Top Signals Research Articles

Genetic association study of preterm birth and gestational age in a population-based case-control study in Peru.

Preterm birth (PTB) affects ∼15 million pregnancies worldwide. Genetic studies have identified several candidate loci for PTB, but results remain inconclusive and limited to European populations. Thus, we conducted a genome-wide association study (GWAS) of PTB and gestational age at delivery (GA) among 2,212 Peruvian women. PTB cases delivered≥20 weeks' but < 37 weeks' gestation, while controls delivered at term (≥37 weeks but <42 weeks). Multivariable regressions were used to identify genetic markers for PTB and GA (∼6 million SNPs), adjusting for maternal age and the first two genetic principal components. In silico functional analysis was conducted among top signals detected with an arbitrary P < 1.0×10-5. We sought to replicate genetic markers for PTB and GA identified in Europeans, and we developed a genetic risk score for GA based on European markers. Mean GA was 30 ± 4 weeks in PTB cases (N = 933) and 39 ± 1 in the controls (N = 1,279). No associatiosn were identified at genome-wide level. Nominal PTB variants were enriched for biological pathways associated with polyketide, progesterone, steroid hormones, and glycosyl metabolism. Nominal GA variants were enriched in intronic regions and cancer pathways. Variants in WNT4 associated with GA in Europeans were replicated in our study. A genetic risk score was associated with a 2-day longer GA (P = 0.002) in our sample. This study identified various signals suggestively associated with PTB and GA in pregnant Peruvian women. None of these variants overlapped with signals previously identified in Europeans.

Pleiotropic effects of an eQTL in the CELSR2/PSRC1/SORT1 cluster that associates with LDL-C and resting metabolic rate.

The locus CELSR2-PSRC1-SORT1, a primary genetic signal for lipids, has recently been implicated in different metabolic processes. Our investigation identified its association with energy metabolism. To determine biological mechanisms that govern diverse functions of this locus. Genotypes for 491,265 variants in 7,000 clinically characterized American Indians were previously determined using a custom-designed array specific for this longitudinally studied American Indian population. Among the genotyped individuals, 5,205 had measures of fasting lipid levels and 509 had measures of resting metabolic rate (RMR) and substrate oxidation rate assessed through indirect calorimetry. A genome-wide association study (GWAS) for LDL-C levels identified a variant in CELSR2 and the molecular impact of this variant on gene expression was assessed in skeletal muscle biopsies from 207 participants, followed by functional validation in mouse myoblasts using a luciferase assay. A GWAS in American Indians identified rs12740374 in CELSR2 as the top signal for LDL-C levels (P = 1 × 10-22); further analysis of this variant identified an unexpected correlation with reduced RMR (effect = -44.3 kcal/day/minor-allele) and carbohydrate oxidation rate (effect = -5.21 mg/hour/kg-EMBS). Tagged variants showed a distinct linkage disequilibrium architecture in American Indians, highlighting a potential functional variant, rs6670347 (minor-allele frequency = 0.20). Positioned in the glucocorticoid receptor's core binding motif, rs6670347 is part of a skeletal muscle-specific enhancer. Human skeletal muscle transcriptome analysis showed CELSR2 as the most differentially expressed gene (P = 1.9 × 10-7), with the RMR-lowering minor allele elevating gene expression. Experiments in mouse myoblasts confirmed enhancer-based regulation of CELSR2 expression, dependent on glucocorticoids. Rs6670347 also associated with increased oxidative phosphorylation gene expression; CELSR2 as a regulator of these genes, suggests potential influence on energy metabolism through muscle oxidative capacity. Variants in the CELSR2/PSRC1/SORT1 locus exhibit tissue-specific effects on metabolic traits, with an independent role in muscle metabolism through glucocorticoid signaling.

