| 1. |
- Kelly, J. J., et al.
(författare)
-
Recoil polarization measurements for neutral pion electroproduction at Q(2)=1(GeV/c)(2) near the Delta resonance
- 2007
-
Ingår i: Physical Review C (Nuclear Physics). - 0556-2813. ; 75:2
-
Tidskriftsartikel (refereegranskat)abstract
- We measured angular distributions of differential cross section, beam analyzing power, and recoil polarization for neutral pion electroproduction at Q(2)=1.0 (GeV/c)(2) in 10 bins of 1.17 <= W <= 1.35 GeV across the Delta resonance. A total of 16 independent response functions were extracted, of which 12 were observed for the first time. Comparisons with recent model calculations show that response functions governed by real parts of interference products are determined relatively well near the physical mass, W=M-Delta approximate to 1.232 GeV, but the variation among models is large for response functions governed by imaginary parts, and for both types of response functions, the variation increases rapidly with W > M-Delta. We performed a multipole analysis that adjusts suitable subsets of center dot(pi)<= 2 amplitudes with higher partial waves constrained by baseline models. This analysis provides both real and imaginary parts. The fitted multipole amplitudes are nearly model independent-there is very little sensitivity to the choice of baseline model or truncation scheme. By contrast, truncation errors in the traditional Legendre analysis of N ->Delta quadrupole ratios are not negligible. Parabolic fits to the W dependence around M-Delta for the multiple analysis gives values for Re(S1+/M1+)=(-6.61 +/- 0.18)% and Re(E1+/M1+)=(-2.87 +/- 0.19)% for the p pi(0) channel at W=1.232 GeV and Q(2)=1.0 (GeV/c)(2) that are distinctly larger than those from the Legendre analysis of the same data. Similarly, the multipole analysis gives Re(S0+/M1+)=(+7.1 +/- 0.8)% at W=1.232 GeV, consistent with recent models, while the traditional Legendre analysis gives the opposite sign because its truncation errors are quite severe.
|
|
| 2. |
- Liu, DJ, et al.
(författare)
-
Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations
- 2023
-
Ingår i: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 55:3, s. 369-
-
Tidskriftsartikel (refereegranskat)abstract
- Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study—and most other large-scale human genetics studies—was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10−6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.
|
|
| 3. |
- Bigdeli, TB, et al.
(författare)
-
Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry
- 2020
-
Ingår i: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:10, s. 2455-2467
-
Tidskriftsartikel (refereegranskat)abstract
- Schizophrenia is a common, chronic and debilitating neuropsychiatric syndrome affecting tens of millions of individuals worldwide. While rare genetic variants play a role in the etiology of schizophrenia, most of the currently explained liability is within common variation, suggesting that variation predating the human diaspora out of Africa harbors a large fraction of the common variant attributable heritability. However, common variant association studies in schizophrenia have concentrated mainly on cohorts of European descent. We describe genome-wide association studies of 6152 cases and 3918 controls of admixed African ancestry, and of 1234 cases and 3090 controls of Latino ancestry, representing the largest such study in these populations to date. Combining results from the samples with African ancestry with summary statistics from the Psychiatric Genomics Consortium (PGC) study of schizophrenia yielded seven newly genome-wide significant loci, and we identified an additional eight loci by incorporating the results from samples with Latino ancestry. Leveraging population differences in patterns of linkage disequilibrium, we achieve improved fine-mapping resolution at 22 previously reported and 4 newly significant loci. Polygenic risk score profiling revealed improved prediction based on trans-ancestry meta-analysis results for admixed African (Nagelkerke’s R2 = 0.032; liability R2 = 0.017; P < 10−52), Latino (Nagelkerke’s R2 = 0.089; liability R2 = 0.021; P < 10−58), and European individuals (Nagelkerke’s R2 = 0.089; liability R2 = 0.037; P < 10−113), further highlighting the advantages of incorporating data from diverse human populations.
|
|
| 4. |
|
|
| 5. |
- Barmpas, Petros, et al.
(författare)
-
A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies : the ATHLOS project
- 2022
-
Ingår i: Health Information Science and Systems. - : Springer Science and Business Media LLC. - 2047-2501. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP).
|
|
| 6. |
|
|
