Back

Whole Exome Sequencing in dense families suggests genetic pleiotropy amongst Mendelian and complex neuropsychiatric syndromes

Ganesh, S.; Vemula, A.; Bhattacharjee, S.; Mathew, K.; Ithal, D.; Navin, K.; Nadella, R. K.; Viswanath, B.; The ADBS Consortium, ; Jain, S.; Purushottam, M.

2021-11-05 genetic and genomic medicine
10.1101/2021.11.04.21265926 medRxiv
Show abstract

Whole Exome Sequencing (WES) studies provide important insights into the genetic architecture of serious mental illness (SMI). Genes that are central to the shared biology of SMIs may be identified by WES in families with multiple affected individuals with diverse SMI (F-SMI). We performed WES in 220 individuals from 75 F-SMI families and 60 unrelated controls. Within pedigree prioritization employed criteria of rarity, functional consequence, and sharing by [≥]3 affected members. Across the sample, gene and gene-set-wide case-control association analysis was performed with Sequence Kernel Association Test, accounting for kinship. In 14/16 families with [≥]3 affected individuals, we identified a total of 79 rare predicted deleterious variants in 79 unique genes shared by [≥]3 members with SMI and absent in 60 unrelated controls. Twenty (25%) genes were implicated in monogenic neurodevelopmental syndromes in OMIM, a fraction that is a significant overrepresentation (Fishers Exact test OR = 2.47, p = 0.001). In gene-set wise SKAT, statistically significant association was noted for genes related to synaptic function (SKAT-p = 0.014). In this WES study in F-SMI, we identify private, rare, protein altering variants in genes previously implicated in Mendelian neuropsychiatric syndromes; suggesting pleiotropic influences in neurodevelopment between complex and Mendelian syndromes.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.