Search bioRxiv⌕ Search

Biology subjects

Loboda, A. A.

Publications and source records attributed to Loboda, A. A..

2 recordsLinked to original sources

The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia

To study how the 22q11.2 deletion predisposes to psychiatric disease, we generated induced pluripotent stem cells from deletion carriers and controls, as well as utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Upon differentiation into neural progenitor cells, we found the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including Autism Spectrum Disorder. In more differentiated excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common (per-SNP heritability p ({tau}c)= 4.2 x 10-6) and rare, loss of function variants (p = 1.29x10-12). These findings suggest a potential relationship between cellular states, developmental windows and susceptibility to psychiatric conditions with different ages of onset. To understand how the deletion contributed to these observed changes in gene expression, we developed and applied PPItools, which identifies the minimal protein-protein interaction network that best explains an observed set of gene expression alterations. We found that many of the genes in the 22q11.2 interval interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways that underlie the broader alterations in psychiatric risk gene expression we identified. Our findings suggest that the 22q11.2 deletion impacts genes and pathways that may converge with risk loci implicated by psychiatric genetic studies to influence disease manifestation in each deletion carrier.

neuroscience↗

A platform for case-control matching enables association studies without genotype sharing

Acquiring a sufficiently powered cohort of control samples can be time consuming or, sometimes, impossible. Accordingly, an ability to leverage control samples that were already collected and sequenced elsewhere could dramatically improve power in all genetic association studies. However, since majority of the genotyped and sequenced human DNA samples to date are subject to strict data sharing regulations, large-scale sharing of, in particular, control samples is extremely challenging. Using insights from image recognition, we developed a method allowing selection of the best-matching controls in an external pool of samples that is compliant with personal genotype data protection restrictions. Our approach uses singular value decomposition of the matrix of case genotypes to rank controls in another study by similarity to cases. We demonstrate that this recovers an accurate case-control association analysis for both ultra-rare and common variants and implement and provide online access to a library of ~17,000 controls that enables association studies for case cohorts lacking control subjects.

genetics↗