Reference-based phasing using the Haplotype Reference Consortium panel

Immune dysregulation in bipolar disorder

BACKGROUND

Bipolar disorder is a debilitating mood disorder associated with a high risk of suicide and characterized by immune dysregulation. In this study, we used a multi-faceted approach to better distinguish the pattern of dysregulation of immune profiles in individuals with BD.

METHODS

We analyzed peripheral blood mononuclear cells (bipolar disorder N = 39, control N = 30), serum cytokines (bipolar disorder N = 86, control N = 58), whole blood RNA (bipolar disorder N = 25, control N = 25), and whole blood DNA (bipolar disorder N = 104, control N = 66) to identify immune-related differences in participants diagnosed with bipolar disorder compared to controls.

RESULTS

Flow cytometry revealed a higher proportion of monocytes in participants with bipolar disorder together with a lower proportion of T helper cells. Additionally, the levels of 18 cytokines were significantly elevated, while two were reduced in participants with bipolar disorder. Most of the cytokines altered in individuals with bipolar disorder were proinflammatory. Forty-nine genes were differentially expressed in our bipolar disorder cohort and further analyses uncovered several immune-related pathways altered in these individuals. Genetic analysis indicated variants associated with inflammatory bowel disease also influences bipolar disorder risk.

DISCUSSION

Our findings indicate a significant immune component to bipolar disorder pathophysiology and genetic overlap with inflammatory bowel disease. This comprehensive study supports existing literature, whilst also highlighting novel immune targets altered in individuals with bipolar disorder. Specifically, multiple lines of evidence indicate differences in the peripheral representation of monocytes and T cells are hallmarks of bipolar disorder.

Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance

BACKGROUND

Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.

RESULTS

The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS.

CONCLUSION

It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Fine-mapping the CYP2A6 regional association with nicotine metabolism among African American smokers

The nicotine metabolite ratio (NMR; 3'hydroxycotinine/cotinine) is a stable biomarker for CYP2A6 enzyme activity and nicotine clearance, with demonstrated clinical utility in personalizing smoking cessation treatment. Common genetic variation in the CYP2A6 region is strongly associated with NMR in smokers. Here, we investigated this regional association in more detail. We evaluated the association of CYP2A6 single-nucleotide polymorphisms (SNPs) and * alleles with NMR among African American smokers (N = 953) from two clinical trials of smoking cessation. Stepwise conditional analysis and Bayesian fine-mapping were undertaken. Putative causal variants were incorporated into an existing African ancestry-specific genetic risk score (GRS) for NMR, and the performance of the updated GRS was evaluated in both African American (n = 953) and European ancestry smokers (n = 933) from these clinical trials. Five independent associations with NMR in the CYP2A6 region were identified using stepwise conditional analysis, including the deletion variant CYP2A6*4 (beta = -0.90, p = 1.55 × 10-11). Six putative causal variants were identified using Bayesian fine-mapping (posterior probability, PP = 0.67), with the top causal configuration including CYP2A6*4, rs116670633, CYP2A6*9, rs28399451, rs8192720, and rs10853742 (PP = 0.09). Incorporating these putative causal variants into an existing ancestry-specific GRS resulted in comparable prediction of NMR within African American smokers, and improved trans-ancestry portability of the GRS to European smokers. Our findings suggest that both * alleles and SNPs underlie the association of the CYP2A6 region with NMR among African American smokers, identify a shortlist of variants that may causally influence nicotine clearance, and suggest that portability of GRSs across populations can be improved through inclusion of putative causal variants.

Type 2 diabetes genetic risk and incident diabetes across diabetes risk enhancers

AIMS

To evaluate the predictive value of a contemporary type 2 diabetes (T2D) polygenic score (PGS) in detecting incident diabetes across a range of diabetes risk factors.

MATERIALS AND METHODS

We analysed participants in the Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk (FOURIER) trial (ClinicalTrials.gov, number NCT0176463), which compared the efficacy of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor evolocumab versus placebo in lowering cardiovascular outcomes in participants with stable atherosclerotic cardiovascular disease and LDL cholesterol levels of 70 mg/dL (1.8 mmol/L) or higher who were receiving statin therapy. Genetic risk was characterized using a previously validated T2D PGS based on ~1.2 million single-nucleotide polymorphisms. PGS was analysed continuously and categorically as high (top 20% of the PGS) and low to intermediate (lower 80% of the PGS). The effect of evolocumab on incident diabetes in patients without diabetes at baseline was also assessed. HbA1c was measured at baseline and every 24 weeks thereafter, while FPG was measured at baseline, week 12, week 24 and every 24 weeks thereafter. Potential cases of incident diabetes were adjudicated centrally. Hazards ratios (HRs) for incident diabetes were adjusted for baseline characteristics and ancestry.

RESULTS

Among 9388 participants, the mean age was 63 ± 9 years and 22.7% were women, with median HbA1c 39 mmol/mol (36 mmol/mol - 41 mmol/mol; 5.7% [5.4%-5.9%]) and mean body mass index (BMI) 28.7 ± 5 kg/m2. Diabetes developed in 690 participants (7.3%) during 2.3 years of median follow-up. T2D PGS predicted incident T2D (HR per 1-SD 1.22, 95% CI 1.14-1.32, p < 0.001). The rates of incident T2D in the high and low to intermediate genetic risk categories were 12.1% versus 6.8%, respectively (HR 1.43 95% CI 1.20-1.70, p < 0.001). Notably, high T2D genetic risk had greater predictive strength among individuals with lower HbA1c (P-int = 0.0499) and lower BMI (P-int = 0.004).

CONCLUSIONS

The T2D polygenic score serves as an independent predictor of incident diabetes, particularly among individuals with lower distribution of traditional risk factors.

Genetic Analysis of Asymptomatic Antinuclear Antibody Production

OBJECTIVE

Antinuclear antibodies (ANA) are detected in up to 14% of the population, and many individuals with ANA are asymptomatic. The literature on the genetic contribution to asymptomatic ANA positivity is limited. In this study, we aimed to perform a genome-wide association study of asymptomatic ANA positivity in multiple populations.

METHODS

Asymptomatic individuals who were either ANA positive or ANA negative from the All of Us Research Program were included in this study, selecting those with an ANA test performed by immunofluorescence and no evidence of autoimmune disease. Imputation was performed, and a multipopulation meta-analysis including approximately 6 million single-nucleotide polymorphisms (SNPs) was conducted. Genome-wide SNP-based heritability was estimated using the Genome-wide Complex Trait Analysis software. A cumulative genetic risk score for lupus was constructed using previously reported genome-wide significant loci.

RESULTS

A total of 1,955 asymptomatic ANA positive and 3,634 asymptomatic ANA negative individuals across three populations were included. The multipopulation meta-analysis revealed SNPs with a suggestive association (P <1 × 10-5) across 8 different loci, but no genome-wide significant loci were identified. A gene variant upstream of HLA-DQB1, (rs17211748, P = 1.4 × 10-6, odds ratio 0.82, 95% confidence interval 0.76-0.89), showed the most significant association. The heritability of asymptomatic ANA positivity was estimated to be 24.9%. Individuals who were asymptomatic and ANA positive did not exhibit increased cumulative genetic risk for lupus compared with individuals who were ANA negative.

CONCLUSION

ANA production is not associated with significant genetic risk and is primarily determined by environmental factors.

Epidemiology and genetic determination of measures of peripheral vascular health in the Long Life Family Study

Peripheral artery disease (PAD) is a major contributor to morbidity in older adults. We aimed to determine genetic and non-genetic determinants of PAD and ankle-brachial index (ABI) in the Long Life Family Study (LLFS). 3006 individuals had ABI assessment, including 1090 probands (mean age 89), 1554 offspring (mean age 60) and 362 spousal controls (mean age 61). Outcomes include minimum of right and left ABIs and PAD (ABI <0.9). Stepwise regression determined independent significant non-genetic correlates of ABI and PAD. Genomewide association and linkage analyses were adjusted for age, sex, study center, significant principal components, and independent predictors. All analyses accounted for familial relatedness. Median ABI was 1.16 and 7.4% had PAD (18.2% probands, 1.0% offspring, 1.9% controls). Correlates of PAD and lower ABI included age, SBP, and creatinine (ABI only); BMI (ABI only), HDL (ABI only) and DBP (PAD only); and antihypertensive use, current smoking, female sex (ABI only), and high school noncompletion (ABI only). Genomewide linkage identified 1 region (15q12-q13) and association identified 3 single nucleotide polymorphisms (rs780213, rs12512857, rs79644420) of interest. In these families, PAD prevalence was low compared to other studies of older adults. We identified four genomic sites that may harbor variants associated with protection from PAD.

ralphi: a deep reinforcement learning framework for haplotype assembly

Haplotype assembly is the problem of reconstructing the combination of alleles on the maternally and paternally inherited chromosome copies. Individual haplotypes are essential to our understanding of how combinations of different variants impact phenotype. In this work, we focus on read-based haplotype assembly of individual diploid genomes, which reconstructs the two haplotypes directly from read alignments at variant loci. We introduce ralphi , a novel deep reinforcement learning framework for haplotype assembly, which integrates the representational power of deep learning with reinforcement learning to accurately partition read fragments into their respective haplotype sets. To set the reward objective for reinforcement learning, our approach uses the classic reduction of the problem to the maximum fragment cut formulation on fragment graphs, where nodes correspond to reads and edge weights capture the conflict or agreement of the reads at shared variant sites. We trained ralphi on a diverse dataset of fragment graph topologies derived from genomes in the 1000 Genomes Project. We show that ralphi consistently achieves lower error rates at comparable or longer haplotype block lengths over the state of the art for short and long ONT reads at varying coverage in standard human genome benchmarks. ralphi is available at https://github.com/PopicLab/ralphi .