The politics of higher education in China: the signal–response mechanism, downward tiered pressure escalation, and the Double First-Class University Initiative

ABSTRACT This article examines the politics of higher education in China drawing on empirical data collected from three elite universities listed in the ‘Double First-Class University Initiative’. We investigate how a ‘signal – response’ mechanism, in which the country’s top leaders signal broad policy goals and subordinate officials respond, works in China’s higher education arena. To address the uncertainties caused by vague signals and avoid blame for failure to fulfil policy goals, a strategy involving a gradual downward increase of implementation pressure within the bureaucratic hierarchy, termed cengceng jiama (层层加码), has been adopted. Universities and their faculties/departments now establish their own ‘up-or-leave’ systems to remove underperforming researchers, apply more stringent criteria when assessing faculty members’ publication rates, and discriminate against domestically trained PhDs when hiring. Consequently, faculty members, especially those in the younger generation, often experience high levels of anxiety, insecurity and inequality, which can hinder their development as scholars.

Illuminating Genetic Diversity and Selection Signatures in Matou Goats through Whole-Genome Sequencing Analysis.

(1) Background: Matou goats, native to Hunan and Hubei provinces in China, are renowned for their exceptional meat and skin quality. However, a comprehensive whole-genome-based exploration of the genetic architecture of this breed is scant in the literature. (2) Methods: To address this substantial gap, we used whole-genome sequences of 20 Matou goats and compared them with published genomic data of 133 goats of different breeds across China. This comprehensive investigation sought to assess genetic diversity, population structure, and the presence of genomic selection signals. (3) Results: The whole genome of Matou goat populations yielded a substantial catalog of over 19 million single nucleotide polymorphisms (SNPs), primarily distributed within intergenic and intron regions. The phylogenetic tree analysis revealed distinct clades corresponding to each goat population within the dataset. Notably, this analysis positioned Matou goats in a closer genetic affinity with Guizhou White goats, compared to other recognized goat breeds. This observation was corroborated by principal component analysis (PCA) and admixture analysis. Remarkably, Matou goats exhibited diminished genetic diversity and a notable degree of inbreeding, signifying a reduced effective population size. Moreover, the study employed five selective sweep detection methods (including PI, CLR, PI-Ratio, Fst, and XP-EHH) to screen top signal genes associated with critical biological functions, encompassing cardiomyocytes, immunity, coat color, and meat quality. (4) Conclusions: In conclusion, this study significantly advances our understanding of the current genetic landscape and evolutionary dynamics of Matou goats. These findings underscore the importance of concerted efforts in resource conservation and genetic enhancement for this invaluable breed.

Genome-wide Studies Reveal Genetic Risk Factors for Hepatic Fat Content.

Genetic susceptibility to metabolic associated fatty liver disease (MAFLD) is complex and poorly characterized. Accurate characterization of the genetic background of hepatic fat content would provide insights into disease etiology and causality of risk factors. We performed genome-wide association study (GWAS) on two noninvasive definitions of hepatic fat content: magnetic resonance imaging proton density fat fraction (MRI-PDFF) in 16,050 participants and fatty liver index (FLI) in 388,701 participants from the United Kingdom (UK) Biobank (UKBB). Heritability, genetic overlap, and similarity between hepatic fat content phenotypes were analyzed, and replicated in 10,398 participants from the University Medical Center Groningen (UMCG) Genetics Lifelines Initiative (UGLI). Meta-analysis of GWASs of MRI-PDFF in UKBB revealed five statistically significant loci, including two novel genomic loci harboring CREB3L1 (rs72910057-T, P = 5.40E-09) and GCM1 (rs1491489378-T, P = 3.16E-09), respectively, as well as three previously reported loci: PNPLA3, TM6SF2, and APOE. GWAS of FLI in UKBB identified 196 genome-wide significant loci, of which 49 were replicated in UGLI, with top signals in ZPR1 (P = 3.35E-13) and FTO (P = 2.11E-09). Statistically significant genetic correlation (rg) between MRI-PDFF (UKBB) and FLI (UGLI) GWAS results was found (rg = 0.5276, P = 1.45E-03). Novel MRI-PDFF genetic signals (CREB3L1 and GCM1) were replicated in the FLI GWAS. We identified two novel genes for MRI-PDFF and 49 replicable loci for FLI. Despite a difference in hepatic fat content assessment between MRI-PDFF and FLI, a substantial similar genetic architecture was found. FLI is identified as an easy and reliable approach to study hepatic fat content at the population level.

