysis will be performed.
* With --groupFile option, you can specify particular set of variants to be grouped for burden tests.
* The group file must be a tab or space delimited file in the following format:
GROUP_ID MARKER1_ID MARKER2_ID MARKER3_ID ...
* MARKER_ID must be in the following format:
CHR:POS:REF:ALT
* An example group file is:
PLEKHN1 1:901922:G:A 1:901923:C:A 1:902088:G:A 1:902128:C:T 1:902133:C:G 1:902176:C:T 1:905669:C:G
HES4 1:934735:A:C 1:934770:G:A 1:934801:C:T 1:935085:G:A 1:935089:C:G
ISG15 1:949422:G:A 1:949491:G:A 1:949502:C:T 1:949608:G:A 1:949802:G:A 1:949832:G:A
AGRN 1:970687:C:T 1:976963:A:G 1:977028:G:T 1:977356:C:T 1:977396:G:A 1:978628:C:T 1:978645:G:A
C1orf159 1:1021285:G:T 1:1021302:T:C 1:1021315:A:C 1:1021386:G:A 1:1022534:C:T 1:1025751:C:T 1:1026913:C:T

====== From an Annotated VCF File ======
If --groupFile option is '''NOT''' specified, '''RAREMETAL''' will look for an annotated vcf file as blue print for variants to group. Users are also allowed to generate a vcf file based on the superset of variants from pooled samples, and annotate outside RAREMETAL. Then, annotated vcf file can be used as input for RAREMETAL for gene-level meta-analysis, or group files can be generated based on the annotated vcf file. Detailed description of these options are [[Rare-Metal#Group_Rare_Variants_from_Annotated_VCF|'''available''']]. There are also [[Rare-Metal#Example_Command_lines|'''examples''']] of this usage at the bottom of this page.

==== QC Options ====
* '''RAREMETAL''' allows filtering of variants from individual studies by their HWE pvalue and call rate, which are generated as part of the output from '''RAREMETALWORKER''' or [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''].
* To filter by HWE p-values, --hwe option should be used. The default is 0.0, which means not filtering any of the variants.
* To filter by call rate, --callRate option can be specified. The default is 0.0, which allows no filtering utilized.

==== Association Options====
* Currently, CMC type burden test, Madsen-Browning burden test, Variable Threshold burden test and SKAT are provided in '''RAREMETAL''', by specifying --burden, --MB, --VT and --SKAT.
* --maf specifies the minor allele frequency cutoff when doing gene-based or group-based burden tests. The default is maf<0.05.

==== Conditional Analysis====
* To decide whether a signal is caused by shadowing a significant common variant nearby, '''RAREMETAL''' also enables conditional analysis with a list of variants to be conditioned upon provided in a file as input for --condition option. An example input file should be space or tab delimited as in the following. When alleles do not match the ref and alt alleles from samples, the variant will be skipped from conditional analysis.

1:861349:C:T 1:905901:G:A 20:986998:G:C 22:3670691:A:G

== Additional Analysis Options ==

=== Group Rare Variants from Annotated VCF ===
* If --groupFile option is '''NOT''' specified, '''RAREMETAL''' will look for an annotated vcf file as blue print for variants to group.
* The annotated VCF file should be specified using --annotatedVcf option.
* --annotation should be used with --annotatedVcf together when specific category of functional variants are of interest to be grouped. For example, if grouping nonsynonymous and splicing variants are of interests, the following should be included in command line:

--annotatedVcf your.annotated.vcf --annotation nonsyn/splicing
Note: this allows you to group variants that are annotated starting with nonsyn or splicing (not case-sensitive).

* Special format for the annotated VCF file is required: all annotation information should be coded in INFO field in VCF file, starting with the key "ANNO=". An example annotated VCF file is in the following:

#CHROM POS ID REF ALT QUAL FILTER INFO
1 19208194 . G A 100 PASS
AC=3;'''ANNO='''nonsynonymous:ALDH4A1:NM_170726:exon8:c.C866T:p.P289L,ALDH4A1:NM_001161504:exon8:c.C686T:p.P229L,ALDH4A1:NM_003748:exon8:c.C866T:p.P289L,;
'''ANNO='''splicing:ALDH4A1
1 19208293 . G C 100 PASS AC=7;STUDIES=5;MAC=7;MAF=0.001;DESIGN=TBD_ASSAY;DSCORE=1.00;
'''ANNO='''nonsynonymous:ALDH4A1:NM_170726:exon8:c.C767G:p.P256R,ALDH4A1:NM_001161504:exon8:c.C587G:p.P196R,ALDH4A1:NM_003748:exon8:c.C767G:p.P256R,

* Notice that each variant is allowed to have more than one annotations; but each annotation should start with a new key "ANNO=" followed by annotation:genename:other transcript information.

=== Generate a VCF File to Annotate Outside of Rare Metal ===
* --writeVCF allows user to write a VCF file including pooled single variants from all studies. Then users can use their favorite annotation tool to annotate the VCF file. After annotating the VCF file, users can use that file as input for '''RAREMETAL''' for further gene-based or region-based meta analysis.
* The output vcf file will be name as: yourPrefix.pooled.variants.vcf. An example output vcf file is in the following:
#CHROM POS ID REF ALT QUAL FILTER INFO
1 115658497 115658497 G A . . ALT_AF=0.380906;
2 74688884 74688884 G A . . ALT_AF=8.33611e-05;
3 121414217 121414217 C A . . ALT_AF=0.0747833;

===Options for Report Generation===
* --correctGC generates QQ plots and manhattan plots with pvalues corrected using genomic control.
* --prefix allows customized prefix for output files.
* --longOutput allows users to output not only burden test results but also the single variant results (allele frequencies, effect sizes, and p-values) for the variants being grouped together. Please refer to the output files section for detailed explanation and examples.
* --tabulateHits works with --hitsCutoff together to generate reports for genes that have p-value less than specified cutoff from burden tests or SKAT. The default cutoff of p-value for genes to be reported is 1.0e-06, which can be specified by --hitsCutoff option. For more explanations and examples, please go to [[Rare-Metal#TABULATED_HITS| Tabulated Hits]].

===Miscellaneous Options===
* --tabix allows rapid analysis when number of groups/genes of interests are small. Currently, when number of groups is less than 100, --tabix option is automatically turned on.

== Reports Generated by RAREMETAL ==
=== Single Variant Meta Analysis Output ===

==== TABLES ====

* Single variant meta analysis output has the following components: header, results and footnote.
* Header lines start with "##" shows summary of the meta analysis including method used, number of studies, and total sample size.
* Header line starts with "#" are column headers for results table.
* Footnote also starts with "#", where genomic controls from each study and the overall sample are reported.
* An example single variant meta analysis output is shown below:

##Method=SinglevarScore
##STUDY_NUM=2
##TotalSampleSize=14308
#CHROM POS REF ALT POOLED_ALT_AF EFFECT_SIZE DIRECTION_BY_STUDY PVALUE
1 115658497 G A 0.380906 0.00954332 ++ 0.45828
2 74688884 G A 8.33611e-05 -0.196387 -! 0.845372
3 121414217 C A 0.0747833 0.0216982 -+ 0.34453
6 137245814 G C 0.000803746 0.105693 ++ 0.601805
* A detailed explanation of each column is in the following:

CHROM: Chromosome Name
POS: Variant Position
REF: Reference Allele Label
ALT: Alternative Allele Label
POOLED_ALT_AF: Pooled Alternative Allele Frequency
EFFECT_SIZE: Alternative Allele Effect Size
DIRECTION_BY_STUDY: Effect size direction of alternative allele from each study.
The order of study is consistent with the order of studies listed in the input file for option --summaryFiles.
"?" means the variant is not observed or monomorphic from the study.
"!" means the variant observed from this study has different alleles from those in the first study.

==== PLOTS====

'''RAREMETAL''' generates QQ plots and manhattan plots from single variant meta-analysis by default. Three QQ plots are generated, one with all variants included, one of variants with maf<0.05 and one of variants with maf<0.01. All plots are saved in a pdf file named yourPrefix.meta.plots.pdf. Genomic controls are also reported in the title of plots. When --correctGC option is specified, GC corrected plots are also generated.
{| border="1" cellpadding="5" cellspacing="0" align="center"
|-
| align="center" width="100" | [[File:QQ.png]]
|-
| align="center" width="200" | [[File:Single_var_manhattan.png]]
|}

=== Gene-level Tests Meta-Analysis Output ===

==== LONG TABLES ====
When --longOutput is used, output includes both burden test results of genes and single variant results of the variants included in burden tests. Here is an example of output file from SKAT when --longOutput is specified.
##Method=Burden
##STUDY_NUM=2
##TotalSampleSize=14308
#GROUPNAME NUM_VAR VARs MAFs SINGLEVAR_EFFECTs SINGLEVAR_PVALUEs AVG_AF MIN_AF MAX_AF EFFECT_SIZE PVALUE
NOC2L 7 1:880502:C:T;1:881918:G:A;1:887799:C:T;1:888659:T:C;1:889238:G:A;1:891591:C:T;1:892380:G:A 0.000166722,0.0242172,0.0109203,0.0355845,0.0333729,0.00700233,0.00200067 -0.183575,-0.00228307,-0.0598337,0.0220595,0.0229464,-0.0302768,-0.0200417 0.790161,0.953446,0.515806,0.503548,0.499251,0.791773,0.926625 0.0161807 0.000166722 0.0355845 0.00667875 0.662531
KLHL17 2 1:897285:A:G;1:898869:C:T 0.0148408,0.00108369 -0.0502034,-0.0256403 0.528269,0.934606 0.00796222 0.00108369 0.0148408 -0.0484494 0.528878

==== SHORT TABLES ====
Otherwise, single variant results of variants included in burden tests will not be included in the output. Here is an example of output file from SKAT when --longOutput is not specified.

##Method=Burden
##STUDY_NUM=2
##TotalSampleSize=14308
#GROUPNAME NUM_VAR VARs AVG_AF MIN_AF MAX_AF EFFECT_SIZE PVALUE
NOC2L 7 1:880502:C:T;1:881918:G:A;1:887799:C:T;1:888659:T:C;1:889238:G:A;1:891591:C:T;1:892380:G:A 0.0161807 0.000166722 0.0355845 0.00667875 0.662531
KLHL17 2 1:897285:A:G;1:898869:C:T 0.00796222 0.00108369 0.0148408 -0.0484494 0.528878

==== TABULATED HITS ====
* When --tabulateHits is specified, top hits from Burden tests will be generated. Each method will have an individual tabulated file generated. The purpose of this tabulated file is to list burden test results of top hits together with single variant results from variants being grouped in burden tests. The difference between this file and the standard long-format output file from burden test is that each row of the file represents a single variant that is included in the gene for burden test. This format allows each sorting on users end.

