Common and rare variant genetic contributions in African Americans with autism.
Cirnigliaro, M.; Lowe, J. K.; Flynn-Carroll, A. O.; Kumagai, M. E.; Gibson, D. S.; Fu, J. M.; Dong, S.; Hou, K.; Pillalamarri, V.; Abbacchi, A. M.; Gulsrud, A. C.; Miller, J.; Zhang, Y.; Graham, E. T.; Akinyemi, E. O.; Adams, M. F.; Clay, A. N.; Arteaga, S. A.; Choi, H.; Kochis, R. M.; Pena-Velasco, J. E.; Hoekstra, J. N.; Besterman, A. D.; Mehta, S.; Hadzic, T.; Wilson, R. B.; Brown, T. R.; Hernandez, L. M.; Marrus, N.; Molholm, S.; Klaiman, C.; Cantor, R. M.; Talkowski, M. E.; Sanders, S. J.; Arking, D. E.; Pasaniuc, B.; Klin, A.; Constantino, J. N.; Genetics of Neurodevelopment in African A
Show abstract
The absence of non-European cohorts in genetic studies of neurodevelopmental and neuropsychiatric disorders severely limits the understanding of their full genetic architecture and undermines implementation of precision medicine. Here, we directly addressed this issue by recruiting African Americans (AfrAms) with autism spectrum disorder (ASD) and analyzing their rare and common genetic variation. We performed both global and local ancestry analyses to characterize the complex patterns of admixture at the individual level and compare genetic factors between European (EUR) and African (AFR) genetically inferred ancestries (GIAs) across multiple cohorts in a total of 38,483 autistic individuals. We showed consistent common variant genetic effect sizes for ASD in EUR and AFR GIAs through genome-wide association studies. We demonstrated the limited transferability of EUR-derived polygenic scores (PGSs) based on polygenic transmission disequilibrium and ancestry partial PGS analysis. We found significant autism association for high-impact rare copy number variants in both GIAs. We identified a set of candidate ASD loci based on rare deletions observed in AFR GIA carriers, including SMC2, DMTN, SORCS1, and ROGDI, and detected a signal for de novo missense variants of predicted low impact in AFR GIA individuals. Finally, we uncovered significant depletion of AFR GIA autistic carriers of rare variants in known associated genes found in EUR cohort studies. These findings are the first to detail common and rare variant genetic contributions to ASD in AfrAms and demonstrate that their involvement in neurodevelopmental and neuropsychiatric disorders genomic research is essential to advance discovery.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Rare coding variation illuminates the allelic architecture, risk genes, cellular expression patterns, and phenotypic context of autism 98%
- Decomposition of phenotypic heterogeneity in autism reveals distinct and coherent genetic programs 96%
- A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex 96%
Similar papers in this journal
- Systematic analysis and prediction of genes associated with disorders on chromosome X 97%
- Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism 97%
- Transcriptome and chromatin accessibility landscapes across 25 distinct human brain regions expand the susceptibility gene set for neuropsychiatric disorders 96%
Similar papers in this journal
- Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes 96%
- Meta-analysis fine-mapping is often miscalibrated at single-variant resolution 94%
- Variant-resolved prediction of context-specific isoform variation with a graph-based attention model 94%
Similar papers in this journal
- Identification of 64 new risk loci for major depression, refinement of the genetic architecture and risk prediction of recurrence and comorbidities 94%
- Quantification of autism recurrence risk by direct assessment of paternal sperm mosaicism 94%
- Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals 93%
"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.