Proxy panels enable privacy-aware outsourcing of genotype imputation

One of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and in cases when genomic data are outsourced to perform analysis tasks, for example, genotype imputation services and federated collaborations genomic analysis. Although numerous cryptographic methods have been developed, these methods may not yet be practical for population-scale tasks in terms of computational requirements, rely on high-level expertise in security, and require each algorithm to be implemented from scratch. In this study, we focus on outsourcing of genotype imputation, a fundamental task that utilizes population-level reference panels, and develop protocols that rely on using "proxy panels" to protect genotype panels, whereas the imputation task is being outsourced at servers. The proxy panels are generated through a series of protection mechanisms such as haplotype sampling, allele hashing, and coordinate anonymization to protect the underlying sensitive panel's genetic variant coordinates, genetic maps, and chromosome-wide haplotypes. Although the resulting proxy panels are almost distinct from the sensitive panels, they are valid panels that can be used as input to imputation methods such as Beagle. We demonstrate that proxy-based imputation protects against well-known attacks with a minor decrease in imputation accuracy for variants in a wide range of allele frequencies.

Deep Learning Derived Adipocyte Size Reveals Adipocyte Hypertrophy is under Genetic Control

Fat distribution and macro structure of white adipose tissue are important factors in predicting obesity-associated diseases, but cellular microstructure of white adipose tissue has been less explored. To investigate the relationship between adipocyte size and obesity-related traits, and their underlying disease-driving genetic associations, we performed the largest study of automatic adipocyte phenotyping linking histological measurements and genetics to date. We introduce deep learning based methods for scalable and accurate semantic segmentation of subcutaneous and visceral adipose tissue histology samples (N=2,667) across 5 independent cohorts, including data from 9,000 whole slide images, with over 27 million adipocytes. Estimates of mean size of adipocytes were validated against Glastonbury et al. 2020. We show that adipocyte hypertrophy correlates with an adverse metabolic profile with increased levels of leptin, fasting plasma glucose, glycated hemoglobin and triglycerides, and decreased levels of adiponectin and HDL cholesterol. We performed the largest GWAS (N Subcutaneous = 2066, N Visceral = 1878) and subsequent meta-analysis of mean adipocyte area, and find two genome-wide significant loci (rs73184721, rs200047724) associated with increased 95%-quantile adipocyte size in respectively visceral and subcutaneous adipose tissue. Stratifying by sex, in females we find two genome-wide significant loci, with one variant (rs140503338) associated with increased mean adipocyte size in subcutaneous adipose tissue, and the other (rs11656704) is associated with decreased 95%-quantile adipocyte size in visceral adipose tissue.

PGSXplorer: an integrated nextflow pipeline for comprehensive quality control and polygenic score model development

The rapid development of next-generation sequencing technologies and genomic data sharing initiatives during the post-Human Genome Project-era has catalyzed major advances in individualized medicine research. Genome-wide association studies (GWAS) have become a cornerstone of efforts towards understanding the genetic basis of complex diseases, leading to the development of polygenic scores (PGS). Despite their immense potential, the scarcity of standardized PGS development pipelines limits widespread adoption of PGS. Herein, we introduce PGSXplorer, a comprehensive Nextflow DSL2 pipeline that enables quality control of genomic data and automates the phasing, imputation, and construction of PGS models using reference GWAS data. PGSXplorer integrates various PGS development tools such as PLINK, PRSice-2, LD-Pred2, Lassosum2, MegaPRS, SBayesR-C, PRS-CSx and MUSSEL, improving the generalizability of PGS through multi-origin data integration. Tested with synthetic datasets, our fully Docker-encapsulated tool has demonstrated scalability and effectiveness for both single- and multi-population analyses. Continuously updated as an open-source tool, PGSXplorer is freely available with user tutorials at https://github.com/tutkuyaras/PGSXplorer, making it a valuable resource for advancing precision medicine in genetic research.

Transcriptional impacts of substance use disorder and HIV on human ventral midbrain neurons and microglia

For people with HIV (PWH), substance use disorders (SUDs) are a prominent neurological risk factor, and the impacts of both on dopaminergic pathways are a potential point of deleterious convergence. Here, we profile, at single nucleus resolution, the substantia nigra (SN) transcriptomes of 90 postmortem donors in the context of chronic HIV and opioid/cocaine SUD, including 67 prospectively characterized PWH. We report altered microglial expression for hundreds of pro- and anti-inflammatory regulators attributable to HIV, and separately, to SUD. Stepwise, progressive microglial dysregulation, coupled to altered SN dopaminergic and GABAergic signaling, was associated with SUD/HIV dual diagnosis and further with lack of viral suppression in blood. In virologically suppressed donors, SUD comorbidity was associated with microglial transcriptional changes permissive for HIV infection. We report HIV-related downregulation of monoamine reuptake transporters specifically in dopaminergic neurons regardless of SUD status or viral load, and additional transcriptional signatures consistent with selective vulnerability of SN dopamine neurons.

Severe COVID-19 disease is associated with genetic factors affecting plasma ACE2 receptor and CRP concentrations

A hyperinflammatory state with highly elevated concentrations of inflammatory biomarkers such as C-reactive protein (CRP) is a characteristic feature of severe coronavirus disease 2019 (COVID-19). To examine a potential role of common genetic factors that may influence COVID-19 outcomes, we investigated whether individuals with a polygenic predisposition for a pro-inflammatory response (in the form of Polygenic Scores) are more likely to develop severe COVID-19. The innovative approach of polygenic scores to investigate genetic factors in COVID-19 severity should provide a comprehensive approach beyond single-gene studies. In our cohort of 156 patients of European ancestry, two overlapping Polygenic Scores (PGS) predicting a genetic predisposition to basal CRP concentrations were significantly different between non-severe and severe COVID-19 cases and were associated with less severe COVID-19 outcomes. Furthermore, specific single nucleotide polymorphisms (SNPs) that contribute to either of the two Polygenic Scores predicting basal CRP levels are associated with different traits that represent risk factors for COVID-19 disease initiation (ACE2 receptor, viral replication) and progression (CRP). We suggest that genetically determined enforced CRP formation may contribute to strengthening of innate immune responses and better initial pathogen control thereby reducing the risk of subsequent hyperinflammation and adverse course of COVID-19.

SPAGRM: effectively controlling for sample relatedness in large-scale genome-wide association studies of longitudinal traits

Sample relatedness is a major confounder in genome-wide association studies (GWAS), potentially leading to inflated type I error rates if not appropriately controlled. A common strategy is to incorporate a random effect related to genetic relatedness matrix (GRM) into regression models. However, this approach is challenging for large-scale GWAS of complex traits, such as longitudinal traits. Here we propose a scalable and accurate analysis framework, SPAGRM, which controls for sample relatedness via a precise approximation of the joint distribution of genotypes. SPAGRM can utilize GRM-free models and thus is applicable to various trait types and statistical methods, including linear mixed models and generalized estimation equations for longitudinal traits. A hybrid strategy incorporating saddlepoint approximation greatly increases the accuracy to analyze low-frequency and rare genetic variants, especially in unbalanced phenotypic distributions. We also introduce SPAGRM(CCT) to aggregate the results following different models via Cauchy combination test. Extensive simulations and real data analyses demonstrated that SPAGRM maintains well-controlled type I error rates and SPAGRM(CCT) can serve as a broadly effective method. Applying SPAGRM to 79 longitudinal traits extracted from UK Biobank primary care data, we identified 7,463 genetic loci, making a pioneering attempt to conduct GWAS for these traits as longitudinal traits.

Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies

In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.

Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes

Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.8% (depending on cell type) showed disease-dependent allelic effects. Across 501 colocalizations for 30 CNS traits, 23.6% had a disease dependency, even after adjusting for disease status. To estimate the unconfounded effect of genes on outcomes, we repeated the analysis using nondiseased brains (n = 183) and reported an additional 91 colocalizations not present in the larger mixed disease and control dataset, demonstrating enhanced interpretation of disease-associated variants. Principled implementation of single-cell Mendelian randomization in control-only brains identified 140 putatively causal gene-trait associations, of which 11 were replicated in the UK Biobank, prioritizing candidate peripheral biomarkers predictive of CNS outcomes.

Role of Planetary Health Diet in the association between genetic susceptibility to obesity and anthropometric measures in adults

BACKGROUND/OBJECTIVE

The roles of overall diet quality in linking genetic background with anthropometric measures are unclear, particularly regarding the recently developed Planetary Health Diet (PHD). This study aims to determine if the PHD mediates or moderates the relationship between genetic susceptibility to obesity and anthropometric measures.

SUBJECTS/METHODS

The study involved 2942 individuals from a Finnish population-based cohort (54% women, mean age 53 (SD ± 13) years). Habitual diet was assessed using a validated 130-item food frequency questionnaire, and the PHD Score (total score range 0-13 points) was adapted for Finnish food culture to evaluate diet quality. Genetic susceptibility to obesity was evaluated with a polygenic risk score (PRS) based on one million single nucleotide polymorphisms associated with body mass index (BMI). Baseline anthropometrics included weight, height, waist circumference (WC), and body fat percentage, with changes in these measures tracked over 7 years. A five-step multiple linear regression model and multivariable logistic regression with interaction terms were used to assess the mediating and moderating effects of the PHD. These analyses were also replicated in another Finnish cohort study (2 834 participants).

RESULTS

PRS for BMI was positively associated with baseline BMI and changes in anthropometric measures, except waist circumference (p = 0.12). Significant associations were observed for baseline BMI and WC (p < 0.001), changes in BMI and WC (p = 0.01), and body fat percentage change (p = 0.05). However, the PHD (average score 3.8 points) did not mediate or moderate these relationships. These findings were consistent in the replication cohort.

CONCLUSION

Diet quality assessed with the PHD did not mediate or moderate the associations between genetic susceptibility to obesity and anthropometric measures. This lack of effect may be partly due to low adherence to the PHD and the older age of participants ( > 50 years) at baseline.

Genomic Study in Opioid-Treated Cancer Patients Identifies Variants Associated With Nausea-Vomiting

CONTEXT

Opioids are the mainstay therapy for patients affected by cancer pain. However, about 10%-20% of patients do not benefit from the received analgesic treatment or experience side effects. Genetic variability might account for the variation in individual responses to opioids, both in terms of efficacy and toxicity.

OBJECTIVES

The aim of this genome-wide association study (GWAS) was to identify genetic markers of opioid toxicity, in terms of nausea-vomiting.

METHODS

Cancer patients receiving morphine, oxycodone, buprenorphine, and fentanyl were recruited from different European countries. Data about toxicity (nausea-vomiting score, NVS) and other relevant clinical information were collected, as well as genotyping data. Regression analysis between genotypes of 2052 patients and NVS was performed, using appropriate covariates, with REGENIE software.

RESULTS

We found 65 variants associated with NVS (P-value < 1.0×10-5). Of note, 14 intronic variants on chromosome 2 were in NPAS2 gene, encoding a circadian transcription factor reported to play a role in another opioid side effect, the alteration of sleep. Some of these variants were previously identified as splicing quantitative trait loci of the NPAS2 gene.

CONCLUSIONS

This is the first GWAS, performed in more than two thousand individually genotyped patients treated with opioids for cancer pain, that investigated the genetic bases of opioid-induced nausea-vomiting. Although further studies are needed to confirm our findings and to characterize the functional role of the identified variants, our results emphasize the importance of performing large pharmacogenomic studies to identify germline variants associated with opioid response, with the ultimate goal of tailoring cancer pain therapies.

Genome-wide association study of prostate-specific antigen levels in 392,522 men identifies new loci and improves prediction across ancestry groups

We conducted a multiancestry genome-wide association study of prostate-specific antigen (PSA) levels in 296,754 men (211,342 European ancestry, 58,236 African ancestry, 23,546 Hispanic/Latino and 3,630 Asian ancestry; 96.5% of participants were from the Million Veteran Program). We identified 318 independent genome-wide significant (P ≤ 5 × 10-8) variants, 184 of which were novel. Most demonstrated evidence of replication in an independent cohort (n = 95,768). Meta-analyzing discovery and replication (n = 392,522) identified 447 variants, of which a further 111 were novel. Out-of-sample variance in PSA explained by our genome-wide polygenic risk scores ranged from 11.6% to 16.6% for European ancestry, 5.5% to 9.5% for African ancestry, 13.5% to 18.2% for Hispanic/Latino and 8.6% to 15.3% for Asian ancestry and decreased with increasing age. Midlife genetically adjusted PSA levels were more strongly associated with overall and aggressive prostate cancer than unadjusted PSA levels. Our study highlights how including proportionally more participants from underrepresented populations improves genetic prediction of PSA levels, offering potential to personalize prostate cancer screening.

Strength of Genetic Associations with Thyrotropin Values Differs Between Populations with Similarity to African and European Reference Populations

Background: Epidemiological data suggest the population distribution of thyrotropin (TSH) values is shifted toward lower values in self-identified Black non-Hispanic individuals compared with self-identified White non-Hispanic individuals. It is unknown whether genetic differences between individuals with genetic similarities to African reference populations (GSA) and those with similarities to European reference populations (GSE) contribute to these observed differences. We aimed to compare genome-wide associations with TSH and putative causal TSH-associated variants between GSA and GSE groups. Methods: We performed genome-wide association studies (GWAS) in 9827 GSA individuals and 9827 GSE individuals with TSH values between 0.45 and 4.5 mU/L. We compared effect sizes and allele frequencies of previously reported putative causal TSH-associated variants and our power to detect associations with these variants between the two groups. We additionally focused on variants in PDE8B and PDE10A, loci that have been most strongly associated with TSH in previous GWAS in GSE populations. Results: Four loci attained genome-wide significance in the GSA group compared with seven in the GSE group. PDE8B was not significantly associated with TSH in the GSA group, despite its strong association in the GSE group. Eight putative causal variants had significantly different effect sizes between groups. There was ≥80% power in the GSA group to detect significant associations with variants in PDE8B, PDE10A, NFIA, and LOC105377480, with higher expected power than in the GSE group for variants in PDE8B, NFIA, and LOC105377480 and similar power for other variants in PDE8B and PDE10A. No additional putative causal variants in PDE8B and PDE10A had effect sizes that differed significantly between the groups; power to identify associations with additional putative causal variants in PDE8B and PDE10A was similar between the groups. Conclusions: Patterns of genetic associations with TSH differed between identically sized GSA and GSE groups. Failure to replicate the strongest associations previously reported in GSE individuals in our GSA population was not fully explained by differences in allele frequencies or power, assuming similar effect sizes. Larger GSA population GWAS are necessary to confirm our findings and further investigate the contribution of genetic factors to population differences in the distribution of TSH values.

TLR4 downregulation protects against cisplatin-induced ototoxicity in adult and pediatric patients with cancer

Cisplatin causes permanent hearing loss or cisplatin-induced ototoxicity in over 50% of treated patients with cancer, leading to significant social and functional limitations. Interindividual variability in developing hearing loss suggests the role of genetic predispositions to cisplatin-induced hearing loss. We investigated genetic associations between cisplatin-induced ototoxicity and toll-like receptor 4 (TLR4), an immune receptor known to mediate inflammatory responses to cisplatin. Using a case-control candidate gene approach, we identified 20 single nucleotide polymorphisms at the TLR4 locus with significant protection against ototoxicity in a cohort of 213 adult patients, followed by an independent pediatric patient cohort (n = 357). Combined cohort analysis demonstrated a significant association between cisplatin-induced ototoxicity protection and a single variant in the TLR4 promoter, rs10759932. We showed that rs10759932 downregulated TLR4 expression that is normally induced by cisplatin. This work provides pharmacogenetic and functional evidence to implicate TLR4 with cisplatin-induced hearing loss in patients. SIGNIFICANCE STATEMENT: Adult and pediatric patients carrying toll-like receptor 4 (TLR4) genetic variants were protected against developing cisplatin-induced hearing loss following cisplatin treatment. Important variants in the TLR4 promoter disrupted a drug-gene interaction between cisplatin and TLR4, mirroring the protective effect conferred by genetic inhibition of TLR4. These variants have the potential to improve the prediction of cisplatin toxicity, allowing for more precise chemotherapy treatment.