* Tabulated top hits are saved in the file:
yourPrefix.meta.tophits.youMethod.tbl (example files names: TG.meta.tophits.burden.tbl, LDL.meta.tophits.SKAT.tbl)

* The following items are tabulated in the output:
GENE: Gene name.
METHOD: Burden test used.
GENE_PVALUE: P-value from gene-based burden tests.
MAF_CUTOFF: MAF cutoff used when doing gene-based tests.
ACTUAL_CUTOFF: Actual MAF cutoff used. (This will be different from MAF_CUTOFF only for Variable Threshold method.
Otherwise, it will be the same as MAF_CUTOFF.)
VAR: Variant name in CHR:POS:REF:ALT format.
MAF: Single variant pooled MAF from all samples.
EFFSIZE: Effect size from single variant meta analysis.
PVALUE: Pvalue from single variant meta analysis.

* An example of tabulated hits from a standard burden test with maf<0.05 as criterion is shown in the following:

GENE METHOD GENE_PVALUE MAF_CUTOFF ACTUAL_CUTOFF VARS MAFS EFFSIZES PVALUES
PCSK9 BURDEN_0.050 7.54587e-11 0.05 0.05 1:55505647:G:T 0.0396631 -0.442192 2.10159e-46
PCSK9 BURDEN_0.050 7.54587e-11 0.05 0.05 1:55518371:G:A 0.0237138 0.0548733 0.430246
PCSK9 BURDEN_0.050 7.54587e-11 0.05 0.05 1:55529187:G:A 0.0433324 0.0946321 0.00129942
APOE BURDEN_0.050 2.83457e-72 0.05 0.05 19:45412079:C:T 0.0413056 -0.554561 2.83457e-72

* According to the example above, PCSK9 had a p-value of 7.54587e-11 from the gene-based burden test, where three variants from this gene were included. Another hit from this meta analysis is APOE, where only one variant was included in the burden test.

==== PLOTS ====
'''RAREMETAL''' generates QQ plots and manhattan plots from single variant and gene-level meta-analysis by default. Example QQ plots and manhattan plots are:
{| border="1" cellpadding="5" cellspacing="0" align="center"
|-
| align="center" width="200" | [[File:manhattan.png]]
|}

==== LOG ====

* A log file is automatically generated by '''RAREMETAL''' to save the parameters in effect. An example is in the following:

The following parameters are in effect:

List of Studies:
============================
--studyName [studyName.SardiNia]

Grouping Methods:
============================
--groupFile [genes.file]
--annotatedVcf []
--annotation []
--writeVcf [OFF]

QC Options:
============================
--hwe [0]
--callRate [0]

Association Methods:
============================
--burden [true]
--MB [false]
--SKAT [false]
--VT [false]
--condition [condition.file]

Other Options:
============================
--tabix [OFF]
--correctGC [ON]
--prefix [test]
--maf [0.05]
--longOutput [false]
--tabulateHits [false]
--hitsCutoff [1e-06]

==Example Command lines==

* Here is an example command line to do single variant meta analysis only:
./raremetal --summaryFiles your.list.of.summary.files --prefix yourPrefix

* When you want to do all burden tests using a group file to specify which variants to group:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --groupFile your.groupfile --burden --MB --SKAT --VT --maf 0.01 --prefix yourPrefix
(NOTE: this will generate single variant meta analysis result and the short format output for burden test results.)

* Here is how to do all SKAT meta analysis using a group file and request a long format output together with tabulated hits:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --groupFile your.groupfile --SKAT --longOutput --tabulateHits --hitsCutoff 1.0e-07 --prefix yourPrefix

* Here is an example of adding QC filters to variants when doing meta analysis.
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --groupFile your.groupfile --SKAT --longOutput --tabulateHits --hitsCutoff 1.0e-07 --hwe 1e-06 --callRate 0.98 --prefix yourPrefix

* Here is how to do the same thing but reading grouping information from an annotated VCF file:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --annotatedVcf your.annotated.vcf --annotation nonsyn/stop/splicing --SKAT --longOutput --tabulateHits --hitsCutoff 1.0e-07 --hwe 1e-06 --callRate 0.98 --prefix yourPrefix

* If you want to write a VCF file of pooled variants from all studies, annotate them using your favorite annotation program, and then come back to '''RAREMETAL''' with the annotate VCF file to do burden tests:
First, use the following command to write the VCF file:
./raremetal --summaryFiles your.list.of.summary.files --writeVcf --prefix yourPrefix
Second, annotate the VCF file using your favorite annotation program. (Annotated VCF file has to follow the format described here: [[Rare-Metal#Group_Rare_Variants_from_Annotated_VCF|annotated VCF format]])
Third, use the following command to do meta analysis:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --annotatedVcf your.annotated.vcf --annotation nonsyn/splicing/stop --burden --MB --SKAT --VT --maf 0.01 --prefix yourPrefix

==Other Useful Info==

* Summary specs can be found [[Summary Files Specification for RAREMETAL]]

==TUTORIAL==
* For a comprehensive tutorial of RAREMETALWORKER and RAREMETAL using example data sets, please go to the following:

[http://genome.sph.umich.edu/wiki/Tutorial:_RareMETAL '''RAREMETAL Tutorial''']

* For a brief tutorial of rvtests, please go to:

[http://genome.sph.umich.edu/wiki/Rvtests '''rvtests''']

==CONTACT==

Please email Shuang Feng (sfengsph at umich dot edu) for questions.

== Change Log ==
* Version 0.0.1 released to U of M CSG group. (2/13/2013)
* Version 0.0.1 released. (2/24/2013)
* Version 0.1.2 released after fixing a few bugs, adding conditional analysis and automatic graphing to the tool. (8/5/2013)
* Version 0.2.9 released after fixing a bug in SKAT and writing PDF when all variants are monomorphic. (10/7/2013)
* Version 0.3.1 released to fix a bug when one of the alleles coded as missing.
* Version 0.4.0 released with a few bugs fixed to properly handling missing genotypes. Major change in command options. Now allow user to specify list of summary statistics files and covariance files separately using --summaryFiles and --covFiles options.

RAREMETAL Documentation

2014-01-23T15:12:23Z

Svrieze: /* TABLES */

== Useful Wiki Pages ==

There are several pages in this Wiki that may be useful to RAREMETAL users. Here are links to key pages:

* The [[RAREMETAL|RAREMETAL Home Page]]

* The [[Tutorial:_RAREMETAL|RAREMETAL Quick Start Tutorial]]

* The [[RAREMETAL FAQ]]

* The [[RAREMETAL Command Reference]]

* The [[RAREMETALWORKER|RAREMETALWORKER documentation]]

The [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''] tool for rare-variant association analysis can also generate output compatible with RAREMETAL.

== Key Features ==
'''RAREMETAL''' has the following features:
* '''RAREMETAL''' performs gene-based or region-based meta analysis using Burden tests with the following methods: CMC_counts, Madsen-Browning, SKAT, and Variable Threshold.
* '''RAREMETAL''' performs single variant metal-analysis by default.
* '''RAREMETAL''' allows customized groups of variants to be tested.
* '''RAREMETAL''' allows conditional analysis to be performed in both gene-level meta-analysis and single variants meta-analysis.
* '''RAREMETAL''' generate QQ plots and manhattan plots by default.

== Brief Description ==

'''RAREMETAL''' is a computationally efficient tool for meta-analysis of rare variants using sequencing or genotyping array data. '''RAREMETAL''' takes summary statistics and LD matrices generated by [[Rare-Metal-Worker|'''RAREMETALWORKER''']] or [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''], handles related and unrelated individuals, and supports both single variant and burden meta-analysis. '''RAREMETAL''' generates high quality plots by default and has options that allow users to build reports at different levels.

'''RAREMETAL''' is developed by Shuang Feng, Dajiang Liu and Gonçalo Abecasis. A R-package written by Dajiang Liu using the same methodology is [[RareMetals|'''available''']].

== Approach ==

The key idea behind meta-analysis with RAREMETAL is that various gene-level test statistics can be reconstructed from single variant score statistics and that, when the linkage disequilibrium relationships between variants are known, the distribution of these gene-level statistics can be derived and used to evaluate signifi-cance. Single variant statistics are calculated using the Cochran-Mantel-Haenszel method. The main formulae are tabulated in the following:

