| 1. |
- Ho, Joshua W. K., et al.
(författare)
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Comparative analysis of metazoan chromatin organization
- 2014
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 512:7515, s. 449-U507
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Tidskriftsartikel (refereegranskat)abstract
- Genome function is dynamically regulated in part by chromatin, which consists of the histones, non-histone proteins and RNA molecules that package DNA. Studies in Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular mechanisms of genome function in humans, and have revealed conservation of chromatin components and mechanisms(1-3). Nevertheless, the three organisms have markedly different genome sizes, chromosome architecture and gene organization. On human and fly chromosomes, for example, pericentric heterochromatin flanks single centromeres, whereas worm chromosomes have dispersed heterochromatin-like regions enriched in the distal chromosomal 'arms', and centromeres distributed along their lengths(4,5). To systematically investigate chromatin organization and associated gene regulation across species, we generated and analysed a large collection of genome-wide chromatin data sets from cell lines and developmental stages in worm, fly and human. Here we present over 800 new data sets from our ENCODE and modENCODE consortia, bringing the total to over 1,400. Comparison of combinatorial patterns of histone modifications, nuclear lamina-associated domains, organization of large-scale topological domains, chromatin environment at promoters and enhancers, nucleosome positioning, and DNA replication patterns reveals many conserved features of chromatin organization among the three organisms. We also find notable differences in the composition and locations of repressive chromatin. These data sets and analyses provide a rich resource for comparative and species-specific investigations of chromatin composition, organization and function.
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| 2. |
- Barnes, J. C., et al.
(författare)
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The propensity for aggressive behavior and lifetime incarceration risk : A test for gene-environment interaction (G x E) using whole-genome data
- 2019
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Ingår i: Aggression and Violent Behavior. - : Elsevier BV. - 1359-1789 .- 1873-6335. ; 49
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Tidskriftsartikel (refereegranskat)abstract
- Incarceration is a disruptive event that is experienced by a considerable proportion of the United States population. Research has identified social factors that predict incarceration risk, but scholars have called for a focus on the ways that individual differences combine with social factors to affect incarceration risk. Our study is an initial attempt to heed this call using whole-genome data. We use data from the Health and Retirement Study (HRS) (N = 6716) to construct a genome-wide measure of genetic propensity for aggressive behavior and use it to predict lifetime incarceration risk. We find that participants with a higher genetic propensity for aggression are more likely to experience incarceration, but the effect is stronger for males than females. Importantly, we identify a gene-environment interaction (G x E)-genetic propensity is reduced, substantively and statistically, to a non-significant predictor for males raised in homes where at least one parent graduated high school. We close by placing these findings in the broader context of concerns that have been raised about genetics research in criminology.
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| 3. |
- Becker, Joel, et al.
(författare)
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Resource profile and user guide of the Polygenic Index Repository
- 2021
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Ingår i: Nature Human Behaviour. - : Nature Research (part of Springer Nature). - 2397-3374. ; 51:6, s. 694-695
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Tidskriftsartikel (refereegranskat)abstract
- Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
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