Genetics of constant and severe pain in the NAPS2 cohort of recurrent acute and chronic pancreatitis patients

Recurrent acute and chronic pancreatitis (RAP, CP) are complex, progressive inflammatory diseases with variable pain experiences impacting patient function and quality of life. The genetic variants and pain pathways in patients contributing to most severe pain experiences are unknown. We used previously genotyped individuals with RAP/CP from the North American Pancreatitis Study II (NAPS2) of European Ancestry for nested genome-wide associated study (GWAS) for pain-severity, chronicity, or both. Lead variants from GWAS were determined using FUMA. Loci with p<1e-5 were identified for post-hoc candidate identification. Transcriptome-wide association studies (TWAS) identified loci in cis and trans to the lead variants. Serum from phenotyped individuals with CP from the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translational StuDies (PROCEED) was assessed for BDNF levels using Meso Scale Discovery Immunoassay. We identified four pain systems defined by candidate genes: 1) Pancreas-associated injury/stress mitigation genes include: REG gene cluster, CTRC, NEURL3 and HSF22. 2) Neural development and axon guidance tracing genes include: SNPO, RGMA, MAML1 and DOK6 (part of the RET complex). 3) Genes linked to psychiatric stress disorders include TMEM65, RBFOX1, and ZNF385D. 4) Genes in the dorsal horn pain-modulating BDNF/neuropathic pathway included SYNPR, NTF3 and RBFOX1. In an independent cohort BDNF was significantly elevated in patients with constant-severe pain. Extension and expansion of this exploratory study may identify pathway- and mechanism-dependent targets for individualized pain treatments in CP patients. PERSPECTIVE: Pain is the most distressing and debilitating feature of chronic pancreatitis. Yet many patients with chronic pancreatitis have little or no pain. The North American Pancreatitis Study II (NAPS2) includes over 1250 pancreatitis patients of all progressive stages with all clinical and phenotypic characteristics carefully recorded. Pain did not correlate well with disease stage, inflammation, fibrosis or other features. Here we spit the patients into groups with the most severe pain and/or chronic pain syndromes and compared them genetically with patients reporting mild or minimal pain. Although some genetic variants associated with pain were expressed in cells (1) of the pancreas, most genetic variants were linked to genes expressed in the nervous system cells associated with (2) neural development and axon guidance (as needed for the descending inhibition pathway), (3) psychiatric stress disorders, and (4) cells regulating sensory nerves associated with BDNF and neuropathic pain. Similar and overlapping genetic variants in systems 2 -4 are also seen in pain syndromes form other organs. The implications for treating pancreatic pain are great in that we can no longer focus on just the pancreas. Furthermore, new treatments designed for pain disorders in other tissues may be effective in some patient with pain syndromes from the pancreas. Further research is needed to replicate and extend these observations so that new, genetics-guided rational treatments can be developed and delivered.

STICI: Split-Transformer with integrated convolutions for genotype imputation

Despite advances in sequencing technologies, genome-scale datasets often contain missing bases and genomic segments, hindering downstream analyses. Genotype imputation addresses this issue and has been a cornerstone pre-processing step in genetic and genomic studies. Although various methods have been widely adopted for genotype imputation, it remains challenging to impute certain genomic regions and large structural variants. Here, we present a transformer-based framework, named STICI, for accurate genotype imputation. STICI models automatically learn genome-wide patterns of linkage disequilibrium, evidenced by much higher imputation accuracy in regions with highly linked variants. Our imputation results on the human 1000 Genomes Project and non-human genomes show that STICI can achieve high imputation accuracy comparable to the state-of-the-art genotype imputation methods, with the additional capability to impute multi-allelic variants and various types of genetic variants. STICI can be trained for any collection of genomes automatically using self-supervision. Moreover, STICI shows excellent performance without needing any special presuppositions about the underlying patterns in collections of non-human genomes, pointing to adaptability and applications of STICI to impute missing genotypes in any species.

Developing and validating a comprehensive polygenic risk score to enhance keratoconus risk prediction

PURPOSE

This study aimed to develop and validate a comprehensive polygenic risk score (PRS) for keratoconus, enhancing the predictive accuracy for identifying individuals at increased risk, which is crucial for preventing keratoconus-associated visual impairment such as post-Laser-assisted in situ keratomileusis (LASIK) ectasia.

METHODS

We applied a multi-trait analysis approach (MTAG) to genome-wide association study data on keratoconus and quantitative keratoconus-related traits and used this to construct PRS models for keratoconus risk using several PRS methodologies. We evaluated the predictive performance of the PRSs in two biobanks: Estonian Biobank (EstBB; 375 keratoconus cases and 17 902 controls) and UK Biobank (UKB: 34 keratoconus cases and 1000 controls). Scores were compared using the area under the curve (AUC) and odds ratios (ORs) for various PRS models.

RESULTS

The PRS models demonstrated significant predictive capabilities in EstBB, with the SBayesRC model achieving the highest OR of 2.28 per standard deviation increase in PRS, with a model containing age, sex and PRS showing good predictive accuracy (AUC = 0.72). In UKB, we found that adding the best-performing PRS to a model containing corneal measurements increased the AUC from 0.84 to 0.88 (P = 0.012 for difference), with an OR of 4.26 per standard deviation increase in the PRS. These models showed improved predictive capability compared to previous keratoconus PRS.

CONCLUSION

The PRS models enhanced prediction of keratoconus risk, even with corneal measurements, showing potential for clinical use to identify individuals at high risk of keratoconus, and potentially help reduce the risk of post-LASIK ectasia.

The influence of fetal sex on maternal blood pressure in pregnancy

Background

Pregnancy with a male fetus carries a higher risk of term pre-eclampsia than pregnancy with a female fetus. Based on evidence that maternal blood pressure (BP) may be raised in pregnancies with Beckwith-Wiedemann syndrome (fetal overgrowth), a possible contributing factor to the association between male sex and term pre-eclampsia is that males grow faster, reaching ~130 g higher birth weight, on average, than females. The association between fetal sex and maternal BP in healthy pregnancies is not known. We hypothesized that male sex would be associated with higher maternal BP in healthy pregnancies, and that this association would be explained by birth weight differences between males and females.

Methods and findings

We tested the association between fetal sex and maternal systolic (SBP) and diastolic blood pressure (DBP), measured at ~28 weeks of gestation, in a meta-analysis of five different cohorts of mother-child pairs (n up to 109,842). Maternal BP was analyzed as both a continuous and dichotomized (high BP: yes or no) outcome. Linear regression models were constructed with and without adjustment for birth weight to assess whether any difference in maternal BP was explained by the difference in birth weight between male and female babies. Lastly, we constructed a fetal genetic score for birth weight using 186 own-birth-weight-associated single-nucleotide polymorphisms (SNPs) to test whether birth-weight-raising-alleles in the fetus were associated with maternal BP in pregnancy (n up to 32,232). Both maternal SBP and DBP were higher in pregnancy when carrying a male fetus compared to a female fetus (mean difference 0.35 mmHg [95%CI: 0.15-0.55] and 0.35 mmHg [95%CI: 0.21-0.49], for SBP and DBP, respectively). An independent effect of fetal sex remained when including birth weight but attenuated slightly (0.22 mmHg [95%CI: 0.02-0.42] and 0.31 mmHg [95%CI: 0.17-0.45], for SBP and DBP respectively). A positive effect estimate was found for odds of experiencing high maternal BP given pregnancy with a male fetus, but confidence intervals were wide (OR 1.05 [95%CI: 0.98-1.12]). No evidence for an association was found between a fetal birth weight genetic score and SBP or DBP when conditioned on maternal genotype.

Conclusions

We found strong evidence to support a small effect of male fetal sex on higher maternal BP in pregnancy and that larger fetal size at birth does not contribute to a substantial part of this association. Our findings do not indicate a difference in maternal BP that would warrant changes to routine monitoring in clinical practice but do suggest that male sex may be a contributing risk factor for BP-related complications.

Somatic and Stem Cell Bank to Study the Contribution of African Ancestry to Dementia: African iPSC Initiative

INTRODUCTION

Africa, home to 1.4 billion people and the highest genetic diversity globally, harbors unique genetic variants crucial for understanding complex diseases like neurodegenerative disorders. However, African populations remain underrepresented in induced pluripotent stem cell (iPSC) collections, limiting the exploration of population-specific disease mechanisms and therapeutic discoveries.

METHODS

To address this gap, we established an open-access African Somatic and Stem Cell Bank.

RESULTS

In this initial phase, we generated 10 rigorously characterized iPSC lines from fibroblasts representing five Nigerian ethnic groups and both sexes. These lines underwent extensive profiling for pluripotency, genetic stability, differentiation potential, and Alzheimer's disease and Parkinson's disease risk variants. CRISPR/Cas9 technology was used to introduce frontotemporal dementia-associated MAPT mutations (P301L and R406W).

DISCUSSION

This collection offers a renewable, genetically diverse resource to investigate disease pathogenicity in African populations, facilitating breakthroughs in neurodegenerative research, drug discovery, and regenerative medicine.

Genetic coupling of enhancer activity and connectivity in gene expression control

Gene enhancers often form long-range contacts with promoters, but it remains unclear if the activity of enhancers and their chromosomal contacts are mediated by the same DNA sequences and recruited factors. Here, we study the effects of expression quantitative trait loci (eQTLs) on enhancer activity and promoter contacts in primary monocytes isolated from 34 male individuals. Using eQTL-Capture Hi-C and a Bayesian approach considering both intra- and inter-individual variation, we initially detect 19 eQTLs associated with enhancer-eGene promoter contacts, most of which also associate with enhancer accessibility and activity. Capitalising on these shared effects, we devise a multi-modality Bayesian strategy, identifying 629 "trimodal QTLs" jointly associated with enhancer accessibility, eGene promoter contact, and gene expression. Causal mediation analysis and CRISPR interference reveal causal relationships between these three modalities. Many detected QTLs overlap disease susceptibility loci and influence the predicted binding of myeloid transcription factors, including SPI1, GABPB and STAT3. Additionally, a variant associated with PCK2 promoter contact directly disrupts a CTCF binding motif and impacts promoter insulation from downstream enhancers. Jointly, our findings suggest an inherent genetic coupling of enhancer activity and connectivity in gene expression control relevant to human disease and highlight the regulatory role of genetically determined chromatin boundaries.

Polygenic contributions to lithium augmentation outcomes in antidepressant non-responders with unipolar depression

Objective

Lithium augmentation (LA) is an effective treatment for patients with major depression after inadequate antidepressant response, but therapeutic outcomes vary considerably between individuals. Molecular studies could yield novel insights into treatment prediction to enable personalized therapy choices. Here, we investigated the effects of polygenic risk scores (PRS) for schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) on clinical outcomes following LA.

Methods

Recent GWAS summary statistics were used to construct disorder-specific PRS in lithium-augmented MDD patients who participated in a prospective study after poor response to at least one antidepressant drug. Depressive symptoms were assessed for four weeks or longer using the Hamilton Depression Rating Scale (HAMD). Hazard ratios (HR) of favorable outcomes, response (≥ 50% reduction in HAMD composite scores) and remission (HAMD ≤ 7), were estimated by Cox proportional hazards regression models adjusted for ancestry, demographic, and clinical covariates.

Results

In 193 patients, BIP-PRS was positively associated with both response (HR = 1.29, 95% CI = 1.02-1.63, p = 0.03, Nagelkerke R2 = 2.51%) and remission (HR = 1.52, 95% CI = 1.14-2.04, p = 0.004, Nagelkerke R2 = 4.53%) after LA. Our data further suggest that individuals who carry a lower polygenic burden for MDD tend to respond better to LA (HR = 0.81, 95% CI = 0.66-1.00, p = 0.048, Nagelkerke R2 = 1.99%). No associations were observed between SCZ-PRS and either clinical outcome (p > 0.05).

Conclusions

Our findings indicate that individuals at higher polygenic risk for BIP and lower polygenic risk for MDD are more likely to benefit from augmentation with lithium. If replicated, PRS may inform future efforts to establish clinical prediction models for LA outcomes in unipolar depression.

Multi-ancestry genome-wide association analyses: a comparison of meta- and mega-analyses in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study

BACKGROUND

There is increasing need for effective incorporation of high-dimensional genetics data from individuals with varied ancestry in genome-wide association (GWAS) analyses. Classically, multi-ancestry GWAS analyses are performed using statistical meta-analysis to combine results conducted within homogeneous ancestry groups. The emergence of cosmopolitan reference panels makes collective preprocessing of GWAS data possible, but impact on downstream GWAS results in a mega-analysis framework merits investigation. We utilized GWAS data from the multi-national Hyperglycemia and Adverse Pregnancy Outcome Study to investigate differences in GWAS findings using a homogeneous ancestry meta-analysis versus a heterogeneous ancestry mega-analysis pipeline. Maternal fasting and 1-hr glucose and metabolomics measured during a 2-hr 75-gram oral glucose tolerance test during early third trimester pregnancy were evaluated as phenotypes.

RESULTS

For the homogeneous ancestry meta-analysis pipeline, variant data were prepared by identifying sets of individuals with similar ancestry and imputing to ancestry-specific reference panels. GWAS was conducted within each ancestry group and results were combined using random-effects meta-analysis. For the heterogeneous ancestry mega-analysis pipeline, data for all individuals were collectively imputed to the Trans-Omics for Precision Medicine (TOPMed) cosmopolitan reference panel, and GWAS was conducted using a unified mega-analysis. The meta-analysis pipeline identified genome-wide significant associations for 15 variants in a region close to GCK on chromosome 7 with maternal fasting glucose and no significant findings for 1-hr glucose. Associations in this same region were identified using the mega-analysis pipeline, along with a well-documented association at MTNR1B on chromosome 11 with both fasting and 1-hr maternal glucose. For metabolomics analyses, the number of significant findings in the heterogeneous ancestry mega-analysis far exceeded those from the homogeneous ancestry meta-analysis and confirmed many previously documented associations, but genomic inflation factors were much more variable.

CONCLUSIONS

For multi-ancestry GWAS, heterogeneous ancestry mega-analysis generates a rich set of variants for analysis using a cosmopolitan reference panel and results in vastly more significant, biologically credible and previously documented associations than a homogeneous ancestry meta-analysis approach. Genomic inflation factors do indicate that findings from the mega-analysis pipeline may merit cautious interpretation and further follow-up.

Local cortical structure pattern and genetic links in schizophrenia: An MRI and CRISPR/Cas9 study

While the etiology of schizophrenia (SZ) remains elusive, its diverse phenotypes suggest the involvement of distinct functional cortical areas, and the heritability of SZ implies the underlying genetic factors. This study aimed to integrate imaging and molecular analyses to elucidate the genetic underpinnings of SZ. We investigated the local cortical structural pattern changes in Brodmann areas (BAs) by calculating the cortical structural pattern index (SPI) using magnetic resonance imaging analysis from 194 individuals with SZ and 330 controls. Significant local structural changes were detected in certain Brodmann areas in symmetric or asymmetric patterns, such as symmetric changes in the BA4 primary motor area and BA23 part of posterior cingulate cortex, and asymmetric changes in the BA13 insula, BA11 inferior orbitofrontal area, and BA 24, and BA 31 cingulate cortex. Following genome-wide association tests, we found genetic variants and SNP-mapped genes and verified the areal preferential expression profiles in the developing human and mouse neocortex. Finally, we performed a loss-of-function analysis using the CRISPR/Cas9 system to investigate the effects of disrupting the SZ-related SNP-mapped Morf4l1, Reep3, or Tmed3 gene on cortical cell fate to understand their roles in generating appropriate composition of cortical neurons. This study outlines a pipeline for identifying local structural changes, associated genetic causes, and potential molecular mechanisms underlying mental disorders. Additionally, these data shed light on establishing a structurally integral cerebral cortex for higher cognitive functions.

Genomics yields biological and phenotypic insights into bipolar disorder

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Functionally enriched human polymorphisms associate to species in the chronic wound microbiome

Chronic wounds are a burden to millions of patients and healthcare providers worldwide. With rising incidence and prevalence, there is an urgent need to address non-healing wounds with novel approaches. Impaired wound healing has been shown to be associated with wound microbiota, and multiple bacterial species are known to contribute to delays in closure. Recent evidence suggests human genetics may shape differences in composition of wound microbiomes, and unraveling this relationship has important implications for understanding wound bioburden and tailoring treatment. Here, a two-stage microbiome genome wide association study (mbGWAS; n=509) was used to test effects of human genetics on the relative abundances of bacterial species detected in chronic wounds using bacterial 16S rRNA gene sequencing. Sixteen species were significantly associated to 193 genetic loci distributed across 25 non-overlapping genomic regions. No locus was associated with more than one species, with heritability estimates per species ranging up to 20%. Functional analyses on genomic regions and species resulted in overrepresentation pertaining to pathways relevant to microbial infection and wound healing, suggesting that genetic and species interactions jointly influence the wound microenvironment. Species associated to host genetics in turn exhibited significant co-occurrence relationships with common wound pathogens including Staphylococcus aureus and Finegoldia magna . Moreover, the overall genetic distance among patients was significantly related to differences in their overall wound microbiome composition. Identification of such genetic biomarkers reveals new mechanistic insight into patient-microbiome interactions and provides an avenue to identify predictive risk factors for diagnosis and management of chronic wounds.