{| border="1" cellpadding="5" cellspacing="0" align="center"
|+'''Formulae for RAREMETAL'''
! scope="col" width="120pt" | Test
! scope="col" width="50pt" | Statistics
! scope="col" width="225pt" | Null Distribution
! scope="col" width="225pt" | Notation
|-
| Single Variant || <math>T=\sum_{i=1}^n {U_i}\bigg/\sqrt{\sum_{i=1}^n{V_i}}</math> || <math>T\sim\mathbf{N}(0,1)</math> ||<math> U_i \text{ is the score statistic from study }i;</math><math> V_i \text{ is the variance of } U_i.</math>
|-
| un-weighted Burden || <math>T_b=\sum_{i=1}^n{\mathbf{U_i}}\Big/\sqrt{\sum_{i=1}^n{\mathbf{V_i}}}</math> || <math>T_b\sim\mathbf{N}(0,1)</math> ||<math> \mathbf{U_i}\text{ is the vector of score statistics from study }i, or </math> <math> \mathbf{U_i}=\{U_{i1},...,U_{im}\};</math> <math>\mathbf{V_i} \text{ is the covariance of } \mathbf{U_i}.</math>
|-
| Weighted Burden || <math>T_{wb}=\mathbf{w^T}\sum_{i=1}^n{\mathbf{U_i}}\bigg/\sqrt{\mathbf{w^T}\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\mathbf{w}}</math> || <math>T_{wb}\sim\mathbf{N}(0,1)</math> || <math> \mathbf{w^T}=\{w_1,w_2,...,w_m\}^T \text{ is the weight vector.}</math>
|-style="height: 50pt;"
| VT || <math>T_{VT}=\max(T_{b\left(f_1\right)},T_{b\left(f_2\right)},\dots,T_{b\left(f_m\right)}),\text{ where}</math><math>T_{b\left(f_j\right)}=\boldsymbol{\phi}_{f_j}^\mathbf{T}\sum_{i=1}^n{\mathbf{U_i}}\bigg/\sqrt{\boldsymbol{\phi}_{f_j}^\mathbf{T}\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_j}} </math> ||<math> \left(T_{b\left(f_1\right)},T_{b\left(f_2\right)},\dots,T_{b\left(f_m\right)}\right)</math><math>\sim\mathbf{MVN}\left(\mathbf{0},\boldsymbol{\Omega}\right)\text{,} </math><math>\text{where }\boldsymbol{\Omega_{ij}}=\frac{\boldsymbol{\phi}_{f_i}^T\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_j}}{\sqrt{\boldsymbol{\phi}_{f_i}^T\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_i}}\sqrt{\boldsymbol{\phi}_{f_j}^T\left(\sum_{i=1}^n{\mathbf{V_i}}\right)\boldsymbol{\phi}_{f_j}}}</math> || <math> \boldsymbol{\phi}_{f_j}\text{ is a vector of } 0 \text{s and } 1\text{s,} </math> <math>\text{indicating the inclusion of a variant using threshold }f_j; </math>
|-
| SKAT || <math>\mathbf{Q}=\left(\sum_{i=1}^n{\mathbf{U_i^T}}\right) \mathbf{W}\left(\sum_{i=1}^n{\mathbf{U_i}}\right)</math> ||<math>\mathbf{Q}\sim\sum_{i=1}^m{\lambda_i\chi_{1,i}^2},\text{ where}</math> <math>\left(\lambda_1,\lambda_2,\dots,\lambda_m\right)\text{ are eigen values of}</math><math>\left(\sum_{i=1}^n{\mathbf{V_i}}\right)^\frac{1}{2}\mathbf{W}\left(\sum_{i=1}^n{\mathbf{V_i}}\right)^\frac{1}{2}</math> || <math>\mathbf{W}\text{ is a diagonal matrix of weights.}</math>
|}

== Download and Installation ==
* University of Michigan CSG users can go to the following:
/net/fantasia/home/sfengsph/code/Rare-Metal/raremetal/bin/raremetal

=== Where to Download ===
* The software package for Linux and Mac (source code included) can be downloaded here: [[Media:Raremetal.0.4.0.tar.gz ‎|'''RAREMETAL DOWNLOAD''']]

=== How to Compile ===
* Save it to your local path and decompress using the following command:
tar xvzf raremetal.0.4.0.tar.gz
* Go to raremetal_0.4.0/raremetal/src and type the following command to compile:
make

=== How to Execute ===
* Go to raremetal_0.4.0/raremetal/bin and use the following:
./raremetal
* For example usage, please refer to [[http://genome.sph.umich.edu/wiki/Rare-Metal#Example_Usage example command lines]]

== Basic Usage Instructions ==

'''RAREMETAL''' is a command line tool. It is typically run from a Linux or Unix prompt by invoking the command <code>raremetal</code>. In the following are descriptions of basic usage for meta analysis. A detailed [[Tutorial:_RareMETAL|'''TUTORIAL''']] with toy data are also available.

==== Prepare Input Files====
'''RAREMETAL''' requires the following basic input files: summary statistics and covariance matrices of score statistics generated by '''RAREMETALWORKER''' or [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''], a file with list of studies to be included and a group file if gene-level meta-analysis is expected.

=====Summary Statistics=====
Files containing summary statistics and LD matrices generated by '''RAREMETALWORKER''' should be compressed and [http://samtools.sourceforge.net/tabix.shtml '''tabix'''] indexed using the following commands:

bgzip study1.singlevar.score.txt
tabix -s 1 -b 2 -e 2 -c "#" study1.singlevar.score.txt.gz
bgzip study1.singlevar.cov.txt
tabix -s 1 -b 2 -e 2 -c "#" study1.singlevar.cov.txt.gz

Files containing summary statistics and LD matrices generated by '''rvtests''' should be compressed and [http://samtools.sourceforge.net/tabix.shtml '''tabix'''] indexed using the following commands:

bgzip study1.MetaScore.assoc
tabix -s 1 -b 2 -e 2 -S 1 study1.MetaScore.assoc.gz
tabix -s 1 -b 2 -e 2 -S 1 study1.MetaCov.assoc.gz

=====List of Studies=====
* --summaryFiles option is crucial for '''RAREMETAL''' to work. Ignoring this option would lead to FATAL ERROR and '''RAREMETAL''' would stop.
* The file should contain the path and prefix of the studies you want to include.
* If there is one or more studies that you want to excluded from your list, but want to save some effort of generating a new file, you can put a "#" in front of the line of record. '''RAREMETAL''' would automatically exclude that study from meta analysis. An example list of summary file is in the following:

/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/TwinsUK/TwinsUK.TG.singlevar.score.txt.gz
#/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/HUNT/RareMetalWorker/HUNT_MI_case.TG.singlevar.score.txt.gz

* When gene-level analysis is requested, --covFiles option should be used to specify the covariance files. An example file is:

/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/TwinsUK/TwinsUK.TG.singlevar.cov.txt.gz
#/net/fantasia/home/sfengsph/prj/raremetal/raremetal/bin/META/HUNT/RareMetalWorker/HUNT_MI_case.TG.singlevar.cov.txt.gz

* The above example study name file guides '''RAREMETAL''' to look for summary statistics from TwinsUK study only, because "HUNT" study is commented out. The following two files are needed for '''RAREMETAL''' to perform further analysis together with their tabix index file are needed.

=====Group Rare Variants=====

====== From a Group File ======
* Grouping methods are only necessary when doing gene-based or group-based burden tests in meta-analysis.
* If none of the grouping method is specified, then only single variant meta-analysis will be performed.
* With --groupFile option, you can specify particular set of variants to be grouped for burden tests.
* The group file must be a tab or space delimited file in the following format:
GROUP_ID MARKER1_ID MARKER2_ID MARKER3_ID ...
* MARKER_ID must be in the following format:
CHR:POS:REF:ALT
* An example group file is:
PLEKHN1 1:901922:G:A 1:901923:C:A 1:902088:G:A 1:902128:C:T 1:902133:C:G 1:902176:C:T 1:905669:C:G
HES4 1:934735:A:C 1:934770:G:A 1:934801:C:T 1:935085:G:A 1:935089:C:G
ISG15 1:949422:G:A 1:949491:G:A 1:949502:C:T 1:949608:G:A 1:949802:G:A 1:949832:G:A
AGRN 1:970687:C:T 1:976963:A:G 1:977028:G:T 1:977356:C:T 1:977396:G:A 1:978628:C:T 1:978645:G:A
C1orf159 1:1021285:G:T 1:1021302:T:C 1:1021315:A:C 1:1021386:G:A 1:1022534:C:T 1:1025751:C:T 1:1026913:C:T

====== From an Annotated VCF File ======
If --groupFile option is '''NOT''' specified, '''RAREMETAL''' will look for an annotated vcf file as blue print for variants to group. Users are also allowed to generate a vcf file based on the superset of variants from pooled samples, and annotate outside RAREMETAL. Then, annotated vcf file can be used as input for RAREMETAL for gene-level meta-analysis, or group files can be generated based on the annotated vcf file. Detailed description of these options are [[Rare-Metal#Group_Rare_Variants_from_Annotated_VCF|'''available''']]. There are also [[Rare-Metal#Example_Command_lines|'''examples''']] of this usage at the bottom of this page.

==== QC Options ====
* '''RAREMETAL''' allows filtering of variants from individual studies by their HWE pvalue and call rate, which are generated as part of the output from '''RAREMETALWORKER''' or [http://genome.sph.umich.edu/wiki/Rvtests '''rvtests'''].
* To filter by HWE p-values, --hwe option should be used. The default is 0.0, which means not filtering any of the variants.
* To filter by call rate, --callRate option can be specified. The default is 0.0, which allows no filtering utilized.

==== Association Options====
* Currently, CMC type burden test, Madsen-Browning burden test, Variable Threshold burden test and SKAT are provided in '''RAREMETAL''', by specifying --burden, --MB, --VT and --SKAT.
* --maf specifies the minor allele frequency cutoff when doing gene-based or group-based burden tests. The default is maf<0.05.

==== Conditional Analysis====
* To decide whether a signal is caused by shadowing a significant common variant nearby, '''RAREMETAL''' also enables conditional analysis with a list of variants to be conditioned upon provided in a file as input for --condition option. An example input file should be space or tab delimited as in the following. When alleles do not match the ref and alt alleles from samples, the variant will be skipped from conditional analysis.

1:861349:C:T 1:905901:G:A 20:986998:G:C 22:3670691:A:G

== Additional Analysis Options ==

=== Group Rare Variants from Annotated VCF ===
* If --groupFile option is '''NOT''' specified, '''RAREMETAL''' will look for an annotated vcf file as blue print for variants to group.
* The annotated VCF file should be specified using --annotatedVcf option.
* --annotation should be used with --annotatedVcf together when specific category of functional variants are of interest to be grouped. For example, if grouping nonsynonymous and splicing variants are of interests, the following should be included in command line:

--annotatedVcf your.annotated.vcf --annotation nonsyn/splicing
Note: this allows you to group variants that are annotated starting with nonsyn or splicing (not case-sensitive).

* Special format for the annotated VCF file is required: all annotation information should be coded in INFO field in VCF file, starting with the key "ANNO=". An example annotated VCF file is in the following:

#CHROM POS ID REF ALT QUAL FILTER INFO
1 19208194 . G A 100 PASS
AC=3;'''ANNO='''nonsynonymous:ALDH4A1:NM_170726:exon8:c.C866T:p.P289L,ALDH4A1:NM_001161504:exon8:c.C686T:p.P229L,ALDH4A1:NM_003748:exon8:c.C866T:p.P289L,;
'''ANNO='''splicing:ALDH4A1
1 19208293 . G C 100 PASS AC=7;STUDIES=5;MAC=7;MAF=0.001;DESIGN=TBD_ASSAY;DSCORE=1.00;
'''ANNO='''nonsynonymous:ALDH4A1:NM_170726:exon8:c.C767G:p.P256R,ALDH4A1:NM_001161504:exon8:c.C587G:p.P196R,ALDH4A1:NM_003748:exon8:c.C767G:p.P256R,

* Notice that each variant is allowed to have more than one annotations; but each annotation should start with a new key "ANNO=" followed by annotation:genename:other transcript information.

=== Generate a VCF File to Annotate Outside of Rare Metal ===
* --writeVCF allows user to write a VCF file including pooled single variants from all studies. Then users can use their favorite annotation tool to annotate the VCF file. After annotating the VCF file, users can use that file as input for '''RAREMETAL''' for further gene-based or region-based meta analysis.
* The output vcf file will be name as: yourPrefix.pooled.variants.vcf. An example output vcf file is in the following:
#CHROM POS ID REF ALT QUAL FILTER INFO
1 115658497 115658497 G A . . ALT_AF=0.380906;
2 74688884 74688884 G A . . ALT_AF=8.33611e-05;
3 121414217 121414217 C A . . ALT_AF=0.0747833;

===Options for Report Generation===
* --correctGC generates QQ plots and manhattan plots with pvalues corrected using genomic control.
* --prefix allows customized prefix for output files.
* --longOutput allows users to output not only burden test results but also the single variant results (allele frequencies, effect sizes, and p-values) for the variants being grouped together. Please refer to the output files section for detailed explanation and examples.
* --tabulateHits works with --hitsCutoff together to generate reports for genes that have p-value less than specified cutoff from burden tests or SKAT. The default cutoff of p-value for genes to be reported is 1.0e-06, which can be specified by --hitsCutoff option. For more explanations and examples, please go to [[Rare-Metal#TABULATED_HITS| Tabulated Hits]].

===Miscellaneous Options===
* --tabix allows rapid analysis when number of groups/genes of interests are small. Currently, when number of groups is less than 100, --tabix option is automatically turned on.

== Reports Generated by RAREMETAL ==
=== Single Variant Meta Analysis Output ===

==== TABLES ====

* Single variant meta analysis output has the following components: header, results and footnote.
* Header lines start with "##" shows summary of the meta analysis including method used, number of studies, and total sample size.
* Header line starts with "#" are column headers for results table.
* Footnote also starts with "#", where genomic controls from each study and the overall sample are reported.
* An example single variant meta analysis output is shown below:

##Method=SinglevarScore
##STUDY_NUM=2
##TotalSampleSize=14308
#CHROM POS REF ALT POOLED_ALT_AF EFFECT_SIZE DIRECTION_BY_STUDY PVALUE
1 115658497 G A 0.380906 0.00954332 ++ 0.45828
2 74688884 G A 8.33611e-05 -0.196387 -! 0.845372
3 121414217 C A 0.0747833 0.0216982 -+ 0.34453
6 137245814 G C 0.000803746 0.105693 ++ 0.601805
* A detailed explanation of each column is in the following:

CHROM: Chromosome Name
POS: Variant Position
REF: Reference Allele Label
ALT: Alternative Allele Label
POOLED_ALT_AF: Pooled Alternative Allele Frequency
EFFECT_SIZE: Alternative Allele Effect Size
DIRECTION_BY_STUDY: Effect size direction of alternative allele from each study.
The order of study is consistent with the order of studies listed in the input file for option --summaryFiles.
"?" means the variant is not observed or monomorphic from the study.
"!" means the variant observed from this study has different alleles from those in the first study.
"!" also can indicate that a particular study did not have an entry for this marker.

==== PLOTS====

'''RAREMETAL''' generates QQ plots and manhattan plots from single variant meta-analysis by default. Three QQ plots are generated, one with all variants included, one of variants with maf<0.05 and one of variants with maf<0.01. All plots are saved in a pdf file named yourPrefix.meta.plots.pdf. Genomic controls are also reported in the title of plots. When --correctGC option is specified, GC corrected plots are also generated.
{| border="1" cellpadding="5" cellspacing="0" align="center"
|-
| align="center" width="100" | [[File:QQ.png]]
|-
| align="center" width="200" | [[File:Single_var_manhattan.png]]
|}

=== Gene-level Tests Meta-Analysis Output ===

==== LONG TABLES ====
When --longOutput is used, output includes both burden test results of genes and single variant results of the variants included in burden tests. Here is an example of output file from SKAT when --longOutput is specified.
##Method=Burden
##STUDY_NUM=2
##TotalSampleSize=14308
#GROUPNAME NUM_VAR VARs MAFs SINGLEVAR_EFFECTs SINGLEVAR_PVALUEs AVG_AF MIN_AF MAX_AF EFFECT_SIZE PVALUE
NOC2L 7 1:880502:C:T;1:881918:G:A;1:887799:C:T;1:888659:T:C;1:889238:G:A;1:891591:C:T;1:892380:G:A 0.000166722,0.0242172,0.0109203,0.0355845,0.0333729,0.00700233,0.00200067 -0.183575,-0.00228307,-0.0598337,0.0220595,0.0229464,-0.0302768,-0.0200417 0.790161,0.953446,0.515806,0.503548,0.499251,0.791773,0.926625 0.0161807 0.000166722 0.0355845 0.00667875 0.662531
KLHL17 2 1:897285:A:G;1:898869:C:T 0.0148408,0.00108369 -0.0502034,-0.0256403 0.528269,0.934606 0.00796222 0.00108369 0.0148408 -0.0484494 0.528878

==== SHORT TABLES ====
Otherwise, single variant results of variants included in burden tests will not be included in the output. Here is an example of output file from SKAT when --longOutput is not specified.

##Method=Burden
##STUDY_NUM=2
##TotalSampleSize=14308
#GROUPNAME NUM_VAR VARs AVG_AF MIN_AF MAX_AF EFFECT_SIZE PVALUE
NOC2L 7 1:880502:C:T;1:881918:G:A;1:887799:C:T;1:888659:T:C;1:889238:G:A;1:891591:C:T;1:892380:G:A 0.0161807 0.000166722 0.0355845 0.00667875 0.662531
KLHL17 2 1:897285:A:G;1:898869:C:T 0.00796222 0.00108369 0.0148408 -0.0484494 0.528878

==== TABULATED HITS ====
* When --tabulateHits is specified, top hits from Burden tests will be generated. Each method will have an individual tabulated file generated. The purpose of this tabulated file is to list burden test results of top hits together with single variant results from variants being grouped in burden tests. The difference between this file and the standard long-format output file from burden test is that each row of the file represents a single variant that is included in the gene for burden test. This format allows each sorting on users end.

* Tabulated top hits are saved in the file:
yourPrefix.meta.tophits.youMethod.tbl (example files names: TG.meta.tophits.burden.tbl, LDL.meta.tophits.SKAT.tbl)

* The following items are tabulated in the output:
GENE: Gene name.
METHOD: Burden test used.
GENE_PVALUE: P-value from gene-based burden tests.
MAF_CUTOFF: MAF cutoff used when doing gene-based tests.
ACTUAL_CUTOFF: Actual MAF cutoff used. (This will be different from MAF_CUTOFF only for Variable Threshold method.
Otherwise, it will be the same as MAF_CUTOFF.)
VAR: Variant name in CHR:POS:REF:ALT format.
MAF: Single variant pooled MAF from all samples.
EFFSIZE: Effect size from single variant meta analysis.
PVALUE: Pvalue from single variant meta analysis.

* An example of tabulated hits from a standard burden test with maf<0.05 as criterion is shown in the following:

GENE METHOD GENE_PVALUE MAF_CUTOFF ACTUAL_CUTOFF VARS MAFS EFFSIZES PVALUES
PCSK9 BURDEN_0.050 7.54587e-11 0.05 0.05 1:55505647:G:T 0.0396631 -0.442192 2.10159e-46
PCSK9 BURDEN_0.050 7.54587e-11 0.05 0.05 1:55518371:G:A 0.0237138 0.0548733 0.430246
PCSK9 BURDEN_0.050 7.54587e-11 0.05 0.05 1:55529187:G:A 0.0433324 0.0946321 0.00129942
APOE BURDEN_0.050 2.83457e-72 0.05 0.05 19:45412079:C:T 0.0413056 -0.554561 2.83457e-72

* According to the example above, PCSK9 had a p-value of 7.54587e-11 from the gene-based burden test, where three variants from this gene were included. Another hit from this meta analysis is APOE, where only one variant was included in the burden test.

==== PLOTS ====
'''RAREMETAL''' generates QQ plots and manhattan plots from single variant and gene-level meta-analysis by default. Example QQ plots and manhattan plots are:
{| border="1" cellpadding="5" cellspacing="0" align="center"
|-
| align="center" width="200" | [[File:manhattan.png]]
|}

==== LOG ====

* A log file is automatically generated by '''RAREMETAL''' to save the parameters in effect. An example is in the following:

The following parameters are in effect:

List of Studies:
============================
--studyName [studyName.SardiNia]

Grouping Methods:
============================
--groupFile [genes.file]
--annotatedVcf []
--annotation []
--writeVcf [OFF]

QC Options:
============================
--hwe [0]
--callRate [0]

Association Methods:
============================
--burden [true]
--MB [false]
--SKAT [false]
--VT [false]
--condition [condition.file]

Other Options:
============================
--tabix [OFF]
--correctGC [ON]
--prefix [test]
--maf [0.05]
--longOutput [false]
--tabulateHits [false]
--hitsCutoff [1e-06]

==Example Command lines==

* Here is an example command line to do single variant meta analysis only:
./raremetal --summaryFiles your.list.of.summary.files --prefix yourPrefix

* When you want to do all burden tests using a group file to specify which variants to group:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --groupFile your.groupfile --burden --MB --SKAT --VT --maf 0.01 --prefix yourPrefix
(NOTE: this will generate single variant meta analysis result and the short format output for burden test results.)

