4366

DATAFREEZE 1 WGS CASES

1832

DATAFREEZE 1 WGS CONTROLS

6538

DATAFREEZE 2 WGS CASES

2415

DATAFREEZE 2 WGS CONTROLS

15000

GOAL TO WGS PATIENTS

7500

GOAL TO WGS CONTROLS


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Coverage


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Burden Testing

Genetic association testing (i.e., genome-wide association studies or GWAS) of common variants (minor allele frequency (MAF) > 1%) typically interrogate single variants through application of linear or logistic regression models. When testing rare variation for association to disease, however, statistical power to detect association decreases rapidly (due to low counts of alleles observed in the data). An alternate approach to single-SNP testing is to combine information across multiple variant sites contained within a specific region (typically defined by a gene and therefore referred to as genic burden testing). Aggregating information across multiple sites can improve power to detect association signals and reduce the multiple testing burden. We have performed rare variant burden using firth logistic regression.

The figure you see below shows the exons (orange blocks) in this gene with the variants (triangles) that were observed in the current dataset. Please hover over the variants for more information. If you are interested, you can zoom in by clicking and draging your mouse across the x-axis.

Various subsets of variants can be made (e.g. MAF<1%, MAF<0.5% or only those variants that were not observed in ExAC).

There is a tradeoff between including more variants, which might yield higher statistical power to detect association, and including to much noise and therefor reduce power. We have performed firth logistic regression on only disruptive variants (fewer variants, but hopefully a good signal to noise ratio), on disruptive + damaging variants and disruptive + damaging + missense-non-damaging variants (many variants, more noise). You can select which subset you are interested in.

If you have selected the subset of variants that you are interested in, then the genic burden plot will be updated. The mini-manhattan plots, which you can see below the genic burden pot, for Family-wise, pathways and drugable categories will be updated as well. For more information on those plots, please see the background infomation on each of the tabs.


Gene Expression



Variant Table

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Literature





Project MinE - WGS Data Freeze 2




Burden testing

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Burden Testing



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Genic



Genic

















Genic Burden Testing - Chinese ancestry


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FAQ

How many cases and controls are in the current dataset? The depth of coverage, burden testing results and variant frequencies are based on on 4,366 Amyotrofic Lateral Sclerosis cases and 1,832 age and gender matched controls.
In what genome build are the variants? All data is in build hg19/GRCh37..
Why do the allele frequencies of the variants differ from ExAC/gnomAD? The allele frequencies are based on different datasets. As a population reference we have added the gnomAD annotation of both genomes and exomes. Please see the variant table on the gene specific pages.
Can I get a copy of the 2016 GWAS summary statistics? Yes you can, either click here , or go to https://www.projectmine.com/research/publications/
Can I get a copy of the most recent burdentesting results based on the Project MinE WGS dataset? Yes you can, please click here
I would like to check for duplicated samples between my own dataset and the Project MinE WGS dataset, how do I do that? Simple, just email us at info@projectmine.com and indicate that you would like to check for duplicates. We will then send you an archive with checksum hashes for all the samples
Can I get access to individual-level genotype data? Yes, if you wish to request data, please visit https://www.projectmine.com/research/data-sharing/
Are there any restrictions on data usage? Please read the Project MinE Data Access Policy
What are the contact details for Project MinE? If you have additional questions, please send an email to info@projectmine.com or visit our website at www.projectmine.com
What browser should I use? Preferably use Chrome v70+, Firefox v60+ or Safari 12+ (tested on HighSierra only). Chrome and Firefox have been tested on Mac, Windows and Linux

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