Plasma metabolites in childhood Burkitt lymphoma cases and cancer-free controls in Uganda

IntroductionBurkitt lymphoma (BL) is an aggressive non-Hodgkin lymphoma associated with Plasmodium falciparum and Epstein-Barr virus, both of which affect metabolic pathways. The metabolomic patterns of BL is unknown.Materials and methodsWe measured 627 metabolites in pre-chemotherapy treatment plasma samples from 25 male children (6–11 years) with BL and 25 cancer-free area- and age-frequency-matched male controls from the Epidemiology of Burkitt Lymphoma in East African Children and Minors study in Uganda using liquid chromatography-tandem mass spectrometry. Unconditional, age-adjusted logistic regression analysis was used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) for the BL association with 1-standard deviation increase in the log-metabolite concentration, adjusting for multiple comparisons using false discovery rate (FDR) thresholds and Bonferroni correction.ResultsCompared to controls, levels for 42 metabolite concentrations differed in BL cases (FDR < 0.001), including triacylglyceride (18:0_38:6), alpha-aminobutyric acid (AABA), ceramide (d18:1/20:0), phosphatidylcholine ae C40:6 and phosphatidylcholine C38:6 as the top signals associated with BL (ORs = 6.9 to 14.7, P < 2.4✕10− 4). Two metabolites (triacylglyceride (18:0_38:6) and AABA) selected using stepwise logistic regression discriminated BL cases from controls with an area under the curve of 0.97 (95% CI: 0.94, 1.00).ConclusionOur findings warrant further examination of plasma metabolites as potential biomarkers for BL risk/diagnosis.

Analysis of Inclisiran in the US FDA adverse event reporting system (FAERS): a focus on overall patient population and Sex-specific Subgroups.

ABSTRACT Background our study aimed to identify inclisiran-related adverse events(AEs) for primary hypercholesterolemia and arteriosclerotic cardiovascular disease(ASCVD) from the US FDA Adverse Event Reporting System (FAERS) database, analyzing its links to AEs in the overall patient population and sex-specific subgroups to improve medication safety. Methods We analyzed inclisiran-related AEs signals by using statistical methods like Reporting Odds Ratio (ROR), Proportional Reporting Ratios (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma-Poisson Shrinker (MGPS). Results Analyzing 2,400 AE reports with inclisiran as the primary suspected drug in the FAERS database, we identified 70 AE signals over 13 organ systems using the above four methods. Notable findings were strong signals for systemic diseases and various reactions at the site of administration (ROR 1.49, 95% CI 1.41-1.57), and Various musculoskeletal and connective tissue diseases (ROR 4.07, 95% CI 3.83-4.03) in overall and gender-specific populations. Myalgia, a new ADE signal not in the drug insert, was a top signal by intensity and frequency (ROR 14.76, 95% CI 12.84-16.98). Conclusion Our study revealed the strongest AE signals associated with inclisiran in both the overall population and gender subgroups, highlighting potential risks in clinical medication use and guiding balanced clinical decision-making.

Shared genetic architecture highlights the bidirectional association between major depressive disorder and fracture risk

BackgroundThere is limited evidence suggesting that osteoporosis might exacerbate depressive symptoms, while more studies demonstrate that depression negatively affects bone density and increases fracture risk.AimsTo explore the relationship between major...

Plasma proteomics of diabetic kidney disease among Asians with younger-onset type 2 diabetes.

Patients with younger onset of type 2 diabetes (YT2D) have increased risk for kidney failure compared to those with late onset. However, the mechanism of diabetic kidney disease (DKD) progression in this high-risk group is poorly understood. To identify novel biomarkers and potential causal proteins associated with DKD progression in patients with YT2D. Among YT2D (T2D onset age ≤ 40 years), 144 DKD progressors (cases) were matched for T2D onset age, sex, and ethnicity with 292 non-progressors (controls) and divided into discovery and validation sets. DKD progression was defined as decline of estimated glomerular filtration rate (eGFR) of 3ml/min/1.73m2 or greater or 40% decline in eGFR from baseline. 1472 plasma proteins were measured through a multiplex immunoassay that uses a proximity extension assay technology. Multivariable logistic regression was used to identify proteins associated with DKD progression. Mendelian randomization (MR) was used to evaluate causal relationship between plasma proteins and DKD progression. 42 plasma proteins were associated with DKD progression, independent of traditional cardio-renal risk factors, baseline eGFR and urine albumin-to-creatinine ratio (uACR). The proteins identified were related to inflammatory and remodelling biological processes. Our findings suggested angiogenin as one of the top signals (odds ratio =5.29, 95% CI 2.39-11.73, P = 4.03 × 10-5). Furthermore, genetically determined plasma angiogenin level was associated with increased odds of DKD progression. Large-scale proteomic analysis identified novel proteomic biomarkers for DKD progression in YT2D. Genetic evidence suggest a causal role of plasma angiogenin in DKD progression.

Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence.

The 'diabetic bone paradox' suggested that type 2 diabetes (T2D) patients would have higher areal bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both weighted genetic risk score (wGRS) and two-sample Mendelian randomization (MR) analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the T2D-related risk factors for fracture, we observed that the effect of T2D on the risk of fracture decreased when the number of T2D-related risk factors decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect size of T2D on fracture risk decreased if the T2D-related risk factors could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.

Deciphering the complex relationship between type 2 diabetes and fracture risk with both genetic and observational evidence

The ‘diabetic bone paradox’ suggested that type 2 diabetes (T2D) patients would have higher areal bone mineral density (BMD) but higher fracture risk than individuals without T2D. In this study, we found that the genetically predicted T2D was associated with higher BMD and lower risk of fracture in both weighted genetic risk score (wGRS) and two-sample Mendelian randomization (MR) analyses. We also identified ten genomic loci shared between T2D and fracture, with the top signal at SNP rs4580892 in the intron of gene RSPO3. And the higher expression in adipose subcutaneous and higher protein level in plasma of RSPO3 were associated with increased risk of T2D, but decreased risk of fracture. In the prospective study, T2D was observed to be associated with higher risk of fracture, but BMI mediated 30.2% of the protective effect. However, when stratified by the T2D-related risk factors for fracture, we observed that the effect of T2D on the risk of fracture decreased when the number of T2D-related risk factors decreased, and the association became non-significant if the T2D patients carried none of the risk factors. In conclusion, the genetically determined T2D might not be associated with higher risk of fracture. And the shared genetic architecture between T2D and fracture suggested a top signal around RSPO3 gene. The observed effect size of T2D on fracture risk decreased if the T2D-related risk factors could be eliminated. Therefore, it is important to manage the complications of T2D to prevent the risk of fracture.

Abstract 2845: Novel genetic risk loci for B-cell acute lymphoblastic leukemia in African American children: Findings from the Admixture and Risk of Acute Leukemia (ADMIRAL) Study