* Here is how to do all SKAT meta analysis using a group file and request a long format output together with tabulated hits:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --groupFile your.groupfile --SKAT --longOutput --tabulateHits --hitsCutoff 1.0e-07 --prefix yourPrefix

* Here is an example of adding QC filters to variants when doing meta analysis.
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --groupFile your.groupfile --SKAT --longOutput --tabulateHits --hitsCutoff 1.0e-07 --hwe 1e-06 --callRate 0.98 --prefix yourPrefix

* Here is how to do the same thing but reading grouping information from an annotated VCF file:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --annotatedVcf your.annotated.vcf --annotation nonsyn/stop/splicing --SKAT --longOutput --tabulateHits --hitsCutoff 1.0e-07 --hwe 1e-06 --callRate 0.98 --prefix yourPrefix

* If you want to write a VCF file of pooled variants from all studies, annotate them using your favorite annotation program, and then come back to '''RAREMETAL''' with the annotate VCF file to do burden tests:
First, use the following command to write the VCF file:
./raremetal --summaryFiles your.list.of.summary.files --writeVcf --prefix yourPrefix
Second, annotate the VCF file using your favorite annotation program. (Annotated VCF file has to follow the format described here: [[Rare-Metal#Group_Rare_Variants_from_Annotated_VCF|annotated VCF format]])
Third, use the following command to do meta analysis:
./raremetal --summaryFiles your.list.of.summary.files --covFiles your.list.of.cov.files --annotatedVcf your.annotated.vcf --annotation nonsyn/splicing/stop --burden --MB --SKAT --VT --maf 0.01 --prefix yourPrefix

==Other Useful Info==

* Summary specs can be found [[Summary Files Specification for RAREMETAL]]

==TUTORIAL==
* For a comprehensive tutorial of RAREMETALWORKER and RAREMETAL using example data sets, please go to the following:

[http://genome.sph.umich.edu/wiki/Tutorial:_RareMETAL '''RAREMETAL Tutorial''']

* For a brief tutorial of rvtests, please go to:

[http://genome.sph.umich.edu/wiki/Rvtests '''rvtests''']

==CONTACT==

Please email Shuang Feng (sfengsph at umich dot edu) for questions.

== Change Log ==
* Version 0.0.1 released to U of M CSG group. (2/13/2013)
* Version 0.0.1 released. (2/24/2013)
* Version 0.1.2 released after fixing a few bugs, adding conditional analysis and automatic graphing to the tool. (8/5/2013)
* Version 0.2.9 released after fixing a bug in SKAT and writing PDF when all variants are monomorphic. (10/7/2013)
* Version 0.3.1 released to fix a bug when one of the alleles coded as missing.
* Version 0.4.0 released with a few bugs fixed to properly handling missing genotypes. Major change in command options. Now allow user to specify list of summary statistics files and covariance files separately using --summaryFiles and --covFiles options.

EMADS Primary Analysis Plan

2013-11-15T03:03:58Z

Svrieze:

{| style="width:100%; background:#fcfcfc; margin-top:1.2em; border:1px solid #ccc;"
|-
| style="width:100%; text-align:center; white-space:nowrap; color:#000;" | <div style="font-size:162%; border:none; margin:0; padding:.1em; color:#000;">This Page is No Longer Supported. Please Visit http://gscan.sph.umich.edu</div>
|}

[[File:EMADS_thumb5.png|thumb|340px|right]]
Exome Meta-Analysis of Drinking and Smoking (EMADS) Analysis Plan

Parent page: [[EMADS]]

== Genotypes ==
All samples have some version of the Exome Chip or exome/whole genome sequences. Individual studies will provide information about the manufacturer and version of the exome chip, or sequencing platform, they are using.

== Inclusion Criteria ==
For our first analysis, samples must be between ages 18 and 70 (inclusive) and be of European ancestry. We will extend analysis to other ancestral groups in the future.

== Quality Control ==
We leave calling algorithms, marker filters, and sample filters to the discretion of local sites, although we will evaluate the possibility of batch effects (where batch might be a study) during the meta-analysis step.

For reference, four currently participating studies have used Illumina chips and Illumina’s genotype caller in Genome Studio (Gencall). Some studies also implemented some manual curation involving reclustering the intensity data of ~1500 markers.

== Strand Orientation ==
Chip TOP allele annotations (typical output from Gencall) need to be updated to the forward strand of build 37.

The strand file for exome chip version 12v1_A is available at:
http://www.well.ox.ac.uk/~wrayner/strand/HumanExome-12v1_A-b37-strand.zip

Usage instructions, including scripts, are available here:
http://www.well.ox.ac.uk/~wrayner/strand/

Future strand files will also be available at that site.

== Phenotypes ==

=== (1) Average cigarettes smoked per day, either as a current smoker or former smoker ===
Individuals who either never smoked, or on whom we have no data (e.g., someone was a former smoker but former smoking was never assessed) will be excluded from analysis. Only cigarettes will be included in the estimate. If preferable, repeated measures designs (longitudinal data) can use all assessments by scaling and correcting for covariates within waves of assessment, then averaging across assessments.

For studies that collect a quantitative measure of CPD, where the respondent is free to provide any integer (e.g., 13 CPD), '''we will bin responses into the following bins: 1-10, 11-20, 21-30, 31+.''' If some study collected binned responses from the outset, and those bins happen to differ from ours (e.g., 1-5, 6-15, etc.), then we will simply use whatever bins the study has collected. Please contact Scott if your study does something completely different.

In analysis, it is likely easiest to consider the bins to correspond to the following numerical values.
*1 = 1-10
*2 = 11-20
*3 = 21-30
*4 = 31+

Please note, however, that when we report descriptive statistics about our phenotypes we will want to report the original participant responses. Even though we'll bin the data for analysis, we'll still report quantitative CPD (when possible) when we describe each study's phenotype in eventual publications.

=== (2) Smoking Initiation ===
This is a binary phenotype. Code "1" for everyone in the study who reports ever being a regular smoker in their life (current or former). Code a "0" for everyone who denies ever being a regular smoker in their life.

Every study had some usable measure of whether a respondent has ever regularly smoked. Almost all asked directly. Some have necessary information to code this variable (e.g., 100 cigs lifetime? Ever smoked every day for 2 weeks straight?).

Note that we’re among the first groups conducting such meta-analyses, and our analysis pipeline is currently restricted to continuous traits. Until methods are developed for binary traits, it is proposed that we analyze smoking initiation as a continuous trait.

=== (3) Pack Years ===
Number of cigarettes per day, divided by 20, then multiplied by the number of years the person has smoked. For this measure please use the quantitative CPD, and not the binned responses discussed above under the CPD heading. If your study collected binned responses from the outset, please use the midpoint of the range in calculating Pack Years. For example, individuals stating they smoked 11-20 CPD would be assumed to have smoked 15.5 on average

=== (4) Age of Initiation of Smoking ===
The age an individual first became a regular smoker. Please check for obvious outliers and remove them (4 years old or younger).

=== (5) Average drinks per week, either as a current drinker or former drinker ===
The average number of drinks a subject reports drinking each week. Most studies asked this question directly. Other studies have converted to grams per day, or grams per week. The latter are fine to analyze directly for our purposes.

Individuals who either never drank, or on whom we have no data (e.g., someone was a former drinker but former drinking was not assessed) will be excluded from analysis. Please combine all types of liquor in the total estimate. If preferable, repeated measures designs (longitudinal data) can use all assessments by scaling and correcting for covariates within waves of assessment, then averaging across assessments.

If your study forced the respondent to report ranges (e.g., 1-5, 6-10, 11-15, 16-20, etc.) please simply use the midpoint of the range. For example, if one range is 1-5 DPW, we assume they drink 2.5 DPW on average. Then use these midpoints in all subsequent analysis.

== Covariate Correction (to be done after left-anchoring and log transformation)==
For CPD we will consider the binned responses to be on a quantitative scale from 1-4 (see above under the CPD phenotype description). '''CPD therefore will not require transformation''' prior to covariate correction.

For the other four quantitative phenotypes (Pack Years, Age of Initiation, Drinks Per Week) please '''left-anchor''' the distribution at 1 and '''log-transform it'''. Left-anchoring, such that no value is less than 1, prevents the log-transform from returning nonsensical values like negative infinity. Then apply the covariate correction to the transformed phenotypes.

No transformations are necessary for the binary smoking initiation phenotype, but we will still correct for covariates for smoking initiation (recall that we are treating this binary phenotype in our analysis as if it were a continuous trait).

Appropriate covariates can often be study-specific. We will depend on local investigators to determine the most appropriate covariates. We list here some covariates that will likely be necessary.

=== Main Effects ===
*Age
**At assessment in current smokers/drinkers
**Age of smoking/drinking for former smokers/drinkers could be age at quitting
**At assessment for Pack Years, Smoking Initiation, and Age of Initiation, regardless of current/former smoking status
*Age squared
*Sex
*Date of birth (or year, or range)
*Cohort
*Genetic principle components (alternatively could use empirical kinships in rare-metal-worker)
*Adolescence versus adulthood (e.g., < 21 years of age versus >=21). Only consider using this covariate if you have a large number of adolescents in your study.
*Date of assessment (e.g., the calendar year of the assessment)?
*Current versus former smoker for smoking phenotypes. This would be a binary covariate.
*Current versus former drinker for drinking phenotypes. This would be a binary covariate.
*For the drinking phenotype, consider Height, weight, and/or BMI (the idea is that a similar amount of alcohol has different effects on a 200 lb person versus a 100 lb person)

=== Interactions ===
These covariates may not be necessary, but we list them for local analysts to consider.
*Sex X Adolescence interaction
*Sex X Age interaction
*Sex X Weight/Height/BMI interaction
*Age X Adolescence interaction

== Analysis of Covariate-Corrected Phenotypes ==
The basic analysis is two-stage. In the first stage, local investigators produce, for each phenotype, a set of single-variant summary statistics using a tool developed at the University of Michigan. In the second stage, these summary statistics are pooled for meta-analysis. All single-variant and gene-based (‘burden’) tests can be conducted from the summary statistics.

These two stages are now described in more detail.

=== Stage 1: Local Sites Produce Summary Statistics Using Rare-Metal-Worker ===
The meta-analysis step (stage 2) requires a very specific set of summary statistics, which includes single-variant test statistics and p-values, as well as the test statistic covariance matrix within a sliding window (default: 1Mb). Shuang Feng, Dajiang Liu, and Goncalo Abecasis at the University of Michigan have developed software specifically for this purpose, called Rare-Metal-Worker. Software and usage instructions to generate necessary single variant statistics is available at [http://genome.sph.umich.edu/wiki/Rare-Metal-Worker Rare-Metal-Worker].If there are installation problems please let Scott know.

Rare-Metal-Worker works best, IMHO, when coding the genotype files as VCF. There are several ways to convert to vcf, including PLINK/SEQ and also WDIST (https://www.cog-genomics.org/wdist/).