Genetic Predisposition to Hippocampal Atrophy and Risk of Amnestic Mild Cognitive Impairment and Alzheimer's Dementia

BACKGROUND

There is a paucity of evidence on the association between genetic propensity for hippocampal atrophy with cognitive outcomes. Therefore, we examined the relationship of the polygenic risk score for hippocampal atrophy (PRShp) with the incidence of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) as well as the rates of cognitive decline.

METHODS

Participants were drawn from the population-based HELIAD cohort. Comprehensive neuropsychological assessments were performed at baseline and at follow-up. PRShp was derived from the summary statistics of a large genome-wide association study for hippocampal volume. Cox proportional hazards models as well as generalized estimating equations (GEEs) were used to evaluate the association of PRShp with the combined incidence of aMCI/AD and cognitive changes over time, respectively. All models were adjusted for age, sex, education, and apolipoprotein E (APOE) genotype.

RESULTS

Our analysis included 618 older adults, among whom 73 developed aMCI/AD after an average follow-up of 2.96 ± 0.8 years. Each additional SD of PRShp elevated the relative hazard for incident aMCI/AD by 46%. Participants at the top quartile of PRShp had an almost three times higher risk of converting to aMCI/AD compared to the lowest quartile group. Higher PRShp scores were also linked to steeper global cognitive and memory decline. The impact of PRShp was greater among women and younger adults.

CONCLUSIONS

Our findings support the association of PRShp with aMCI/AD incidence and with global cognitive and memory decline over time. The PRS association was sex- and age-dependent, suggesting that these factors should be considered in genetic modelling for AD.

Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

A genealogy-based approach for revealing ancestry-specific structures in admixed populations

Elucidating ancestry-specific structures in admixed populations is crucial for comprehending population history and mitigating confounding effects in genome-wide association studies. Existing methods for elucidating the ancestry-specific structures generally rely on frequency-based estimates of genetic relationship matrix (GRM) among admixed individuals after masking segments from ancestry components not being targeted for investigation. However, these approaches disregard linkage information between markers, potentially limiting their resolution in revealing structure within an ancestry component. We introduce ancestry-specific expected GRM (as-eGRM), a novel framework for elucidating the relatedness within ancestry components between admixed individuals. The key design of as-eGRM consists of defining ancestry-specific pairwise relatedness between individuals based on genealogical trees encoded in the Ancestral Recombination Graph (ARG) and local ancestry calls and computing the expectation of the ancestry-specific relatedness across the genome. Comprehensive evaluations using both simulated stepping-stone models of population structure and empirical datasets based on three-way admixed Latino cohorts showed that analysis based on as-eGRM robustly outperforms existing methods in revealing the structure in admixed populations with diverse demographic histories. Taken together, as-eGRM has the promise to better reveal the fine-scale structure within an ancestry component of admixed individuals, which can help improve the robustness and interpretation of findings from association studies of disease or complex traits for these understudied populations.

Implementation of a genotyped African population cohort, with virtual follow-up: A feasibility study in the Western Cape Province, South Africa

Background

There is limited knowledge regarding African genetic drivers of disease due to prohibitive costs of large-scale genomic research in Africa.

Methods

We piloted a scalable virtual genotyped cohort in South Africa that was affordable in this resource-limited context, cost-effective, scalable virtual genotyped cohort in South Africa, with participant recruitment using a tiered informed consent model and DNA collection by buccal swab. Genotype data was generated using the H3Africa Illumina micro-array, and phenotype data was derived from routine health data of participants. We demonstrated feasibility of nested case control genome wide association studies using these data for phenotypes type 2 diabetes mellitus (T2DM) and severe COVID-19.

Results

2267346 variants were analysed in 459 participant samples, of which 229 (66.8%) are female. 78.6% of SNPs and 74% of samples passed quality control (QC). Principal component analysis showed extensive ancestry admixture in study participants. Of the 343 samples that passed QC, 93 participants had T2DM and 63 had severe COVID-19. For 1780 previously published COVID-19-associated variants, 3 SNPs in the pre-imputation data and 23 SNPS in the imputed data were significantly associated with severe COVID-19 cases compared to controls (p<0.05). For 2755 published T2DM associated variants, 69 SNPs in the pre-imputation data and 419 SNPs in the imputed data were significantly associated with T2DM cases when compared to controls (p<0.05).

Conclusions

The results shown here are illustrative of what will be possible as the cohort expands in the future. Here we demonstrate the feasibility of this approach, recognising that the findings presented here are preliminary and require further validation once we have a sufficient sample size to improve statistical significance of findings.We implemented a genotyped population cohort with virtual follow up data in a resource-constrained African environment, demonstrating feasibility for scale up and novel health discoveries through nested case-control studies.

Long-read sequencing reveals novel genetic polymorphisms in the major histocompatibility complex region and their impacts on the Han Chinese population

Human leukocyte antigen (HLA) genes in the major histocompatibility complex (MHC) region are crucial for immunity and are associated with numerous diseases and phenotypes. The MHC region's complexity and high genetic diversity make it challenging to analyze using short-read sequencing (SRS) technology. We sequence the MHC region of 100 Han Chinese individuals using both long-read sequencing (LRS) and SRS platforms at approximately 30X coverage to study genetic alterations and their potential functional impacts. LRS provides significantly greater coverage of the MHC region and eight classical HLA genes, particularly at the HLA-DRB1 locus, compared with SRS. We detect 78,249 single nucleotide polymorphisms (SNPs) using LRS, with 26.0% undetectable by SRS. Based on SNP and inferred HLA allele types, we construct an LRS-based MHC reference panel for the Han Chinese, containing approximately 2.6 times more genetic variants than the SRS-based Han-MHC reference panel. A phenome-wide association study assessing 26,024 phenotypes across 15 categories identifies significant associations for 7,879 independent variants (including 809 LRS-specific SNPs) with 409 phenotypes in nine categories. This analysis reveals 24 unreported HLA allele associations in the bioelectric and cellular categories. The conditional analysis identifies 530 independent signals across the 409 phenotypes, including 28 previously unreported signals of eight classical HLA genes associated with 33 phenotypes. Of the top-associated SNPs, 191 are detected by LRS only. Fine-mapping identifies 126 independent candidate causal SNPs for three immune-related cellular phenotypes, with 17 detected exclusively by LRS. Our study reveals previously unreported variants and their functional impacts in the MHC region, enhancing our understanding of genetic diversity and its potential biological implications in the Han Chinese population.

Patterns of population structure and genetic variation within the Saudi Arabian population

The Arabian Peninsula is considered the initial site of historic human migration out of Africa. The modern-day indigenous Arabians are believed to be the descendants who remained from the ancient split of the migrants into Eurasia. Here, we investigated how the population history and cultural practices such as endogamy have shaped the genetic variation of the Saudi Arabians. We genotyped 3,352 individuals and identified twelve genetic sub-clusters that corresponded to the geographical distribution of different tribal regions, differentiated by distinct components of ancestry based on comparisons to modern and ancient DNA references. These sub-clusters also showed variation across ranges of the genome covered in runs of homozygosity, as well as differences in population size changes over time. Using 25,488,981 variants found in whole genome sequencing data (WGS) from 302 individuals, we found that the Saudi tend to show proportionally more deleterious alleles than neutral alleles when compared to Africans/African Americans from gnomAD (e.g. a 13% increase of deleterious alleles annotated by AlphaMissense between 0.5 - 5% frequency in Saudi, compared to 7% decrease of the benign alleles; P < 0.001). Saudi sub-clusters with greater inbreeding and lower effective population sizes showed greater enrichment of deleterious alleles as well. Additionally, we found that approximately 10% of the variants discovered in our WGS data are not observed in gnomAD; these variants are also enriched with deleterious annotations. To accelerate studying the population-enriched deleterious alleles and their health consequences in this population, we made available the allele frequency estimates of 25,488,981 variants discovered in our samples. Taken together, our results suggest that Saudi's population history impacts its pattern of genetic variation with potential consequences to the population health. It further highlights the need to sequence diverse and unique populations so to provide a foundation on which to interpret medical- and pharmaco- genomic findings from these populations.

Variants in the β-globin locus are associated with pneumonia in African American children

In African American adults, the strongest genetic predictor of pneumonia appears to be the A allele of rs334, a variant in the β-globin gene, which in homozygous form causes sickle cell disease (SCD). No comparable studies have been done in African American children. We performed genome-wide association analyses of 482 African American children with documented pneumonia and 2,048 African American control individuals using genotypes imputed from two reference panels: 1000 Genomes (1KG) (which contains rs334) and TOPMed (does not contain rs334). Using 1KG imputed genotypes, the most significant variant was rs334 (A allele; odds ratio [OR] = 2.76; 95% CI, 2.21-3.74; p = 5.9 × 10-19); using TOPMed imputed genotypes the most significant variant was rs2226952, found in the β-globin locus control region (G allele; OR = 2.14; 95% CI, 1.78-2.57; p = 5.1 × 10-16). After conditioning on rs334, the most strongly associated variant in the β-globin locus, rs33930165 (T allele, 1KG: OR = 4.09; 95% CI, 2.29-7.29; p = 1.7 × 10-6; TOPMed: OR = 3.58; 95% CI, 2.18-5.90; p = 4.7 × 10-7), which as a compound heterozygote with rs334 A allele, can cause SCD. To compare the power of different sample sets we developed a way to estimate the power of sample sets with different sample sizes, genotype arrays, and imputation platforms. Our results suggest that, in African American children, the strongest genetic determinants of pneumonia are those that increase the risk of SCD.

Gene signatures derived from transcriptomic-causal networks stratify colorectal cancer patients for effective targeted therapy

BACKGROUND

Gene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions.

METHODS

We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes. This integrative approach involved germline genotype and tumor RNA-seq data from 1165 metastatic CRC patients. The patients were enrolled in a randomized clinical trial receiving either cetuximab or bevacizumab in combination with chemotherapy. An external cohort of paired CRC normal and tumor samples, along with protein-protein interaction databases, was used for replication.

RESULTS

We identify promising predictive and prognostic gene signatures from pre-treatment gene expression profiles. Our study discerns sets of genes, each forming a signature that collectively contribute to define patient subgroups with different prognosis and response to the therapies. Using an external cohort, we show that the genes influencing OS within the signatures, such as FANCI and PRC1, are upregulated in CRC tumor vs. normal tissue. These signatures are highly associated with immune features, including macrophages, cytotoxicity, and wound healing. Furthermore, the corresponding proteins encoded by the genes within the signatures interact with each other and are functionally related.

CONCLUSIONS

This study underscores the utility of gene signatures derived from transcriptomic-causal networks in patient stratification for effective therapies. The interpretability of the findings, supported by replication, highlights the potential of these signatures to identify patients likely to benefit from cetuximab or bevacizumab.

Response eQTLs, chromatin accessibility, and 3D chromatin structure in chondrocytes provide mechanistic insight into osteoarthritis risk

Osteoarthritis (OA) poses a significant healthcare burden with limited treatment options. While genome-wide association studies (GWASs) have identified over 100 OA-associated loci, translating these findings into therapeutic targets remains challenging. To address this gap, we mapped gene expression, chromatin accessibility, and 3D chromatin structure in primary human articular chondrocytes in both resting and OA-mimicking conditions. We identified thousands of differentially expressed genes, including those associated with differences in sex and age. RNA sequencing in chondrocytes from 101 donors across two conditions uncovered 3,782 unique eGenes, including 420 that exhibited strong and significant condition-specific effects. Colocalization with OA GWAS signals revealed 13 putative OA risk genes, 6 of which have not been previously identified. Chromatin accessibility and 3D chromatin structure provided insights into the mechanisms and conditional specificity of these variants. Our findings shed light on OA pathogenesis and highlight potential targets for therapeutic development.

Deep analysis of the major histocompatibility complex genetic associations using covariate analysis and haploblocks unravels new mechanisms for the molecular etiology of Elite Control in AIDS

INTRODUCTION

We have reanalyzed the genomic data from the International Collaboration for the Genomics of HIV (ICGH), focusing on HIV-1 Elite Controllers (EC).

METHODS

A genome-wide association study (GWAS) was performed, comparing 543 HIV-1 EC individuals with 3,272 uninfected controls (CTR) of European ancestry. 8 million single nucleotide polymorphisms (SNPs) and HLA class I and class II gene alleles were imputed to compare EC and CTR.

RESULTS

Two thousand six hundred twenty-six SNPs were associated with EC (p<5.10-8), all located within the Major Histocompatibility Complex (MHC) region. Stepwise regression analysis narrowed this list to 17 SNPs. In parallel, 22 HLA class I and II alleles were associated with EC. Through meticulous mapping of the LD between all identified signals and employing reciprocal covariate analyses, we delineated a final set of 6 independent SNPs and 3 HLA class I gene alleles that accounted for most of the associations observed with EC. Our study revealed the presence of cumulative haploblock effects (SNP rs9264942 contributing to the HLA-B*57:01 effect) and that several HLA allele associations were in fact caused by SNPs in linkage disequilibrium (LD). Upon investigating SNPs in LD with the selected 6 SNPs and 3 HLA class I alleles for their impact on protein function (either damaging or differential expression), we identified several compelling mechanisms potentially explaining EC among which: a multi-action mechanism of HLA-B*57:01 involving MICA mutations and MICB differential expression overcoming the HIV-1 blockade of NK cell response, and overexpression of ZBTB12 with a possible anti-HIV-1 effect through HERV-K interference; a deleterious mutation in PPP1R18 favoring viral budding associated with rs1233396.

CONCLUSION

Our results show that MHC influence on EC likely extends beyond traditional HLA class I or class II allele associations, encompassing other MHC SNPs with various biological impacts. They point to the key role of NK cells in preventing HIV-1 infection. Our analysis shows that HLA-B*57:01 is indeed associated with partially functional MICA/MICB proteins which could also explain this marker's involvement in other diseases such as psoriasis. More broadly, our findings suggest that within any HLA class I and II association in diseases, there may exist distinct causal SNPs within this crucial, gene-rich, and LD-rich MHC region.

GWAS highlights the neuronal contribution to multiple sclerosis susceptibility

Multiple Sclerosis (MS) is a chronic inflammatory and neurodegenerative disease affecting the brain and spinal cord. Genetic studies have identified many risk loci, that were thought to primarily impact immune cells and microglia. Here, we performed a multi-ancestry genome-wide association study with 20,831 MS and 729,220 control participants, identifying 236 susceptibility variants outside the Major Histocompatibility Complex, including four novel loci. We derived a polygenic score for MS and, optimized for European ancestry, it is informative for African-American and Latino participants. Integrating single-cell data from blood and brain tissue, we identified 76 genes affected by MS risk variants. Notably, while T cells showed the strongest enrichment, inhibitory neurons emerged as a key cell type. The expression of IL7 and STAT3 are affected only in inhibitory neurons, highlighting the importance of neuronal and glial dysfunction in MS susceptibility.

Scent of COVID-19: Whole-Genome Sequencing Analysis Reveals the Role of ACE2, IFI44, and NDUFAF4 in Long-Lasting Olfactory Dysfunction

COVID-19-related persistent olfactory dysfunction (OD) presents remarkable interindividual differences, and little is known about the host genetic factors that are involved in its etiopathogenesis. The goal of this study was to explore the genetic factors underpinning COVID-19-related OD through the analysis of Whole Genome Sequencing data of 153 affected subjects, focusing on genes involved in antiviral response regulation. An innovative approach was developed, namely the assessment of the association between a "gene score", defined as the ratio of the number of homozygous alternative variants within the gene to its length, and participants' olfactory function. The analysis highlighted how an increased gene score in the ACE2 gene is associated with a worse olfactory performance, while an increased gene score in the IFI44 and NDUFAF4 genes is associated with a better olfactory function. Considering the physiological role of the proteins encoded by these genes, it can be hypothesized that a reduced expression of ACE2 may be associated with a protracted and severe inflammatory response in the olfactory epithelium, thus worsening patients' smell abilities. Conversely, an increased gene score in IFI44 and NDUFAF4 might be associated with a decreased inflammatory response, thus correlating with a better olfactory performance. Overall, this study identified new host genetic factors that may play a pivotal role in determining COVID-19-related OD heterogeneity, possibly enabling more personalized and effective clinical management for affected individuals.

A genome-wide association study of European advanced cancer patients treated with opioids identifies regulatory variants on chromosome 20 associated with pain intensity

BACKGROUND

Opioids in step III of the WHO analgesic ladder are the standard of care for treating cancer pain. However, a significant minority of patients do not benefit from therapy. Genetics might play a role in predisposing patients to a good or poor response to opioids. Here, we investigated this issue by conducting a genome-wide association study (GWAS).