Abstract Background: Racial and ethnic disparities in incidence and outcomes for childhood B-cell acute lymphoblastic leukemia (B-ALL) are well established. While genome-wide association studies (GWASs) among children of European (EUR) genetic ancestry have identified robust risk loci, comparable studies in children of African (AFR) genetic ancestry do not exist. Here we report on results from the first GWAS of B-ALL exclusive to AFR children. Methods: A total of 840 AFR cases (diagnosis age &amp;lt;21y) and 3360 sex-/ancestry-matched controls (from ADMIRAL and ZOE 2.0, a childhood caries study) were evaluated. We used 656 cases from the Children’s Oncology Group (COG) ALL frontline trials for discovery and 184 non-COG cases for replication. Samples were genotyped with the Illumina Global Diversity Array and imputed with the TOPMed reference panel. Ancestry was ascertained with RFMix-inferred global ancestry values and principal components analysis. Logistic regression models adjusted for cryptic population substructure evaluated variant risk associations. Discovery and replication results were meta-analyzed using an inverse variance-weighted fixed effects model. Results: We identified 7 genome-wide significant (GWS, P&amp;lt;5 × 10−8) variants, including 2 novel B-ALL risk loci with replication evidence (P&amp;lt;0.05) and striking per allele effect sizes: CNTN4 (meta-analysis odds ratio [OR]=2.09, P=1.4 × 10−11) and FAM174A (OR=2.94, P=1.2 × 10−15). Established B-ALL EUR risk loci were corroborated in these data (ARIDB5, IKZF1; P&amp;lt;5 × 10−8). Five additional novel loci were GWS after meta-analysis including at SLC37A4 (OR=2.02, P=1.1 × 10−8) and CBWD1 (OR=2.69, P=2.7 × 10−9), with suggestive associations in discovery data (P&amp;lt;5 × 10−6) and nominal replication (P&amp;lt;0.05). Notably, all novel risk alleles were low-frequency in reference AFR populations and AFR-specific (minor allele frequencies of 2-9% in 1000 Genomes AFR and &amp;lt;0.1% in non-AFR). Top variants had relevant in silico annotations; variants at CNTN4 and SLC37A4 had high predicted functional probability (&amp;gt;0.8, RegulomeDb). For example, annotations for a top CNTN4 signal suggest a potential leukemogenesis mechanism, overlapping a ChIP-seq peak for CEBPA in transdifferentiated B cells. In our replication data, a polygenic risk score (PRS) with 5 novel AFR-specific GWS variants from discovery had an appreciably stronger B-ALL risk association (top 25% vs rest: OR=2.03, P=1.2 × 10−4) compared to a published B-ALL PRS from a GWAS in EUR children (top 25% vs rest: OR=1.59, P=0.014). Conclusions: Despite the modest number of AFR B-ALL cases, we successfully detected and replicated genotype risk associations with considerable effect sizes. These results demonstrate the critical importance of increasing diversity in genomic studies of childhood ALL to promote discovery and reduce disparities. Citation Format: Cindy Im, Andrew Raduski, Lauren J. Mills, Rebecca A. Johnson, Andrew DeWan, Xiaomei Ma, Joseph L. Wiemels, Catherine Metayer, Jun J. Yang, Heather H. Nelson, Tianzhong Yang, Saonli Basu, Lucie M. Turcotte, Nathan Pankratz, Michael E. Scheurer, Logan G. Spector. Novel genetic risk loci for B-cell acute lymphoblastic leukemia in African American children: Findings from the Admixture and Risk of Acute Leukemia (ADMIRAL) Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2845.

Abstract 3432: Blood metabolomic profile of glioma risk: A pooled, multi-cohort analysis in COMETS

Abstract Although gliomas are relatively rare, they are among the most lethal malignancies. Yet, with the exception of ionizing radiation and family history, their etiologic factors remain largely unknown. We conducted an untargeted metabolomics investigation pooling nested case-control studies from 9 prospective cohorts (median age 63 years, 59% women) in the Consortium of Metabolomics Studies (COMETS). One cancer-free control was individually matched to each of the 803 glioma cases based on age, sex, self-identified race/ethnicity, and blood collection date. Using an ultrahigh-performance LC-MS/MS platform (Metabolon, Inc.), we identified 874 known metabolites in pre-diagnostic serum or plasma, and logistic regression models conditioned on the matching factors) estimated odds ratios (ORs) and 95% confidence intervals for the associations between 1-SD increases in circulating metabolites and glioma risk (Bonferroni corrected significance threshold P=5.7 × 10−5). A total of 58 metabolites were associated with glioma (P&amp;lt;0.05), with the top three being 3-methylglutarylcarnitine (OR=1.19; P=0.001), cholate (OR=0.85; P=0.001) and a sphingomyelin (SM) (d18:2/21:0, d16:2/23:0, OR=1.19; P=0.002). Analysis restricted to cases diagnosed &amp;gt;5 years after blood collection showed 51 metabolites associated with risk (P&amp;lt;0.05), including SM d18:2/23:1 (OR=1.32; P=5.2 × 10−5), and two other SMs (d18:2/21:0, d16:2/23:0 and d17:1/24:1(15Z)) (ORs=1.27 and 1.26, respectively; P&amp;lt;5.6 × 10−4). Our findings were not materially altered with further adjustment for smoking, BMI, height and diabetes. Analysis restricted to high-grade glioma cases (n=595) showed similar associations, whereas low-grade glioma (n=207) showed fewer metabolites related to risk (top signals of sphingosine, sphinganine and L-glutamic acid, each inversely associated). We did, however, observe that the 2-hydroxyglutarate/alpha-ketoglutarate ratio was significantly associated with low-grade disease (OR=2.9; P=0.0008), which was most pronounced among cases diagnosed &amp;gt;10 years following blood collection (OR=9.99; P=0.003). Glioblastoma (n=485) showed similar positive risk estimates including for 3-methylglutarylcarnitine which achieved Bonferroni significance (OR=1.35; P=3 × 10−5) and several SMs. In contrast, the top metabolite for astrocytoma risk (n=88) was tyrosine (OR=0.55; P=0.002). Gene-set and principal components analyses confirmed that the sub-pathways of SM, dihydro-SM, fatty acid, and dihydroceramide metabolism were significantly associated with overall glioma risk, and dihydro-SM, dihydroceramide, SM, lactosylceramide, glutathione, and purine metabolism were associated with high-grade glioma (all P values &amp;lt;0.05). These novel findings regarding altered metabolic pathways/dysregulation occurring years before glioma diagnoses may be related to etiologic factors or subclinical disease. Citation Format: Jiaqi Huang, Bin Zhao, Stephanie J. Weinstein, Marian L. Neuhouser, Steve C. Moore, Heather Eliassen, David J. Cote, Loic Le Marchand, Lorelei Mucci, Ying Yang, Victoria Stevens, Wei Zheng, Xiao-Ou Shu, Demetrius Albanes. Blood metabolomic profile of glioma risk: A pooled, multi-cohort analysis in COMETS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3432.

