Another paper illustrating the limitations of the TWAS method which I think should lead to greater caution in using TWAS results to assign causality in genetic studies
https://www.biorxiv.org/content/10.1101/2020.08.31.273458v2
https://www.biorxiv.org/content/1... href="https://twitter.com/MarylynRitchie">@MarylynRitchie https://twitter.com/biorxivpreprint/status/1300867559701520388">https://twitter.com/biorxivpr...
https://www.biorxiv.org/content/10.1101/2020.08.31.273458v2
https://www.biorxiv.org/content/1... href="https://twitter.com/MarylynRitchie">@MarylynRitchie https://twitter.com/biorxivpreprint/status/1300867559701520388">https://twitter.com/biorxivpr...
The strongest metabolite hit ever reported is for UGT1A1 and bilirubin (close to 1e-20000 in https://www.biorxiv.org/content/10.1101/660506v1.)
In">https://www.biorxiv.org/content/1... this TWAS paper, all the genes in the region are tagged, even genes with no relation to bilirubin.
In">https://www.biorxiv.org/content/1... this TWAS paper, all the genes in the region are tagged, even genes with no relation to bilirubin.
As they note in the TWAS paper this probably reflects a shared regulatory mechanism.
Not stated in this paper is this is likely the source of the high false positive rate in TWAS and other eQTL-related analyses.
Not stated in this paper is this is likely the source of the high false positive rate in TWAS and other eQTL-related analyses.
The authors miss an opportunity to highlight another example of this. They find only 2 "hits" for HDL levels. One is the well-known CETP gene.
Out of the entire genome the only other hit is...
The gene right next door to CETP:
Out of the entire genome the only other hit is...
The gene right next door to CETP: