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- Dareng, EO, et al.
(author)
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Polygenic risk modeling for prediction of epithelial ovarian cancer risk
- 2022
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In: European journal of human genetics : EJHG. - : Springer Science and Business Media LLC. - 1476-5438 .- 1018-4813. ; 30:3, s. 349-362
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Journal article (peer-reviewed)abstract
- Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.
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- Forslund, Sofia K., et al.
(author)
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Combinatorial, additive and dose-dependent drug–microbiome associations
- 2021
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 600:7889, s. 500-505
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Journal article (peer-reviewed)abstract
- During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1–5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug–host–microbiome interactions in cardiometabolic disease.
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- Alström, Per, et al.
(author)
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Multiple species within the Striated Prinia Prinia crinigera-Brown Prinia P. polychroa complex revealed through an integrative taxonomic approach
- 2020
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In: Ibis. - : Wiley. - 0019-1019 .- 1474-919X. ; 162:3, s. 936-967
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Journal article (peer-reviewed)abstract
- We re-evaluated the taxonomy of the Striated Prinia Prinia crinigera-Brown Prinia P. polychroa complex using molecular, morphological and vocal analyses. The extensive seasonal, sexual, age-related, geographical and taxon-specific variation in this complex has never before been adequately studied. As no previous genetic or vocal analyses have focused on this group, misinterpretation of taxonomic signals from limited conventional morphological study alone was likely. Using mitochondrial and nuclear DNA, we found that P. crinigera sensu lato (s.l.) comprises two non-sister groups of taxa (Himalayan crinigera and Chinese striata groups) that differ substantially morphologically and vocally and that are broadly sympatric in Yunnan Province, China. Prinia polychroa cooki (Myanmar) and P. p. rocki (southern Vietnam) are each morphologically, vocally and genetically distinct. Thai, Cambodian and Laotian populations formerly ascribed to P. p. cooki are morphologically and vocally most similar to and most closely related to Javan P. p. polychroa, and require a new name, proposed here. Prinia p. bangsi of Yunnan is part of the crinigera group rather than of P. polychroa, and hence there is no evidence for sympatry between P. polychroa s.l. and P. crinigera s.l., nor of the occurrence of P. polychroa in mainland China or Taiwan. We recommend the recognition of five species in the complex, with the following suggestions for new English names: Himalayan Prinia P. crinigera sensu stricto (s.s.; with subspecies striatula, crinigera, yunnanensis and bangsi); Chinese Prinia P. striata (subspecies catharia, parumstriata and striata); Burmese Prinia P. cooki (monotypic); Annam Prinia P. rocki (monotypic) and Deignan's Prinia P. polychroa s.s. (subspecies Javan polychroa and the new Southeast Asian taxon). This study underlines the importance of using multiple datasets for the elucidation of diversity of cryptic bird species and their evolutionary history and biogeography.
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- Cardoso, Gabriel, et al.
(author)
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State and parameter learning with PARIS particle Gibbs
- 2023
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In: Proceedings of the 40th International Conference on Machine Learning, ICML 2023. - : ML Research Press. ; , s. 3625-3675
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Conference paper (peer-reviewed)abstract
- Non-linear state-space models, also known as general hidden Markov models (HMM), are ubiquitous in statistical machine learning, being the most classical generative models for serial data and sequences. Learning in HMM, either via Maximum Likelihood Estimation (MLE) or Markov Score Climbing (MSC) requires the estimation of the smoothing expectation of some additive functionals. Controlling the bias and the variance of this estimation is crucial to establish the convergence of learning algorithms. Our first contribution is to design a novel additive smoothing algorithm, the Parisian particle Gibbs (PPG) sampler, which can be viewed as a PARIS (Olsson & Westerborn, 2017) algorithm driven by conditional SMC moves, resulting in bias-reduced estimates of the targeted quantities. We substantiate the PPG algorithm with theoretical results, including new bounds on bias and variance as well as deviation inequalities. We then establish, in the learning context, and under standard assumptions, non-asymptotic bounds highlighting the value of bias reduction and the implicit Rao-Blackwellization of PPG. These are the first non-asymptotic results of this kind in this setting. We illustrate our theoretical results with numerical experiments supporting our claims.
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- Coignard, J, et al.
(author)
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A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers
- 2021
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1, s. 1078-
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Journal article (peer-reviewed)abstract
- Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10−8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers.
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