Abstract A109: Novel germline genetic variants in pseudogenes associated with overall survival in advanced stage ovarian cancer treated with carboplatin, paclitaxel, and bevacizumab: Results from the ROSiA trial

Abstract A109: Novel germline genetic variants in pseudogenes associated with overall survival in advanced stage ovarian cancer treated with carboplatin, paclitaxel, and bevacizumab: Results from the ROSiA trial

Suspected adverse drug reactions of rivaroxaban reported in the United States food and drug administration adverse event reporting system database: a pharmacovigilance study.

This study aimed to characterize the safety profiles of rivaroxaban-associated suspected adverse events by mining the Food and Drug Administration Adverse Event Reporting System (FAERS). A disproportionality analysis of spontaneously reported suspected adverse drug reactions (ADRs) was conducted. The reports in FAERS from 2014 to 2024 were compiled. Frequentist and Bayesian statistics were both applied to calculate drug-AE combinations in system organ classes and preferred-term levels. Reporting odds ratio (ROR), proportional reporting ratio (PRR), the Medicines and Healthcare products Regulatory Agency (MHRA), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) methods were analyzed and used to compare the suspected AEs. Of 77,384 ADR reports, 66,705 (86.20%) were serious rivaroxaban AE reports. The most common age group was above 65years. The suspected adverse effects of rivaroxaban emerging for system organ classes (SOCs) primarily included "Gastrointestinal disorders"; "Injury, poisoning, and procedural complications", "Nervous system disorders" and "Vascular disorders". Ranked by EBGM, the top signal strength of suspected AE signals of rivaroxaban under ROR algorithm at the preferred-term (PT) level were "Haemorrhagic arteriovenous malformation" (N = 571, ROR = 756.520, PRR = 754.029, Information Component (IC) = 7.197, Empirical Bayesian Geometric Mean (EBGM) = 146.725), "Gastrointestinal vascular malformation haemorrhagic" (N = 197, ROR = 211.138, PRR = 210.950, IC = 6.614, EBGM = 97.923), and "Diverticulum intestinal haemorrhagic" (N = 722, ROR = 169.898, PRR = 169.210, IC = 6.458, EBGM = 97.920). Moreover, uncommon but significantly suspected AE signals, such as "Coagulation factor X level increased", "Basal ganglia haematoma", and "Proctitis haemorrhagic" were observed. Notably, "Gastrointestinal haemorrhage" (N = 13,436, ROR = 80.477, PRR = 74.460, IC = 5.729, EBGM = 53.042), "Upper gastrointestinal haemorrhage"(N = 2,872, ROR = 73.978, PRR = 72.797, IC = 5.706, EBGM = 52.198) and "Internal haemorrhage" (N = 2,368, ROR = 91.979, PRR = 80.899, IC = 5.813, EBGM = 56.212) exhibited relatively high occurrence rates and signal strengths. From 2014 to 2024, the IC values of rivaroxaban-associated suspected AEs for "Surgical and medical procedures" and "Cardiac disorders" showed an annual increasing trend in the time-span analysis. Based on the various visulization plots, a key discovery is that "Gastrointestinal hemorrhage" emerged as the most significant suspected AE across five algorithms. The exciting finding was that the MGPS algorithm revealed a higher risk of suspected AEs under the "Investigations" category. However, the results of the analyses of the other algorithms at the SOC level were not akin to this. Moreover, the results of signal mining for the three main types of indication populations with adverse drug reactions (ADRs), including Atrial fibrillation, Cerebrovascular accident prophylaxis, and Deep vein thrombosis were shown that "Gastrointestinal haemorrhage", "Epistaxis", "Haematuria", "Rectal haemorrhage", and "Upper gastrointestinal haemorrhage" were detected as the most common and significant signals of suspected adverse events. Rivaroxaban has risks of various suspected adverse reactions while providing therapeutic effects and being used widely. Our pharmacovigilance study may provide valuable hints that practitioners should closely monitor occurrences of "Gastrointestinal disorders", "Injury, poisoning, and procedural complications" and "Nervous system disorders", and other events in clinical applications. Consequently, it remains to persist in monitoring rivaroxaban, assessing the associated risks in the future.

A megastudy on the predictability of personal information from facial images: Disentangling demographic and non-demographic signals

While prior research has shown that facial images signal personal information, publications in this field tend to assess the predictability of a single variable or a small set of variables at a time, which is problematic. Reported prediction quality is hard to compare and generalize across studies due to different study conditions. Another issue is selection bias: researchers may choose to study variables intuitively expected to be predictable and underreport unpredictable variables (the ‘file drawer’ problem). Policy makers thus have an incomplete picture for a risk-benefit analysis of facial analysis technology. To address these limitations, we perform a megastudy—a survey-based study that reports the predictability of numerous personal attributes (349 binary variables) from 2646 distinct facial images of 969 individuals. Using deep learning, we find 82/349 personal attributes (23%) are predictable better than random from facial image pixels. Adding facial images substantially boosts prediction quality versus demographics-only benchmark model. Our unexpected finding of strong predictability of iPhone versus Galaxy preference variable shows how testing many hypotheses simultaneously can facilitate knowledge discovery. Our proposed L1-regularized image decomposition method and other techniques point to smartphone camera artifacts, BMI, skin properties, and facial hair as top candidate non-demographic signals in facial images.

Genome-wide association study of susceptibility to hospitalised respiratory infections.

Background: Globally, respiratory infections contribute to significant morbidity and mortality. However, genetic determinants of respiratory infections are understudied and remain poorly understood. Methods: We conducted a genome-wide association study in 19,459 hospitalised respiratory infection cases and 101,438 controls from UK Biobank (Stage 1). We followed-up well-imputed top signals from our Stage 1 analysis in 50,912 respiratory infection cases and 150,442 controls from 11 cohorts (Stage 2). We aggregated effect estimates across studies using inverse variance-weighted meta-analyses. Additionally, we investigated the function of the top signals in order to gain understanding of the underlying biological mechanisms. Results: From our Stage 1 analysis, we report 56 signals at P<5×10 -6, one of which was genome-wide significant ( P<5×10 -8). The genome-wide significant signal was in an intron of PBX3, a gene that encodes pre-B-cell leukaemia transcription factor 3, a homeodomain-containing transcription factor. Further, the genome-wide significant signal was found to colocalise with gene-specific expression quantitative trait loci (eQTLs) affecting expression of PBX3 in lung tissue, where the respiratory infection risk alleles were associated with decreased PBX3 expression in lung tissue, highlighting a possible biological mechanism. Of the 56 signals, 40 were well-imputed in UK Biobank and were investigated in Stage 2. None of the 40 signals replicated, with effect estimates attenuated. Conclusions: Our Stage 1 analysis implicated PBX3 as a candidate causal gene and suggests a possible role of transcription factor binding activity in respiratory infection susceptibility. However, the PBX3 signal, and the other well-imputed signals, did not replicate in the meta-analysis of Stages 1 and 2. Significant phenotypic heterogeneity and differences in study ascertainment may have contributed to this lack of statistical replication. Overall, our study highlighted putative associations and possible biological mechanisms that may provide insight into respiratory infection susceptibility.

Predicting and Exploring Abandonment Signals in a Banking Task-Oriented Chatbot Service

In this study, we developed predictive models to address the problem of chatbot abandonment, a problem that can result in losing business opportunities. Specifically, we target on a conversation log dataset of a banking chatbot involving 1,373 users and hand-crafted features. By leveraging a pre-trained BERT model on the textural features and the hand-crafted features, the model achieved an F1-score of 0.89 in predicting discontinued conversation and 0.80 in predicting abandonment. Our findings indicate that textual features help capture more abandonment, while hand-crafted features improve detection precision. Our analysis with SHAP and LIME revealed that user typing, the chatbot expressing inability of recognizing intent, and the chatbot asking what users want to do during an ongoing conversation are top signals of user abandoning the chatbot. These findings suggest that chatbot designers should consider providing pre-set options or constraints for user inputs and presenting possible intents to the user and avoid expressing inability, incompetence, or ignoring the users’ current attempt.

"TO BE OR NOT TO BE" GWAS Ends the Controversy about the DRD2 Gene as a Determinant of Reward Deficiency Syndrome (RDS).

Since 1990, there have been thousands of published studies on addiction psychiatry. Several from Blum et al showed the clinical relevance of the Genetic Addiction Risk Severity (GARS) test in identifying risk for reward deficiency behaviors in cohorts from polysubstance abuse and pain clinics, post-surgical bariatrics, and DWI offenders facing prison time. Since Blum et al first published in JAMA (1990) concerning the association of the DRD2 gene polymorphism and severe alcoholism, reactions have been mixed. More recently, however, a meta-analysis of 62 studies showed a significant association between DRD2 rs1800497 and Alcohol Use Disorder (AUD). Other studies from Yale University showed that a haplotype block of the DRD2 gene A1 allele was associated with AUD and heroin dependence. GWAS studies of depression and suicide in 1.2 million veterans confirmed the first psychiatric candidate gene study finding from Blum et al 1990; a significant association between the minor DRD2 allele, Taq A1 and severe alcoholism. Additionally, the DRD2 rs1800497 is robustly associated with suicidal behaviors. Furthermore, DNA polymorphic alleles underlying substance use disorder (SUD) with multiple substances were mapped via chromatin refolding, revealing that the DRD2 gene and associated polymorphism(s) as the top gene signal. Based on these investigations, we conclude that GWAS should end the controversy about the DRD2 gene being one determinant of Reward Deficiency Syndrome (RDS) first reported in 1996.

A Multibreed Genome-Wide Association Study for Cattle Leukocyte Telomere Length.

Telomeres are terminal DNA regions of chromosomes that prevent chromosomal fusion and degradation during cell division. In cattle, leukocyte telomere length (LTL) is associated with longevity, productive lifespan, and disease susceptibility. However, the genetic basis of LTL in this species is less studied than in humans. In this study, we utilized the whole-genome resequencing data of 239 animals from 17 cattle breeds for computational leukocyte telomere length estimation and subsequent genome-wide association study of LTL. As a result, we identified 42 significant SNPs, of which eight were found in seven genes (EXOC6B, PTPRD, RPS6KC1, NSL1, AGBL1, ENSBTAG00000052188, and GPC1) when using covariates for two major breed groups (Turano-Mongolian and European). Association analysis with covariates for breed effect detected 63 SNPs, including 13 in five genes (EXOC6B, PTPRD, RPS6KC1, ENSBTAG00000040318, and NELL1). The PTPRD gene, demonstrating the top signal in analysis with breed effect, was previously associated with leukocyte telomere length in cattle and likely is involved in the mechanism of alternative lengthening of telomeres. The single nucleotide variants found could be tested for marker-assisted selection to improve telomere-length-associated traits.