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Ori, A. P.

Publications and source records attributed to Ori, A. P..

3 recordsLinked to original sources

Stabilising selection enriches the tails of complex traits with rare alleles of large effect

Establishing the relative contribution of common and rare variants to complex trait heritability is a key goal of biomedical research. Recent statistical genetics inference suggests that common variants explain most complex trait heritability, but little is known about how genetic architecture varies across the trait continuum. If rare variants make a small contribution to heritability but have their effects concentrated in the tails of complex traits, where disease typically manifests, then they may have a greater clinical impact than previously inferred. Here, we perform simulations using the forward-in-time simulator SLiM to generate population genetic and complex trait data, in which traits evolve under neutrality or stabilising selection and are entirely heritable. Recent studies suggest that stabilising selection is the dominant force shaping the genetic architecture of complex traits; this is consistent with our simulations here, since data simulated under stabilising selection more closely resembles real data. Moreover, we observe a shift of rare, large-effect alleles towards the tails of the distributions of traits simulated under stabilising selection. In our simulations, individuals in the tails of complex traits are, depending on the strength of selection, 10-20x more likely to harbour singleton or extremely rare alleles of large effect under stabilising selection than neutrality. Such an enrichment of rare, large-effect alleles in the tails of real complex traits subject to stabilising selection could have important implications for the design of studies to detect rare variants, for our understanding of the consequences of natural selection on complex traits, and for the prediction and prevention of complex disease.

genetics↗

Integrative genomic strategies applied to a lymphoblast cell line model reveal specific transcriptomic signatures associated with clozapine response

Clozapine is an important antipsychotic drug. However, its use is often accompanied by metabolic adverse effects and, in rare instances, agranulocytosis. The molecular mechanisms underlying these adverse events are unclear. To gain more insights into the response to clozapine at the molecular level, we exposed lymphoblastoid cell lines (LCLs) to increasing concentrations of clozapine and measured genome-wide gene expression and DNA methylation profiles. We observed robust and significant changes in gene expression levels due to clozapine (n = 463 genes at FDR < 0.05) affecting cholesterol and cell cycle pathways. At the level of DNA methylation, we find significant changes upstream of the LDL receptor, in addition to global enrichments of regulatory, immune and developmental pathways. By integrating these data with human tissue gene expression levels obtained from the Genotype-Tissue Expression project (GTEx), we identified specific tissues, including liver and several tissues involved in immune, endocrine and metabolic functions, that clozapine treatment may disproportionately affect. Notably, differentially expressed genes were not enriched for genome-wide disease risk of schizophrenia or for known psychotropic drug targets. However, we did observe a nominally significant association of genetic signals related to total cholesterol and low-density lipoprotein levels. Together, these results shed light on the biological mechanisms through which clozapine functions. The observed associations with cholesterol pathways, its genetic architecture and specific tissue effects may be indicative of the metabolic adverse effects observed in clozapine users. LCLs may thus serve as a useful tool to study these molecular mechanisms further.

genomics↗

Schizophrenia is characterized by age- and sex-specific effects on epigenetic aging

BackgroundThe study of biological age acceleration may help identify at-risk individuals and contribute to reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a severe mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a multi-cohort whole blood sample consisting of 1,090 SCZ cases and 1,206 controls, we investigated differential aging using three DNAm clocks (i.e. Hannum, Horvath, Levine). These clocks are highly predictive of chronological age and are known to capture different processes of biological aging. ResultsWe found that blood-based DNAm aging is significantly altered in SCZ with age- and sex-specific effects that differ between clocks and map to distinct chronological age windows. Most notably, differential phenotypic age (Levine clock) was most pronounced in female SCZ patients in later adulthood compared to matched controls. Female patients with high SCZ polygenic risk scores (PRS) present the highest age acceleration in this age group with +4.30 years (CI: 2.40-6.20, P=1.3E-05). Phenotypic age and SCZ PRS contribute additively to the illness and together explain up to 22.4% of the variance in disease status in this study. This suggests that combining genetic and epigenetic predictors may improve predictions of disease outcomes. ConclusionsSince increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. These results provide new biological insights into the aging landscape of SCZ with age- and sex-specific effects and warrant further investigations into the potential of DNAm clocks as clinical biomarkers that may help with disease management in schizophrenia.

genomics↗