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Di

Publications and source records attributed to Di.

2 recordsLinked to original sources

Contribution of rare copy number variants to bipolar disorder risk is limited to schizoaffective cases

BackgroundGenetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. BD subtypes schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I) and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania and depression. The factors contributing to the combination of symptoms within a given patient are poorly understood.\n\nMethodsRare, large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis], 1436 BD II, 579 SAB) and 8656 controls. Measures of CNV burden were integrated with polygenic risk scores (PRS) for schizophrenia (SCZ) to evaluate the relative contributions of rare and common variants to psychosis risk.\n\nResultsCNV burden did not differ in BD relative to controls when treated as a single diagnostic entity. Burden in SAB was increased compared to controls (p-value = 0.001), BD I (p-value = 0.0003) and BD II (p-value = 0.0007). Burden and SCZ PRS were higher in SAB compared to BD I with psychosis (CNV p-value = 0.0007, PRS p-value = 0.004) and BD I without psychosis (CNV p-value = 0.0004, PRS p-value = 3.9 x 10-5). Within BD I, psychosis was associated with higher SCZ PRS (p-value = 0.005) but not with CNV burden.\n\nConclusionsCNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms.

genetics

Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared

Joint analysis of data from multiple studies in collaborative efforts strengthens scientific evidence, with the gold standard approach being the pooling of individual participant data (IPD). However, sharing IPD often has legal, ethical, and logistic constraints for sensitive or high-dimensional data, such as in clinical trials, observational studies, and large-scale omics studies. Therefore, meta-analysis of study-level effect estimates is routinely done, but this compromises on statistical power, accuracy, and flexibility. Here we propose a novel meta-analytical approach, named partial derivatives meta-analysis, that is mathematically equivalent to using IPD, yet only requires the sharing of aggregate data. It not only yields identical results as pooled IPD analyses, but also allows post-hoc adjustments for covariates and stratification without the need for site-specific re-analysis. Thus, in case that IPD cannot be shared, partial derivatives meta-analysis still produces gold standard results, which can be used to better inform guidelines and policies on clinical practice.

Bioinformatics