| 1. |
- Blokland, G. A. M., et al.
(författare)
-
Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders
- 2022
-
Ingår i: Biological Psychiatry. - : Elsevier BV. - 0006-3223 .- 1873-2402. ; 91:1, s. 102-117
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. Methods: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. Results: Across disorders, genome-wide significant single nucleotide polymorphism–by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10−8), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10−6) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10−7; rs73033497, p = 8.8 × 10−7; rs7914279, p = 6.4 × 10−7), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10−7) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10−7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10−7) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). Conclusions: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels. © 2021 Society of Biological Psychiatry
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Cearns, M., et al.
(författare)
-
Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- 2022
-
Ingår i: British Journal of Psychiatry. - : Royal College of Psychiatrists. - 0007-1250 .- 1472-1465. ; 220:4, s. 219-228
-
Tidskriftsartikel (refereegranskat)abstract
- Background Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. Aims To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. Method This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi(+)Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. Results The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. Conclusions Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
|
|
| 5. |
- Watson, Hunna J., et al.
(författare)
-
Common Genetic Variation and Age of Onset of Anorexia Nervosa
- 2022
-
Ingår i: BIOLOGICAL PSYCHIATRY: GLOBAL OPEN SCIENCE. - : Elsevier BV. - 2667-1743. ; 2:4, s. 368-378
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Genetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche.METHODS: A secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed, which included 9335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age of onset, early-onset AN (,13 years), and typical-onset AN, and genetic correlation, genetic risk score, and Mendelian randomization analyses.RESULTS: Two loci were genome-wide significant in the typical-onset AN GWAS. Heritability estimates (single nucleotide polymorphism-h2) were 0.01-0.04 for age of onset, 0.16-0.25 for early-onset AN, and 0.17-0.25 for typical-onset AN. Early-and typical-onset AN showed distinct genetic correlation patterns with putative risk factors for AN. Specifically, early-onset AN was significantly genetically correlated with younger age at menarche, and typical-onset AN was significantly negatively genetically correlated with anthropometric traits. Genetic risk scores for age of onset and early-onset AN estimated from independent GWASs significantly predicted age of onset. Mendelian randomization analysis suggested a causal link between younger age at menarche and early -onset AN.CONCLUSIONS: Our results provide evidence consistent with a common variant genetic basis for age of onset and implicate biological pathways regulating menarche and reproduction.
|
|
| 6. |
|
|
| 7. |
|
|
