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- de Vries, Paul S., et al.
(author)
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Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study
- 2017
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:1
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Journal article (peer-reviewed)abstract
- An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5x10(-8) is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5x10(-8)), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.
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- Pick, C. M., et al.
(author)
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Family still matters : Human social motivation across 42 countries during a global pandemic
- 2022
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In: Evolution and human behavior. - : Elsevier BV. - 1090-5138 .- 1879-0607. ; 43:6, s. 527-535
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Journal article (peer-reviewed)abstract
- The COVID-19 pandemic caused drastic social changes for many people, including separation from friends and coworkers, enforced close contact with family, and reductions in mobility. Here we assess the extent to which people's evolutionarily-relevant basic motivations and goals—fundamental social motives such as Affiliation and Kin Care—might have been affected. To address this question, we gathered data on fundamental social motives in 42 countries (N = 15,915) across two waves, including 19 countries (N = 10,907) for which data were gathered both before and during the pandemic (pre-pandemic wave: 32 countries, N = 8998; 3302 male, 5585 female; Mage = 24.43, SD = 7.91; mid-pandemic wave: 29 countries, N = 6917; 2249 male, 4218 female; Mage = 28.59, SD = 11.31). Samples include data collected online (e.g., Prolific, MTurk), at universities, and via community sampling. We found that Disease Avoidance motivation was substantially higher during the pandemic, and that most of the other fundamental social motives showed small, yet significant, differences across waves. Most sensibly, concern with caring for one's children was higher during the pandemic, and concerns with Mate Seeking and Status were lower. Earlier findings showing the prioritization of family motives over mating motives (and even over Disease Avoidance motives) were replicated during the pandemic. Finally, well-being remained positively associated with family-related motives and negatively associated with mating motives during the pandemic, as in the pre-pandemic samples. Our results provide further evidence for the robust primacy of family-related motivations even during this unique disruption of social life.
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- Bernal, D. E., et al.
(author)
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Alternative regularizations for Outer-Approximation algorithms for convex MINLP
- 2022
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In: Journal of Global Optimization. - : Springer Nature. - 0925-5001 .- 1573-2916. ; 84:4, s. 807-842
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Journal article (peer-reviewed)abstract
- In this work, we extend the regularization framework from Kronqvist et al. (Math Program 180(1):285–310, 2020) by incorporating several new regularization functions and develop a regularized single-tree search method for solving convex mixed-integer nonlinear programming (MINLP) problems. We propose a set of regularization functions based on distance metrics and Lagrangean approximations, used in the projection problem for finding new integer combinations to be used within the Outer-Approximation (OA) method. The new approach, called Regularized Outer-Approximation (ROA), has been implemented as part of the open-source Mixed-integer nonlinear decomposition toolbox for Pyomo—MindtPy. We compare the OA method with seven regularization function alternatives for ROA. Moreover, we extend the LP/NLP Branch and Bound method proposed by Quesada and Grossmann (Comput Chem Eng 16(10–11):937–947, 1992) to include regularization in an algorithm denoted RLP/NLP. We provide convergence guarantees for both ROA and RLP/NLP. Finally, we perform an extensive computational experiment considering all convex MINLP problems in the benchmark library MINLPLib. The computational results show clear advantages of using regularization combined with the OA method.
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