'''NOTE:''' It is essential that analysis proceeds in the following order. For CPD, please bin quantitative responses and correct for covariates to obtain residuals. For Pack Years, Age of Initiation, and Drinks Per Week, please left-anchor responses at 1, log-transform, and then correct for covariates to obtain residuals. In this way we will obtain residualized phenotypes ready for analysis with Rare-Metal-Worker. These steps are probably easier to do in your software of choice.

Now, using the residualized phenotypes in a .ped file please specify the --makeResiduals and --inverseNormalize options. These will correct for the intercept and then inverse-normalize the phenotype prior to conducting association tests.

'''Marker Grid for Fast-LMM Empirical Kinship'''

If you plan to use the Fast-LMM mixed model capability in Rare-Metal-Worker, it is likely preferable that you construct your kinship matrix either 1) with genome-wide markers from a GWAS panel (or 2nd generation exome chip) or 2) a subset of selected markers from the exome chip array. A list of markers can be obtained from Scott. There are many common markers on the first version of the exome chip, and many were selected for fine mapping (of MHC) or because of prior GWAS signals. These latter markers would ideally be excluded from the set of markers used to construct the empirical kinship matrix.

'''Running Times'''

Run times depend heavily on the type of analysis. If all samples are unrelated, and no kinship matrix is used, then run times should be relatively fast (tens of minutes). If a mixed model is used, for example using an empirical kinship, then in samples of a few thousand rare-metal-worker should take less than 20 minutes to complete. In larger samples, especially of related individuals (~10,000 or more with phenotype data), it can take several days to complete an exome-chip-wide scan.

'''Submitting Results for Meta-Analysis'''

All output files from Rare-Metal-Worker can then be uploaded to an sftp server at the University of Michigan for central analysis -- please email [mailto:svrieze@umich.edu Scott Vrieze] for the hostname, username, and password. One site used Aspera to transmit results, which worked well.

=== Stage 2: Single-Variant and Gene-Based Meta-Analysis ===
Single-Variant Tests
We will do meta-analysis of score statistics for individual variants weighting by sample size using [[Rare-Metal]]. Details are provided at that site.

== Gene-Based Tests ==
Gene-based tests can be conducted centrally by Scott using output from Rare-Metal-Worker.

We will implement three burden tests.
# First, a Variable Threshold Combined Multivariate and Collapsing count method ([http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032073/ VTCMC]), where the number of rare alleles is counted in each gene, then the gene is tested for association. The threshold for what variants are considered "rare" (MAF < .05? MAF < .01?) is set adaptively such that the result minimizes the p-value obtained.
# Second, we will use SKAT ([http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3135811/ SKAT]) for all rare variants (MAF < .05) within a gene. SKAT allows for variants with opposite directions of effect within the same gene, whereas the variable threshold combined multivariate and collapsing method does not.
# Third, we will use a burden test developed by Madsen and Browning ([http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000384 M-B]) where the number of rare alleles is counted in each gene, then the gene is tested for association, but alleles in the count are weighted by the inverse of the MAF. Thus rarer alleles are given more weight than common allele.

== Genotype Annotation ==
Gene-based burden tests can be augmented with genotype annotation. We currently plan to use only nonsynonymous variants from ANNO-generated annotations relative to GENCODE transcripts. All annotation can be done centrally at the meta-analysis stage to ensure consistency across sites.

== Multivariate Test ==
We will pursue development of a multivariate test for drinking and smoking jointly. This could be as simple as, on a per-marker or per-gene basis, averaging effect sizes or p-values for meta-analytic CPD and DPW p-value results.

== Further Downstream Analysis ==
To be determined. Will depend on results from the main analysis above.

We more than welcome individual sites to propose additional analysis, as well as to take the lead on additional projects related to the primary aims of this meta-analysis.

== Descriptive Phenotype Information ==
When it comes time to publish our results we'll need descriptive information about our phenotypes. In anticipation of this Scott has sent around some draft tables. The tables will contain descriptive information about your study and phenotypes. For each phenotype we need:
*sample size of non-missing observations
*mean, standard deviation, range for quantitative phenotypes (including quantitative CPD, before binning)
*Counts for smoking initiation, a binary phenotype
*The 5x5 correlation matrix between residualized phenotypes, as well as the sample size contributing to each correlation.

2013-06-16T01:30:35Z

Svrieze:

Abecasis Lab

2013-06-16T01:29:16Z

Svrieze:

[[Image:Abecasis_group_photo_cropped.jpg|900px|center|Group Photo 2013]]



== Mission Statement ==

We are developing and applying computational and statistical tools to further understanding of complex human diseases, such as cardiovascular disease and diabetes.

== Leadership ==

[[Goncalo Abecasis]] is currently the Felix Moore Collegiate Professor of Biostatistics at the University of Michigan School of Public Health.

== Current Members ==

=== Research Fellows ===

* Goo Jun
* Christian Fuchsberger
* Alex Tsoi
* [[Dajiang Liu]]
* Lars Fritsche
* [[Scott Vrieze]]

=== International Visitors ===

* Andrea Maschio
* Giorgio Pistis
* Eleanora Porcu

=== Graduate Students ===

* Su Chu

* Sayantan Das

* Shuang Feng

* Dan Hovelson

* [[Alan Kwong]]

* Ben Lerch

* Sara Rashkin

* Sebanti Sengupta

* Vivian Wang

* Xiaowei Zhan

* Tingting Zhou

=== Staff ===

* Laura Baker

* Tom Blackwell

* [[Sean Caron]]

* Jennifer Bragg-Gresham

* Kevin Li

* Mary Kate Wing

== Alumni ==

=== Former Research Faculty ===

Hyun Min Kang (''graduated in 2011''), now Assistant Professor at the [http://www.sph.umich.edu/biostat/ University of Michigan School of Public Health, Department of Biostatistics].

=== Former Research Fellows ===

Weimin Chen (graduated 2007), now Assistant Professor at the [http://people.virginia.edu/~wc9c/ Department of Public Health Sciences & Center for Public Health Genomics, University of Virginia]

Bingshan Li (graudated 2011), now Assistant Professor at the [https://medschool.vanderbilt.edu/cqs/people/Bingshan/Li/cqs-faculty-members Center for Quantitative Sciences, Vanderbilt University]

Serena Sanna (graduated 2007), now an investigator at the [http://www.serenasanna.com/ Istituto di Neurogenetica e Neurofarmacologia in Sardinia, Italy]

Paul Scheet (graduated 2008), now Assistant Professor at [http://faculty.mdanderson.org/Paul_Scheet/Default.asp?SNID=221605974 Department of Epidemiology, University of Texas MD Anderson Cancer Center]

Carlo Sidore (graduated 2012), now an investigator at the [http://www.serenasanna.com/ Istituto di Neurogenetica e Neurofarmacologia in Sardinia, Italy]

William Stewart (graduated 2008), now Assistant Professor at [http://www.mathmed.org/#William_Stewart Battelle Center for Computational Medicine, Departments of Statistics and Pediatrics, National Children's Hospital and Ohio State University]

=== Former Doctoral Students ===

Wei Chen (graduated 2011), now Assistant Professor at the [http://www.chp.edu/CHP/Chen%2C+Wei%2C+PhD Department of Pediatrics, University of Pittsburgh Medical Center]

Jun Ding (graduate 2010), now Staff Scientist / Facility Head at the [http://www.grc.nia.nih.gov/branches/lg/lg.htm Laboratory of Genetics, National Institute on Aging (NIH)].

Yun Li (graduated 2009), now Assistant Professor at the [http://www.sph.unc.edu/?option=com_profiles&Itemid=6138&profileAction=ProfDetail&pid=708777879 Department of Biostatistics, University of North Carolina].

Youna Hu (graduated 2012), now a Research Fellow [http://cteg.berkeley.edu/members/hu.html working with Rasmus Nielsen at Berkeley]

Mingyao Li (graduated 2005), now Associate Professor at the [http://www.cceb.upenn.edu/faculty/index.php?id=159 Department of Biostatistics and Epidemiology, University of Pennsylvania]

Liming Liang (graduated 2009), now Assistant Professor at the [http://www.hsph.harvard.edu/faculty/liming-liang/ Departments of Biostatistics and Epidemiology, Harvard University]

Tasha Fingerlin (graduated 2003), now Associate Professor at the [http://www.ucdenver.edu/academics/colleges/PublicHealth/departments/Epidemiology/About/Faculty/Pages/FingerlinT.aspx Section of Epidemiology and Community Health, University of Colorado Health Sciences Center]

Andrew Skol (graduated 2006), now Assistant Professor at the [http://med-www02.bsd.uchicago.edu/339/FacultyPro/faculty_profile.aspx?empl_id=10164 Section of Genetic Medicine, University of Chicago]

Jin Zhen (graduated 2009), now working in the Pharmaceutical Industry.

=== Former Masters Students ===

Melinda Curran

Vesela Gateva

Xijing Han

Elizabeth Jewell

Yanming Li

Heather Munro

Theresa Scott (nee Daigneault)

Matthew Snyder

Yuan Wei

Abigail Woodroffe

Zaojun Ye

Matthew Zawitowski

Anita Yu Zhao

=== Visitors ===

Toshiko Tanakato

== Really Useful Stuff ==

* [[Abecasis Group Awards]]
* [https://calendars.office.microsoft.com/pubcalstorage/m3n2kr0z1470909/Goncalo_Abecasis_Calendar(1).ics Goncalo's Calendar]

Abecasis Lab

2013-06-16T01:27:06Z

Svrieze:

Group photo 2013
[[Image:Abecasis_group_photo_cropped.jpg|900px|center|Group Photo 2013]]

Group photo 2009
[[Image:2009.08_Group_Retreat_Photo.jpg|400px|center|Group Photo]]

== Mission Statement ==

We are developing and applying computational and statistical tools to further understanding of complex human diseases, such as cardiovascular disease and diabetes.

== Leadership ==

[[Goncalo Abecasis]] is currently the Felix Moore Collegiate Professor of Biostatistics at the University of Michigan School of Public Health.