METHODS

We genotyped 2057 European advanced cancer patients treated with morphine, buprenorphine, fentanyl and oxycodone. We carried out a whole-genome regression model (using REGENIE software) between genotypes and the opioid response phenotype, defined as a numerical score measuring patient pain intensity.

RESULTS

The GWAS identified five non-coding variants on chromosome 20 with a p-value <5.0 × 10-8. For all of them, the minor allele was associated with lower pain intensity. These variants were intronic to the PCMTD2 gene and were 200 kbp downstream of OPRL1, the opioid related nociceptin receptor 1. Notably according to the eQTLGen database, these variants act as expression quantitative trait loci, modulating the expression mainly of PCMTD2 but also of OPRL1. Variants in the same chromosomal region were recently reported to be significantly associated with pain intensity in a GWAS conducted in subjects with different chronic pain conditions.

CONCLUSIONS

Our results support the role of genetics in the opioid response in advanced cancer patients. Further functional analyses are needed to understand the biological mechanism underlying the observed association and lead to the development of individualized pain treatment plans, ultimately improving the quality of life for cancer patients.

SIGNIFICANCE STATEMENT

This genome-wide association study on European advanced cancer patients treated with opioids identifies novel regulatory variants on chromosome 20 (near PCMTD2 and OPRL1 genes) associated with pain intensity. These findings enhance our understanding of the genetic basis of opioid response, suggesting new potential markers for opioid efficacy. The study is a significant advancement in pharmacogenomics, providing a robust dataset and new insights into the genetic factors influencing pain intensity, which could lead to personalized cancer pain management.

Interactions of genes with alcohol consumption affect insulin sensitivity and beta cell function

AIMS/HYPOTHESIS

Alcohol consumption has complex effects on diabetes and metabolic disease, but there is widespread heterogeneity within populations and the specific reasons are unclear. Genetic factors may play a role and warrant exploration. The aim of this study was to elucidate genetic variants modulating the impact of alcohol consumption on insulin sensitivity and pancreatic beta cell function within populations presenting normal glucose tolerance (NGT).

METHODS

We recruited 4194 volunteers in Nanjing, 854 in Jurong and an additional 5833 in Nanjing for Discovery cohorts 1 and 2 and a Validation cohort, respectively. We performed an OGTT on all participants, establishing a stringent NGT group, and then assessed insulin sensitivity and beta cell function. Alcohol consumption was categorised as abstinent, light-to-moderate (<210 g per week) or heavy (≥210 g per week). After excluding ineligible individuals, an exploratory genome-wide association study identified potential variants interacting with alcohol consumption in 1862 NGT individuals. These findings were validated in an additional cohort of 2169 NGT individuals. Cox proportional hazard regression was further employed to evaluate the effect of the interaction between the potential variants and alcohol consumption on the risk of type 2 diabetes within the UK Biobank cohort.

RESULTS

A significant correlation was observed between drinking levels and insulin sensitivity, accompanied by a consequent inverse relationship with insulin resistance and beta cell insulin secretion after adjusting for confounding factors in NGT individuals. However, no significant associations were noted in the disposition indexes. The interaction of variant rs56221195 with alcohol intake exhibited a pronounced effect on the liver insulin resistance index (LIRI) in the discovery set, corroborated in the validation set (combined p=1.32 × 10-11). Alcohol consumption did not significantly affect LIRI in rs56221195 wild-type (TT) carriers, but a strong negative association emerged in heterozygous (TA) and homozygous (AA) individuals. The rs56221195 variant also significantly interacts with alcohol consumption, influencing the total insulin secretion index INSR120 (the ratio of the AUC of insulin to glucose from 0 to 120 min) (p=2.06 × 10-9) but not disposition index. In the UK Biobank, we found a significant interaction between rs56221195 and alcohol consumption, which was linked to the risk of type 2 diabetes (HR 0.897, p=0.008).

CONCLUSIONS/INTERPRETATION

Our findings reveal the effects of the interaction of alcohol and rs56221195 on hepatic insulin sensitivity in NGT individuals. It is imperative to weigh potential benefits and detriments thoughtfully when considering alcohol consumption across diverse genetic backgrounds.

ZIC1 is a context-dependent medulloblastoma driver in the rhombic lip

Transcription factors are frequent cancer driver genes, exhibiting noted specificity based on the precise cell of origin. We demonstrate that ZIC1 exhibits loss-of-function (LOF) somatic events in group 4 (G4) medulloblastoma through recurrent point mutations, subchromosomal deletions and mono-allelic epigenetic repression (60% of G4 medulloblastoma). In contrast, highly similar SHH medulloblastoma exhibits distinct and diametrically opposed gain-of-function mutations and copy number gains (20% of SHH medulloblastoma). Overexpression of ZIC1 suppresses the growth of group 3 medulloblastoma models, whereas it promotes the proliferation of SHH medulloblastoma precursor cells. SHH medulloblastoma ZIC1 mutants show increased activity versus wild-type ZIC1, whereas G4 medulloblastoma ZIC1 mutants exhibit LOF phenotypes. Distinct ZIC1 mutations affect cells of the rhombic lip in diametrically opposed ways, suggesting that ZIC1 is a critical developmental transcriptional regulator in both the normal and transformed rhombic lip and identifying ZIC1 as an exquisitely context-dependent driver gene in medulloblastoma.

Correlation between polygenic risk scores of depression and cortical morphology networks

BACKGROUND

Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls.

METHODS

We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T 1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback-Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level.

RESULTS

We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status.

LIMITATIONS

Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure.

CONCLUSION

The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.

Polygenic Score: A Tool for Evaluating the Genetic Background of Sporadic Hidradenitis Suppurativa

Sporadic hidradenitis suppurativa (spHS) is a multifactorial disease in which genetic predisposition is intertwined with environmental factors. Owing to the still-to-date limited knowledge of spHS genetics, we calculated polygenic scores (PGSs) to study the genetic underpinnings that contribute to spHS within European demographic. A total of 256 patients with spHS and 1686 healthy controls were analyzed across 6 European clinical centers. PGSs were calculated using a clumping and thresholding technique on 70% of the total sample, with the remaining 30% used for testing. The PANTHER tool was used to identify overrepresented genes. We generated a PGS characterized by 923 SNPs with a statistically significant association with spHS (P = 2 × 10-2). The statistically significant age-, sex-, and ancestry-adjusted association of our developed PGSs in spHS allows us to attribute a genetic contribution to the susceptibility of spHS (pseudo-R2 = 0.0053). Variants enriched for developing PGSs show a statistically significant preference for mapping to genes that encode primarily for cell adhesion proteins. Although this study developed a polygenic model associated with spHS, the low number of patients enrolled is a limitation. However, we believe that with larger experimental datasets, our model has the potential to serve as a valuable tool for predicting spHS states in future studies.

Genetic Variants Associated With Preeclampsia and Maternal Serum sFLT1 Levels

BACKGROUND

Elevated maternal serum sFLT1 (soluble fms-like tyrosine kinase 1) has a key role in the pathophysiology of preeclampsia. We sought to determine the relationship between the maternal and fetal genome and maternal levels of sFLT1 at 12, 20, 28, and 36 weeks of gestational age (wkGA).

METHODS

We studied a prospective cohort of nulliparous women (3968 mother-child pairs). We related maternal and fetal genotype to the adjusted sFLT1 Z score and sFLT1:placental growth factor (PlGF) ratio Z score at each wkGA and the change in the Z score between 28 and 36 wkGA (Δ36-28). We studied genetic variants from a previous fetal genome-wide association study of preeclampsia and an externally defined polygenic score from a maternal genome-wide association study of preeclampsia.

RESULTS

Four variants from the fetal preeclampsia genome-wide association study were positively associated with sFLT1 and sFLT1:PlGF Z score at 36 wkGA, and FLT1 enhancer single-nucleotide polymorphisms were associated with increased Δ36-28 of sFLT1. The associations were specific for the fetal genome or stronger for the fetal than the maternal genome. An increased risk of preeclampsia based on the maternal polygenic score for preeclampsia was associated with lower levels of sFLT1 and sFLT1:PlGF ratio in the first trimester and a greater Δ36-28 for sFLT1.

CONCLUSIONS

The current data are consistent with a causal association between sFLT1 released by the placenta in late pregnancy and the pathophysiology of preeclampsia. The data are also consistent with maternal components to the protective effect of high sFLT1 in the first trimester and the rise in third-trimester sFLT1 levels and preeclampsia.