medRxivGenetic & Genomic MedicinePreprint
Substantial role of rare inherited variation in individuals with developmental disorders
Kaitlin E. SamochaiD, V. Kartik ChundruiD, Jack M. FuiD, Eugene J. GardneriD, Petr DanecekiD, Emilie M. WigdoriD, Daniel Shiloh MalawskyiD, Sarah LindsayiD, Patrick H. Campbell, Tarjinder SinghiD, Ruth Y. EberhardtiD, Giuseppe GalloneiD, Caroline Fiona WrightiD, Hilary C. MartiniD, Helen V. FirthiD, Matthew E. HurlesiD
Abstract
While the role of de novo and recessively-inherited coding variation in risk for rare developmental disorders (DDs) has been well established, the contribution of damaging variation dominantly-inherited from parents is less explored. Here, we investigated the contribution of rare coding variants to DDs by analyzing 13,452 individuals with DDs, 18,613 of their family members, and 3,943 controls using a combination of family-based and case/control analyses. In line with previous studies of other neuropsychiatric traits, we found a significant burden of rare (allele frequency < 1x10-5) predicted loss-of-function (pLoF) and damaging missense variants, the vast majority of which are inherited from apparently unaffected parents. These predominantly inherited burdens are strongest in DD-associated genes or those intolerant of pLoF variation in the general population, however we estimate that [~]10% of the excess of these variants in DD cases is found within the DD-associated genes, implying many more risk loci are yet to be identified. We found similar, but attenuated, burdens when comparing the unaffected parents of individuals with DDs to controls, indicating that parents have elevated risk of DDs due to these rare variants, which are overtransmitted to their affected children. We estimate that 6-8.5% of the population attributable risk for DDs are due to rare pLoF variants in those genes intolerant of pLoF variation in the general population. Finally, we apply a Bayesian framework to combine evidence from these analyses of rare, mostly-inherited variants with prior de novo mutation burden analyses to highlight an additional 25 candidate DD- associated genes for further follow up.
- organism: human
- assay: whole-genome sequencing
- research focus: variant effect prediction
- research focus: GWAS / association mapping
- approach: statistical model
- approach: Bayesian inference
- Citations
- 4
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