== Current Members ==

=== Research Fellows ===

* Goo Jun
* Christian Fuchsberger
* Alex Tsoi
* [[Dajiang Liu]]
* Lars Fritsche
* [[Scott Vrieze]]

=== International Visitors ===

* Andrea Maschio
* Giorgio Pistis
* Eleanora Porcu

=== Graduate Students ===

* Su Chu

* Sayantan Das

* Shuang Feng

* Dan Hovelson

* [[Alan Kwong]]

* Ben Lerch

* Sara Rashkin

* Sebanti Sengupta

* Vivian Wang

* Xiaowei Zhan

* Tingting Zhou

=== Staff ===

* Laura Baker

* Tom Blackwell

* [[Sean Caron]]

* Jennifer Bragg-Gresham

* Kevin Li

* Mary Kate Wing

== Alumni ==

=== Former Research Faculty ===

Hyun Min Kang (''graduated in 2011''), now Assistant Professor at the [http://www.sph.umich.edu/biostat/ University of Michigan School of Public Health, Department of Biostatistics].

=== Former Research Fellows ===

Weimin Chen (graduated 2007), now Assistant Professor at the [http://people.virginia.edu/~wc9c/ Department of Public Health Sciences & Center for Public Health Genomics, University of Virginia]

Bingshan Li (graudated 2011), now Assistant Professor at the [https://medschool.vanderbilt.edu/cqs/people/Bingshan/Li/cqs-faculty-members Center for Quantitative Sciences, Vanderbilt University]

Serena Sanna (graduated 2007), now an investigator at the [http://www.serenasanna.com/ Istituto di Neurogenetica e Neurofarmacologia in Sardinia, Italy]

Paul Scheet (graduated 2008), now Assistant Professor at [http://faculty.mdanderson.org/Paul_Scheet/Default.asp?SNID=221605974 Department of Epidemiology, University of Texas MD Anderson Cancer Center]

Carlo Sidore (graduated 2012), now an investigator at the [http://www.serenasanna.com/ Istituto di Neurogenetica e Neurofarmacologia in Sardinia, Italy]

William Stewart (graduated 2008), now Assistant Professor at [http://www.mathmed.org/#William_Stewart Battelle Center for Computational Medicine, Departments of Statistics and Pediatrics, National Children's Hospital and Ohio State University]

=== Former Doctoral Students ===

Wei Chen (graduated 2011), now Assistant Professor at the [http://www.chp.edu/CHP/Chen%2C+Wei%2C+PhD Department of Pediatrics, University of Pittsburgh Medical Center]

Jun Ding (graduate 2010), now Staff Scientist / Facility Head at the [http://www.grc.nia.nih.gov/branches/lg/lg.htm Laboratory of Genetics, National Institute on Aging (NIH)].

Yun Li (graduated 2009), now Assistant Professor at the [http://www.sph.unc.edu/?option=com_profiles&Itemid=6138&profileAction=ProfDetail&pid=708777879 Department of Biostatistics, University of North Carolina].

Youna Hu (graduated 2012), now a Research Fellow [http://cteg.berkeley.edu/members/hu.html working with Rasmus Nielsen at Berkeley]

Mingyao Li (graduated 2005), now Associate Professor at the [http://www.cceb.upenn.edu/faculty/index.php?id=159 Department of Biostatistics and Epidemiology, University of Pennsylvania]

Liming Liang (graduated 2009), now Assistant Professor at the [http://www.hsph.harvard.edu/faculty/liming-liang/ Departments of Biostatistics and Epidemiology, Harvard University]

Tasha Fingerlin (graduated 2003), now Associate Professor at the [http://www.ucdenver.edu/academics/colleges/PublicHealth/departments/Epidemiology/About/Faculty/Pages/FingerlinT.aspx Section of Epidemiology and Community Health, University of Colorado Health Sciences Center]

Andrew Skol (graduated 2006), now Assistant Professor at the [http://med-www02.bsd.uchicago.edu/339/FacultyPro/faculty_profile.aspx?empl_id=10164 Section of Genetic Medicine, University of Chicago]

Jin Zhen (graduated 2009), now working in the Pharmaceutical Industry.

=== Former Masters Students ===

Melinda Curran

Vesela Gateva

Xijing Han

Elizabeth Jewell

Yanming Li

Heather Munro

Theresa Scott (nee Daigneault)

Matthew Snyder

Yuan Wei

Abigail Woodroffe

Zaojun Ye

Matthew Zawitowski

Anita Yu Zhao

=== Visitors ===

Toshiko Tanakato

== Really Useful Stuff ==

* [[Abecasis Group Awards]]
* [https://calendars.office.microsoft.com/pubcalstorage/m3n2kr0z1470909/Goncalo_Abecasis_Calendar(1).ics Goncalo's Calendar]

File:Abecasis group photo cropped.jpg

2013-06-16T01:24:40Z

Svrieze:

Scott Vrieze

2013-06-14T12:12:38Z

Svrieze:

Scott Vrieze

2013-06-12T16:41:51Z

Svrieze:

Scott is a research fellow working with [[Goncalo Abecasis]] to investigate the genetic etiology and architecture of substance use behaviors. Scott earned his PhD at the University of Minnesota, where he worked with [http://www.psych.umn.edu/people/facultyprofile.php?UID=wiacono Bill Iacono], [http://www.psych.umn.edu/people/facultyprofile.php?UID=mcgue001 Matt McGue], and
[http://www.psych.umn.edu/people/facultyprofile.php?UID=grove001 Will Grove].

In August 2014, after finishing his postdoc at Michigan, Scott will join the faculty at the University of Colorado Boulder as an assistant professor. He'll be at the Institute for Behavior Genetics and an assistant professor in the Clinical Psychology program.

2013-05-24T16:40:01Z

Svrieze: /* Primary Call */

EMADS

2013-05-24T16:39:13Z

Svrieze: /* Detailed Evaluation of Chromosome 15 Region */

EMADS

2013-05-24T12:54:53Z

Svrieze: /* Analyst Call */

EMADS Primary Analysis Plan

2013-05-13T18:42:37Z

Svrieze:

[[File:EMADS_thumb5.png|thumb|340px|right]]
Exome Meta-Analysis of Drinking and Smoking (EMADS) Analysis Plan

Parent page: [[EMADS]]

== Genotypes ==
All samples have some version of the Exome Chip or exome/whole genome sequences. Individual studies will provide information about the manufacturer and version of the exome chip, or sequencing platform, they are using.

== Inclusion Criteria ==
For our first analysis, samples must be between ages 18 and 70 (inclusive) and be of European ancestry. We hope to extend analysis to other ancestral groups in the future.

== Quality Control ==
We leave calling algorithms, marker filters, and sample filters to the discretion of local sites, although we will evaluate the possibility of batch effects (where batch might be a study) during the meta-analysis step.

For reference, four currently participating studies have used Illumina chips and Illumina’s genotype caller in Genome Studio (Gencall). Some studies also implemented some manual curation involving reclustering the intensity data of ~1500 markers.

== Strand Orientation ==
Chip TOP allele annotations (typical output from Gencall) need to be updated to the forward strand of build 37.

The strand file for exome chip version 12v1_A is available at:
http://www.well.ox.ac.uk/~wrayner/strand/HumanExome-12v1_A-b37-strand.zip

Usage instructions, including scripts, are available here:
http://www.well.ox.ac.uk/~wrayner/strand/

Future strand files will also be available at that site.

== Phenotypes ==

=== (1) Average cigarettes smoked per day, either as a current smoker or former smoker ===
Individuals who either never smoked, or on whom we have no data (e.g., someone was a former smoker but former smoking was never assessed) will be excluded from analysis. Only cigarettes will be included in the estimate. If preferable, repeated measures designs (longitudinal data) can use all assessments by scaling and correcting for covariates within waves of assessment, then averaging across assessments.

For studies that collect a quantitative measure of CPD, where the respondent is free to provide any integer (e.g., 13 CPD), '''we will bin responses into the following bins: 1-10, 11-20, 21-30, 31+.''' If some study collected binned responses from the outset, and those bins happen to differ from ours (e.g., 1-5, 6-15, etc.), then we will simply use whatever bins the study has collected. Please contact Scott if your study does something completely different.

In analysis, it is likely easiest to consider the bins to correspond to the following numerical values.
*1 = 1-10
*2 = 11-20
*3 = 21-30
*4 = 31+

Please note, however, that when we report descriptive statistics about our phenotypes we will want to report the original participant responses. Even though we'll bin the data for analysis, we'll still report quantitative CPD (when possible) when we describe each study's phenotype in eventual publications.

=== (2) Smoking Initiation ===
Every study had some usable measure of whether a respondent has ever regularly smoked. Almost all asked directly. Some have necessary information for this variable (e.g., 100 cigs lifetime? Ever smoked every day for 2 weeks straight?).

Note that we’re among the first groups conducting such meta-analyses, and our analysis pipeline is currently restricted to continuous traits. Until methods are developed for binary traits, it is proposed that we analyze smoking initiation as a continuous trait.

=== (3) Pack Years ===
Number of cigarettes per day, divided by 20, then multiplied by the number of years the person has smoked. For this measure please use the quantitative CPD, and not the binned responses discussed above under the CPD heading. If your study collected binned responses from the outset, please use the midpoint of the range in calculating Pack Years. For example, individuals stating they smoked 11-20 CPD would be assumed to have smoked 15.5 on average

=== (4) Age of Initiation of Smoking ===
The age an individual first became a regular smoker. Please check for obvious outliers and remove them (4 years old or younger).

=== (5) Average drinks per week, either as a current drinker or former drinker ===
Individuals who either never drank, or on whom we have no data (e.g., someone was a former drinker but former drinking was not assessed) will be excluded from analysis. Please combine all types of liquor in the total estimate. If preferable, repeated measures designs (longitudinal data) can use all assessments by scaling and correcting for covariates within waves of assessment, then averaging across assessments.

If your study forced the respondent to report ranges (e.g., 1-5, 6-10, 11-15, 16-20, etc.) please simply use the midpoint of the range. For example, if one range is 1-5 DPW, we assume they drink 2.5 DPW on average. Then use these midpoints in all subsequent analysis.

== Covariate Correction (to be done after left-anchoring and log transformation)==
For CPD we will consider the binned responses to be on a quantitative scale from 1-4 (see above under the CPD phenotype description). '''CPD therefore will not require transformation''' prior to covariate correction.

For the other four quantitative phenotypes (Pack Years, Age of Initiation, Drinks Per Week) please '''left-anchor''' the distribution at 1 and '''log-transform it'''. Left-anchoring, such that no value is less than 1, prevents the log-transform from returning nonsensical values like negative infinity. Then apply the covariate correction to the transformed phenotypes. This step is unnecessary for the binary smoking initiation phenotype

Appropriate covariates can often be study-specific. We will depend on local investigators to determine the most appropriate covariates. We list here some covariates that will likely be necessary.

