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#70 Prioritizing drug target genes with Marie Sadler
In this episode, Marie Sadler talks
about her recent Cell Genomics paper, Multi-layered genetic approaches to
identify approved drug targets.
Previous studies have found that the drugs that target a gene linked to the
disease are more likely to be approved. Yet there are many ways to define what
it means for a gene to be linked to the disease. Perhaps the most
straightforward approach is to rely on the genome-wide association studies (GWAS) data,
but that data can also be integrated with quantitative trait loci (eQTL or pQTL) information
to establish less obvious links between genetic variants (which often lie
outside of genes) and genes. Finally, there’s exome sequencing, which, unlike
GWAS, captures rare genetic variants. So in this paper, Marie and her
colleagues set out to benchmark these different methods against one another.
Listen to the episode to find out how these methods work, which ones
work better, and how network propagation can improve the prediction accuracy.
Links:
(Marie C. Sadler, Chiara Auwerx, Patrick Deelen, Zoltán Kutalik)
Thank you to Jake Yeung, Michael Weinstein, and other Patreon members for supporting this episode.
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By Roman Cheplyaka
4.8
3434 ratings
In this episode, Marie Sadler talks
about her recent Cell Genomics paper, Multi-layered genetic approaches to
identify approved drug targets.
Previous studies have found that the drugs that target a gene linked to the
disease are more likely to be approved. Yet there are many ways to define what
it means for a gene to be linked to the disease. Perhaps the most
straightforward approach is to rely on the genome-wide association studies (GWAS) data,
but that data can also be integrated with quantitative trait loci (eQTL or pQTL) information
to establish less obvious links between genetic variants (which often lie
outside of genes) and genes. Finally, there’s exome sequencing, which, unlike
GWAS, captures rare genetic variants. So in this paper, Marie and her
colleagues set out to benchmark these different methods against one another.
Listen to the episode to find out how these methods work, which ones
work better, and how network propagation can improve the prediction accuracy.
Links:
(Marie C. Sadler, Chiara Auwerx, Patrick Deelen, Zoltán Kutalik)
Thank you to Jake Yeung, Michael Weinstein, and other Patreon members for supporting this episode.
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