New ionosphere and electrodynamics modules have been incorporated in the thermosphere and ionosphere eXtension of the Whole Atmosphere Community Climate Model (WACCM‐X), in order to self‐consistently ...simulate the coupled atmosphere‐ionosphere system. The first specified dynamics WACCM‐X v.2.0 results are compared with several data sets, and with the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM), during the deep solar minimum year. Comparisons with Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite of temperature and zonal wind in the lower thermosphere show that WACCM‐X reproduces the seasonal variability of tides remarkably well, including the migrating diurnal and semidiurnal components and the nonmigrating diurnal eastward propagating zonal wavenumber 3 component. There is overall agreement between WACCM‐X, TIE‐GCM, and vertical drifts observed by the Communication/Navigation Outage Forecast System (C/NOFS) satellite over the magnetic equator, but apparent discrepancies also exist. Both model results are dominated by diurnal variations, while C/NOFS observed vertical plasma drifts exhibit strong temporal variations. The climatological features of ionospheric peak densities and heights (NmF2 and hmF2) from WACCM‐X are in general agreement with the results derived from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) data, although the WACCM‐X predicted NmF2 values are smaller, and the equatorial ionization anomaly crests are closer to the magnetic equator compared to COSMIC and ionosonde observations. This may result from the excessive mixing in the lower thermosphere due to the gravity wave parameterization. These data‐model comparisons demonstrate that WACCM‐X can capture the dynamic behavior of the coupled atmosphere and ionosphere in a climatological sense.
Plain Language Summary
The Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM‐X) is a numerical model of entire atmosphere, from the surface to around 600 km in altitude. Recent enhancements to WACCM‐X include a fully coupled ionosphere, including electric field effects and ion transport. WACCM‐X results are compared with several datasets, and with a predecessor, the TIE‐GCM during a very low solar activity year 2008. Comparisons with wind and temperature measurements by the TIMED satellite show that WACCM‐X reproduces the seasonal variability of atmospheric tides remarkably well. There is overall agreement between WACCM‐X, TIE‐GCM, and vertical ion motions observed by the C/NOFS satellite over the magnetic equator, but apparent discrepancies also exist among them. The climatological features of ionospheric peak densities and heights from WACCM‐X are in general agreement with results derived from COSMIC data, although the WACCM‐X‐predicted peak values are smaller, and the equatorial ionosphere has bands of enhancement that are closer to the magnetic equator compared to COSMIC and ionosonde observations. These data‐model comparisons demonstrate that WACCM‐X can capture the basic climate and variation of the coupled atmosphere and ionosphere.
Key Points
First evaluation of WACCM‐X during deep solar minimum year was carried out
Data‐model comparisons illustrate the high fidelity of WACCM‐X
Abstract
Background
Alzheimer disease (AD) is the fourth leading cause of death in Puerto Rico. The Puerto Rican (PR) population has a high proportion of older adults (18%, over > 65), with 12.5% of ...them suffering from ADThese statistics highlight the need to investigate the genetic risk factors underlying AD in the PR population, as it could lead to the development of targeted treatments and therapies. Moreover, the ancestrally admixed makeup of the PR population provides an opportunity to assess the role of the European (∼67%), African (∼20%) and Amerindian (∼13%) ancestry in AD risk. We performed genome wide association analysis (GWAS) using whole genome sequence data to identify genetic risk/protective factors associated with AD in the PR population.
Method
The PR dataset includes WGS and phenotype data from 640 individuals, comprising 335 AD and 305 cognitively unimpaired (CU) controls. To account for the population substructure, we calculated the global ancestry (principal components) using EIGENSTRAT. We performed GWAS analyses with a generalized linear mixed‐model using the SAIGE software. The model included genotype, sex, age, and principal components (population substructure) as fixed effects and genetic relationship matrix as a random effect. The genetic relationship matrix was calculated based on genomic data and accounted for the relatedness among the individuals in the dataset.
Result
We identified four suggestive significant loci (P<1×10
−6
) associated with the risk of AD in PRs: NACC2 (pv = 4.8×10
−7
) on chromosome 9, SCN8A (pv = 9.3×10
−7
) on chromosome 12, FOXK2 (pv = 9.9×10
−7
) on chromosome 17, and APOEe4 (pv = 6.8×10
−8
) on chromosome 19. Eight additional AD loci with the same lead marker from European GWAS study (Bellenquez et al.) showed nominal significance: FERMT2 (pv = 6.2×10
−3
), TREM2 (pv = 8.0×10
−3
), CLU (pv = 1.8×10
−2
), RASGEF1C (pv = 2.4×10
−2
), ADAM17 (pv = 3.5×10
−2
), DOC2A (pv = 4.3×10
−2
), GRN (pv = 4.4×10
−2
) and SORL1 (pv = 5.1×10
−2
).
Conclusion
This study identified three suggestive novel significant loci (NACC2, SCN8A, and FOXK2) associated with AD risk in PRs. In addition, GWAS study on PRs with a high proportion of European ancestry was able to replicate nine AD loci previously identified in European studies. These findings provide new insights into the genetic architecture of AD in the PR population.
Abstract
Background
Increasing ethnic/ancestral diversity in genetic studies is critical for defining the genetic architecture of Alzheimer disease (AD). Amerindian (AI) populations are substantially ...underrepresented in AD genetic studies. The Peruvian (PE) population, with up to ∼80% of AI ancestry, provides a unique opportunity to assess the role of AI ancestry in AD. We performed the first genome‐wide association study (GWAS) in the PE population to identify novel AD susceptibility loci and characterize known AD genetic risk loci.
Method
The PE dataset includes array‐genotype and phenotype data from 542 individuals (189 cases; 353 controls), imputed to the NHLBI TOPMedv5 haplotype reference panel. We used a generalized linear mixed‐model (SAIGE software) for the GWAS analysis. We analyzed two separate models; the first model accounted for sex, age, and population substructure, while the second model also included the dosage of APOEe4. In both models, we included a genetic relationship matrix as a random effect to account for any potential relatedness. To determine if the associations are specific to specific ancestries, we employed ancestry‐aware approaches using the RFMix software.
Result
APOE was significantly associated with AD with an effect size comparable to that found in non‐Hispanic white (NHW) populations (OR = 3.3(2.2‐4.8),pv = 8.0×10
−10
). Two additional known AD loci,
TREML2
(pv = 0.008) and
CLU
(pv = 0.012), showed nominal significance Variants at three additional loci reached suggestive significance (pv<1×10
−6
):
NFASC
(pv = 9.4×10
−8
;chromosome 1),
STK32A
(pv = 9.3×10
−7
; chromosome 5), and
LOC100132830
(pv = 6.7×10
−7
;chromosome 6). The
NFASC
locus neared genome‐wide significance in the
APOE
adjusted model (pv = 6.7×10
−8
). The haplotypes associated with AD at the
NFASC
locus were found to be of European origin. Additionally, the
STK32A
locus was found to have a protective effect specifically among individuals of AI background. We did not observe significant heterogeneity of effect at the
APOE
and
LOC100132830
loci across different ancestral backgrounds.
Conclusion
PE GWAS identified a novel, promising AD susceptibility locus in the
NFASC
gene of European origin. We also detected a potential protective effect in the
STK32A
locus on AI background, emphasizing the importance of incorporating ancestry‐aware approaches in gene discovery in admixed populations.
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