Rare variant architecture of epilepsy

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Great paper from the Epi25 collaborative on the rare variant associations of different types of epilepsy based on WES analysis of ~54k individuals. This paper is packed with insightful findings. I'll highlight a few 🧵 medrxiv.org/content/10.1101/2023.02.22.23286310v1

The authors studied three types of eplipesy. One rare, severe form: developmental and epileptic encephalopathies (DEE N=~2k), which is often comorbid with developmental delay.

Two common, milder forms: genetic generalized epilepsy (GGE, N=5.5k) characterized generalized seizures and non-acquired focal epilepsy (NAFE, N=9.2k) characterized by focal seizures.

1. Common vs rare variant architecture We should think about genetic architecture of a disease in terms of both common and rare variants. So, you can gain more insights if you read the GWAS paper alongside that looked at the common variant associations. medrxiv.org/content/10.1101/2022.06.08.22276120v1

A general rule is that rare and severe disease get more contributions from rare and large effect variants whereas common and milder form get more contributions from common and small effect variants, which you can appreciate from the results.

GGE (n=5.5k) and NAFE (9.2k), despite having larger sample sizes than DEE (2k), had lower power to discover rare variant associations. Analysis of burden of protein truncating variants (PTVs) yielded 5 sig genes for DEE, 1 for NAFE and none for GGE.

On the other hand, GGE has stronger contributions from common variants with SNP heritability estimate nearing ~40%. This Manhattan plot displaying 25 hits is based on a GWAS of GGE in 7.4k cases (only slightly larger N than ExWAS) and ~50k controls.

The NAFE subtype is a special and interesting case. Despite being common and so having a large N (16.3k cases) they yielded no GWAS hits. Reasons suspected: phenotype heterogeneity, somatic mutations etc.

Keeping NAFE aside, there is a clear contrast in the common vs rare variant contributions between DEE and GGE. 2. Gaining power by gene sets As I've pointed many times before, we can increased statistical power by looking at gene sets than indiv genes. twitter.com/doctorveera/status/1513349069351006209?s=20

After studying for individual gene level associations, the authors looked at associations at gene set levels, particularly gene families and those encode protein complexes.

One of the strongest associations was seen between NAFE subtype and genes encoding GATOR complex proteins.

If you go back to the individual gene associations of NAFE, you'll notice that, in addition to the top hit (DEPDC5), there are two other subthreshold hits that are components of GATOR complex: NPRL3 and NPRL2.

This finding reminds us a lesson we've learned in the past: one way to identify novel genes is to look at the sub-threshold genes that are members of the pathways implicated by significant genes. twitter.com/doctorveera/status/1570300260081336323?s=20

3. Converging evidence across multiple variant types Strong associations with PTVs than missense variants suggest that the diseaase mechanism is due to haploinsufficiency rather than gain of function (which seem to be the case for GATOR complex genes)

One way to confirm that haploinsufficiency leads to disease is to look at deletion CNVs encompassing the gene of interest. The authors found many of the genes implicated by PTVs also show association with deletion CNVs.

Among which, NRPL3 showed impressive convergence between PTVs and CNVs. Combining both the evidence together rendered NRPL3 exome-wide significant.

This is an orthogonal way of replicating genetic findings. In addition to replicating the association with same genetic variant in independent samples, we shud also search for genetic associations with independent variants in the same sample (i.e. identify allelic series).

4. Phenotypic heterogeneity This is an extremely important concept when it comes to developmental genes. Phenotypic heterogeneity arises when different mutations in a gene lead to different phenotypes.

This phenotypic heterogeneity can be milder with different mutations causing same disease with varied clinical presentations. Here is a great example. twitter.com/doctorveera/status/1516638245471211522?s=20

Or can be severe, with different mutations causing strikingly different phenotypes. e.g. there are genes where missense variants cause NDD and PTVs cause schizophrenia. twitter.com/doctorveera/status/1513349259403268096?s=20

This is important to know as sometimes depletion of a class of variant in the disease group can be misinterpreted as absence of pathogenicity or even worse:protective associations, where in truth those variants are hiding among a diff disease group.

In the current study the authors see strong enrichment for ultrarare missense variants but not PTVs in GABA receptor complex genes, which is also reported in earlier studies. pubmed.ncbi.nlm.nih.gov/31327507/

The lack of PTV enrichment in these genes simply could be due to ascertainment bias where the cases are enriched for early onset severe cases while PTVs are hiding in milder cases and will come out only with large N twitter.com/doctorveera/status/1516638278291648513?s=20

The authors also show a beautiful example of a gene, KDM6B, with strong phenotypic heterogeneity where PTVs cause NDD but missense variants cause epilepsy.

Epilepsy is one of the most common neurological conditions and our knowledge of their genetic etiologies has been so far predominantly based on studies of isolated cases/families with highly penetrant mutations.

It's fantastic that now we are able to study the genetics of epilepsy at scale in an unbiased hypothesis free manner. Great accomplishment by the Epi25 collaborative team of researchers. 👏 👏