=== Main Effects ===
*Age
**At assessment in current smokers/drinkers
**Age of smoking/drinking for former smokers/drinkers could be age at quitting
**At assessment for Pack Years, Smoking Initiation, and Age of Initiation, regardless of current/former smoking status
*Age squared
*Sex
*Date of birth (or year, or range)
*Cohort
*Height, weight, BMI, for drinking (a single beer has different effects on a 200 lb man versus a 100 lb woman)
*Genetic principle components (alternatively could use empirical kinships in rare-metal-worker)
*Adolescence versus adulthood (e.g., < 21 years of age versus >=21)
*Date of assessment (e.g., the calendar year of the assessment)?
*Current versus former smoker/drinker?

=== Interactions ===
*Sex X Adolescence interaction
*Sex X Age interaction
*Sex X Weight/Height/BMI interaction
*Age X Adolescence interaction

== Analysis of Covariate-Corrected Phenotypes ==
The basic analysis is two-stage. In the first stage, local investigators produce, for each phenotype, a set of single-variant summary statistics using a tool developed at the University of Michigan. In the second stage, these summary statistics are pooled for meta-analysis. All single-variant and gene-based (‘burden’) tests can be conducted from the summary statistics.

These two stages are now described in more detail.

=== Stage 1: Local Sites Produce Summary Statistics Using Rare-Metal-Worker ===
The meta-analysis step (stage 2) requires a very specific set of summary statistics, which includes single-variant test statistics and p-values, as well as the test statistic covariance matrix within a sliding window (default: 1Mb). Shuang Feng, Dajiang Liu, and Goncalo Abecasis at the University of Michigan have developed software specifically for this purpose, called Rare-Metal-Worker. Software and usage instructions to generate necessary single variant statistics is available at [http://genome.sph.umich.edu/wiki/Rare-Metal-Worker Rare-Metal-Worker].If there are installation problems please let Scott know.

'''NOTE:''' It is essential that analysis proceeds in the following order. For CPD, please bin quantitative responses and correct for covariates to obtain residuals. For Pack Years, Age of Initiation, and Drinks Per Week, please left-anchor responses at 1, log-transform, and then correct for covariates to obtain residuals. In this way we will obtain residualized phenotypes ready for analysis with Rare-Metal-Worker. These steps are probably easier to do in your software of choice.

Now, using the residualized phenotypes in a .ped file please specify the --makeResiduals and --inverseNormalize options. These will correct for the intercept and then inverse-normalize the phenotype prior to conducting association tests.

'''Marker Grid for Fast-LMM Empirical Kinship'''

If you plan to use the Fast-LMM mixed model capability in Rare-Metal-Worker, it is likely preferable that you construct your kinship matrix either 1) with genome-wide markers from a GWAS panel (or 2nd generation exome chip) or 2) a subset of selected markers from the exome chip array. A list of markers can be obtained from Scott. There are many common markers on the first version of the exome chip, and many were selected for fine mapping (of MHC) or because of prior GWAS signals. These latter markers would ideally be excluded from the set of markers used to construct the empirical kinship matrix.

'''Running Times'''

Run times depend heavily on the type of analysis. If all samples are unrelated, and no kinship matrix is used, then run times should be relatively fast (tens of minutes). If a mixed model is used, for example using an empirical kinship, then in samples of a few thousand rare-metal-worker should take less than 20 minutes to complete. In larger samples (~10,000 or more with phenotype data) it can take several days to complete an exome-chip-wide scan.

'''Submitting Results for Meta-Analysis'''

All output files from Rare-Metal-Worker can then be uploaded to an sftp server at the University of Michigan for central analysis -- please email [mailto:svrieze@umich.edu Scott Vrieze] for the hostname, username, and password.

=== Stage 2: Single-Variant and Gene-Based Meta-Analysis ===
Single-Variant Tests
We will do meta-analysis of score statistics for individual variants weighting by sample size using [[Rare-Metal]]. Details are provided at that site.

== Gene-Based Tests ==
Gene-based tests can be conducted centrally by Scott using output from Rare-Metal-Worker.

We will implement three burden tests.
# First, a Variable Threshold Combined Multivariate and Collapsing count method ([http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032073/ VTCMC]), where the number of rare alleles is counted in each gene, then the gene is tested for association. The threshold for what variants are considered "rare" (MAF < .05? MAF < .01?) is set adaptively such that the result minimizes the p-value obtained.
# Second, we will use SKAT ([http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3135811/ SKAT]) for all rare variants (MAF < .05) within a gene. SKAT allows for variants with opposite directions of effect within the same gene, whereas the variable threshold combined multivariate and collapsing method does not.
# Third, we will use a burden test developed by Madsen and Browning ([http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000384 M-B]) where the number of rare alleles is counted in each gene, then the gene is tested for association, but alleles in the count are weighted by the inverse of the MAF. Thus rarer alleles are given more weight than common allele.

== Genotype Annotation ==
Gene-based burden tests can be augmented with genotype annotation. We currently plan to use only nonsynonymous variants from ANNO-generated annotations relative to GENCODE transcripts. All annotation can be done centrally at the meta-analysis stage to ensure consistency across sites.

== Multivariate Test ==
We will pursue development of a multivariate test for drinking and smoking jointly. This could be as simple as, on a per-marker or per-gene basis, averaging effect sizes or p-values for meta-analytic CPD and DPW p-value results.

== Further Downstream Analysis ==
To be determined. Will depend on results from the main analysis above.

We more than welcome individual sites to propose additional analysis, as well as to take the lead on additional projects related to the primary aims of this meta-analysis.

== Descriptive Phenotype Information ==
When it comes time to publish our results we'll need descriptive information about our phenotypes. In anticipation of this Scott has sent around some draft tables. The tables will contain descriptive information about your study and phenotypes. For each phenotype we need:
*sample size of non-missing observations
*mean, standard deviation, range for quantitative phenotypes (including quantitative CPD, before binning)
*Counts for smoking initiation, a binary phenotype
*The 5x5 correlation matrix between residualized phenotypes, as well as the sample size contributing to each correlation.

2013-05-13T18:16:07Z

Svrieze:

EMADS

2013-05-13T18:13:51Z

Svrieze:

[[File:EMADS_thumb4.png|thumb|360px|right]]
Welcome! This is an informational page devoted to the EMADS consortium, the exome meta-analysis of drinking and smoking.

If you're looking for the analysis plan see this page: [[EMADS Primary Analysis Plan|Analysis Plan]].

== Contact ==

If you are interested in contributing to the consortium please email [mailto:svrieze@umich.edu Scott Vrieze] or [mailto:goncalo@umich.edu Goncalo Abecasis].

== Description and Rationale ==

EMADS is a collaborative effort of many studies to investigate the potential role of rare exomic variation on drinking and smoking phenotypes. Through the effort we hope to extend results of previous GWAS meta-analyses of drinking and smoking as well as identify novel genetic associations. As of this writing over 20 studies plan to contribute a total of 120,000 samples. Several other studies hope to contribute but, given the novelty of the exome chip genotyping array, are awaiting completion of genotyping.

We use the exome chip as the primary genotyping technology because larger samples have been genotyped on the exome chip (compared to sequencing). However, studies with sequences are more than welcome and can easily be included in our current analysis efforts.

== Organization ==

=== Primary Call ===
We have a primary teleconference on a monthly basis. Senior and junior investigators from each site contribute to consortium planning.

'''The next primary call will take place on May 24 2013 at 11am EDT (4pm BST).'''

=== Analyst Call ===
In addition, we have a biweekly analyst call to discuss technical issues and analysis coordination.

'''The next analyst call will take place on May 16 2013 at 11am EDT (4pm BST)'''

== Analysis Projects ==

=== Exome-Wide Meta-Analysis ===

The current primary goal of the EMADS consortium is an exome-wide analysis of nonsynonymous variation in smoking and drinking phenotypes.

Please see the [[EMADS Primary Analysis Plan|Analysis Plan]].

=== Detailed Evaluation of Chromosome 15 Region ===
We hope to expand the list of possible projects using the data available through our consortium. These may include a project headed by Nancy Saccone and Laura Beirut on detailed analysis of the chromosome 15 region and smoking.

== Guidelines for participation ==

While we have no strict policies or procedures, there are a few best practices guidelines to consider.

*We believe it’s best if participants refrain from contribution to similar meta-anlayses that duplicate our efforts. Ideally, similar meta-analyses would join efforts.
*Any work that uses data from EMADS should, at the very least, include the consortium name in the list of authors. Depending on the extent of involvement of individuals in EMADS, individual contributors should also be included in the author list.

== Authorship ==

While authorship is decided on an individual basis for each paper (depending on contribution), typically, authorship is arranged in groups. We hope the GIANT investigators will forgive us for adopting their authorship guidelines.

# A group of 6 or fewer junior investigators who strongly led the efforts, usually starred to denote equal contribution, followed by additional junior investigators who played key, central roles.
# In alphabetical order, junior investigators who had substantial individual contributions but not as much as those in Group 1. Typically, these might be lead analysts or other junior investigators who made a sizable contribution such as GWA analyses performed specifically for the paper.
# In alphabetical order, junior investigators who had notable individual contributions but not as much as those in Groups 1 or 2. Typically, these might be lead analysts for replication cohorts, providing results for a group of top hits.
# In alphabetical order, junior and senior investigators who had contributions worthy of authorship (participating in analysis, phenotype collection, genotyping, oversight of cohorts, etc. that was specific to the paper) but not as much as those in the other groups.
# In alphabetical order, senior investigators who had contributions worthy of authorship and contributed more than those in group 4. Typically, these might be a lead PI of a participating cohort who did not participate as strongly in EMADS activities as those in group 6.
# In alphabetical order, senior investigators who participated strongly in EMADS activities but did not strongly lead/oversee the writing and/or analysis for the paper. Typically, these might be members of the EMADS steering committee or leaders of other key EMADS activities.
# The senior investigators who strongly led/oversaw the writing and/or analysis of the paper, including a subset that are co-corresponding authors (usually 6 or fewer).

File:EMADS thumb4.png

2013-05-13T18:13:36Z

Svrieze:

File:EMADS thumb3.png

2013-05-13T18:11:54Z

Svrieze: