GWAS Traits Research Articles

Genome-wide association study, combined with bulk segregant analysis, identify plant receptors and defense related genes as candidate genes for downy mildew resistance in quinoa

BackgroundDowny mildew is the most relevant disease of quinoa and the most widespread. Though, little is known about the genetics of resistance to this disease. The objective of this study was to identify the genomic regions controlling downy mildew resistance in quinoa and candidate genes for this trait. With this aim we carried out a GWAS analysis in a collection formed by 211 quinoa accessions from different origins. This approach was combined with inheritance studies and Bulk Segregant Analysis (BSA) in a segregating population.ResultsGWAS analysis identified 26 genomic regions associated with the trait. Inheritance studies in a F2 population segregating for resistance revealed the existence of a major single dominant gene controlling downy mildew complete resistance in quinoa accession PI614911. Through BSA, this gene was found to be located in chromosome 4, in a region also identified by GWAS. Furthermore, several plant receptors and resistance genes were found to be located into the genomic regions identified by GWAS and are postulated as candidate genes for resistance.ConclusionsUntil now, little was known about the genetic control of downy mildew resistance in quinoa. A previous inheritance study suggested that resistance to this disease was a quantitative polygenic trait and previous GWAS analyses were unable to identify accurate markers for this disease. In our study we demonstrate the existence of, at least, one major gene conferring resistance to this disease, identify the genomic regions involved in the trait and provide plausible candidate genes involved in defense. Therefore, this study significantly increases our knowledge about the genetics of downy mildew resistance and provides relevant information for breeding for this important trait.

CSF N-acylethanolamine acid amidase level and Parkinson's disease risk: A mendelian randomization study

BackgroundNeuroinflammation is involved in the progression of Parkinson's disease (PD), and N-acylethanolamine acid amidase (NAAA) is involved in regulating inflammation by hydrolyzing bioactive lipid mediators called N-acylethanolamines (NAEs). However, the causal relationship between cerebrospinal fluid (CSF) NAAA protein levels and the risk of PD remains unclear. This study aimed to explore the causal effect of CSF NAAA levels on PD risk through Mendelian randomization (MR) analysis. MethodGenome-wide association study (GWAS) summary statistics for CSF NAAA protein quantitative trait loci (pQTL) and GWAS summary statistics for PD were obtained from publicly available databases. Inverse-variance weighted (IVW) was the main causal estimation method for MR analysis. In addition, the maximum likelihood, MR Egger regression, and weighted median were used to supplement the IVW results. Finally, various sensitivity tests were performed to verify the reliability of the MR findings. ResultsIn the initial MR analysis, the IVW showed that CSF NAAA protein levels significantly increased PD risk (odds ratio [OR] = 1.17, 95% confidence interval [CI]: 1.01–1.35, P = 0.031). This finding was further validated in a replicate MR analysis (OR = 1.20, 95% CI: 1.02–1.41, P = 0.027). Sensitivity analysis showed that MR results were stable and not affected by heterogeneity and horizontal pleiotropy. ConclusionThe present MR study supports a causal relationship between elevated CSF NAAA protein levels and increased PD risk.

ICARUS v3, a massively scalable web server for single-cell RNA-seq analysis of millions of cells.

In recent years, improvements in throughput of single-cell RNA-seq have resulted in a significant increase in the number of cells profiled. The generation of single-cell RNA-seq datasets comprising >1 million cells is becoming increasingly common, giving rise to demands for more efficient computational workflows. We present an update to our single-cell RNA-seq analysis web server application, ICARUS (available at https://launch.icarus-scrnaseq.cloud.edu.au) that allows effective analysis of large-scale single-cell RNA-seq datasets. ICARUS v3 utilizes the geometric cell sketching method to subsample cells from the overall dataset for dimensionality reduction and clustering that can be then projected to the large dataset. We then extend this functionality to select a representative subset of cells for downstream data analysis applications including differential expression analysis, gene co-expression network construction, gene regulatory network construction, trajectory analysis, cell-cell communication inference, and cell cluster associations to GWAS traits. We demonstrate analysis of single-cell RNA-seq datasets using ICARUS v3 of 1.3 million cells completed within the hour. ICARUS is available at https://launch.icarus-scrnaseq.cloud.edu.au.

The impact of genetically controlled splicing on exon inclusion and protein structure.

Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.

Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations

BackgroundType 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition the heterogeneity of T2D, in order to stratify patient risk and gain mechanistic insight. We expanded on these approaches by performing colocalization across GWAS traits while assessing the causality and directionality of genetic associations.MethodsWe applied colocalization between T2D and 20 related metabolic traits, across 243 loci, to obtain inferences of shared casual variants. Network-based unsupervised hierarchical clustering was performed on variant-trait associations. Partitioned polygenic risk scores (PRSs) were generated for each cluster using T2D summary statistics and validated in 21,742 individuals with T2D from 3 cohorts. Inferences of directionality and causality were obtained by applying Mendelian randomization Steiger’s Z-test and further validated in a pediatric cohort without diabetes (aged 9–12 years old, n = 3866).ResultsWe identified 146 T2D loci that colocalized with at least one metabolic trait locus. T2D variants within these loci were grouped into 5 clusters. The clusters corresponded to the following pathways: obesity, lipodystrophic insulin resistance, liver and lipid metabolism, hepatic glucose metabolism, and beta-cell dysfunction. We observed heterogeneity in associations between PRSs and metabolic measures across clusters. For instance, the lipodystrophic insulin resistance (Beta − 0.08 SD, 95% CI [− 0.10–0.07], p = 6.50 × 10−32) and beta-cell dysfunction (Beta − 0.10 SD, 95% CI [− 0.12, − 0.08], p = 1.46 × 10−47) PRSs were associated to lower BMI. Mendelian randomization Steiger analysis indicated that increased T2D risk in these pathways was causally associated to lower BMI. However, the obesity PRS was conversely associated with increased BMI (Beta 0.08 SD, 95% CI 0.06–0.10, p = 8.0 × 10−33). Analyses within a pediatric cohort supported this finding. Additionally, the lipodystrophic insulin resistance PRS was associated with a higher odds of chronic kidney disease (OR 1.29, 95% CI 1.02–1.62, p = 0.03).ConclusionsWe successfully partitioned T2D genetic variants into phenotypic pathways using a colocalization first approach. Partitioned PRSs were associated to unique metabolic and clinical outcomes indicating successful partitioning of disease heterogeneity. Our work expands on previous approaches by providing stronger inferences of shared causal variants, causality, and directionality of GWAS variant-trait associations.

Extreme trait GWAS (Et-GWAS): Unraveling rare variants in the 3,000 rice genome.

Identifying high-impact, rare genetic variants associated with specific traits is crucial for crop improvement. The 3,010 rice genome (3K RG) dataset offers a valuable resource for discovering genomic regions with potential applications in crop breeding. We used Extreme Trait GWAS (Et-GWAS), employing bulk pooling and allele frequency measurement to efficiently extract rare variants from the 3K RG. This innovative approach facilitates the detection of associations between genetic variants and target traits, concentrating and quantifying rare alleles. In our study, on grain yield under drought stress, Et-GWAS successfully identified five key genes (OsPP2C11, OsK5.2, OsIRO2, OsPEX1, and OsPWA1) known for enhancing yield under drought. In addition, we examined the overlap of our results with previously reported qDTY-QTLs and observed that OsUCH1 and OsUCH2 genes were located within qDTY2.2 We compared Et-GWAS with conventional GWAS, finding it effectively capturing most candidate genes associated with the target trait. Validation with resistant starch showed similar results. To enhance user-friendliness, we developed a GUI for Et-GWAS; https://et-gwas.shinyapps.io/Et-GWAS/.

Bayesian multivariate genetic analysis improves translational insights

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

QT–GWAS: A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

AbstractAlthough the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind, a large fraction of these metabolites and their biosynthetic pathways remain unknown. Resolving metabolite structures and their biosynthetic pathways is key to gaining biological understanding and to allow metabolic engineering. In order to retrieve novel biosynthetic genes involved in specialized metabolism, we developed a novel untargeted method designated as qualitative trait GWAS (QT–GWAS) that subjects qualitative metabolic traits to a genome-wide association study, while the conventional metabolite GWAS (mGWAS) mainly considers the quantitative variation of metabolites. As a proof of the validity of QT-GWAS, 23 and 15 of the retrieved associations identified in Arabidopsis thaliana by QT-GWAS and mGWAS, respectively, were supported by previous research. Furthermore, seven gene-metabolite associations retrieved by QT–GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme assays. As such, we established that CYTOCHROME P450 706A5 (CYP706A5) is involved in the biosynthesis of chroman derivatives, UDP-GLYCOSYLTRANSFERASE 76C3 (UGT76C3) is able to hexosylate guanine in vitro and in planta, and SULFOTRANSFERASE 202B1 (SULT202B1) catalyzes the sulfation of neolignans in vitro. Collectively, our study demonstrates that the untargeted QT–GWAS method can retrieve valid gene–metabolite associations at the level of enzyme-encoding genes, even new associations that cannot be found by the conventional mGWAS, providing a new approach for dissecting qualitative metabolic traits.

0622 Clustering bipolar disorder risk variants by their effect on sleep and circadian traits

Abstract Introduction Bipolar disorder (BD) is a mood disorder that causes noticeable, sometimes extreme, changes in mood and behaviour. BD affects nearly 50 million individuals worldwide including 2-4% of the US population. Previous studies have reported sleep and circadian disturbances in patients with BD. Genetic studies have identified 63 risk variants for bipolar disorder (BD). Methods To enable new insights into BD disease causing pathways, we clustered BD risk variants by their effect on sleep and circadian rhythm traits. A Bayesian soft-clustering approach was used to determine if BD variants cluster according to effect on eight subjectively and two objectively measured sleep and circadian traits (using sleep and circadian trait GWAS from the UK Biobank in European ancestry, n = 453,000). Results We found evidence of genetic correlation between BD and sleep/circadian traits (p< 0.05). Bipolar risk variants cluster into two clusters based on their association with sleep and circadian traits. The two clusters are: Cluster 1: late chronotype, shorter time in bed (by actigraphy), difficulty waking up, longer sleep duration (self-report), longer periods of daytime sleepiness, longer and frequent naps and increasing insomnia and Cluster 2: early chronotype, longer time in bed (by actigraphy), longer sleep duration (self-report) and less insomnia. In general, cluster 1 appears to be contributing to sleep insufficiency, while cluster 2 appears to be contributing to hypersomnia or more “typical” sleep. Conclusion In conclusion, sleep and circadian trait clusters indicate bipolar genetic risk may function through two distinct mechanisms. Some genetic risk variants appear to be related to sleep insufficiency, while others are related to hypersomnia, suggesting that it may be possible to genetically subtype BD. This finding may allow classification of BD-patients by distinct genetic pathways linking to their sleep-circadian disturbances, potentially offering a way forward toward genetically informed diagnosis, surveillance and management of BD-patients. Support (if any) N/A

Delineation of complex gene expression patterns in single cell RNA-seq data with ICARUS v2.0.

Complex biological traits and disease often involve patterns of gene expression that can be characterised and examined. Here we present ICARUS v2.0, an update to our single cell RNA-seq analysis web server with additional tools to investigate gene networks and understand core patterns of gene regulation in relation to biological traits. ICARUS v2.0 enables gene co-expression analysis with MEGENA, transcription factor regulated network identification with SCENIC, trajectory analysis with Monocle3, and characterisation of cell-cell communication with CellChat. Cell cluster gene expression profiles may be examined against Genome Wide Association Studies with MAGMA to find significant associations with GWAS traits. Additionally, differentially expressed genes may be compared against the Drug-Gene Interaction database (DGIdb 4.0) to facilitate drug discovery. ICARUS v2.0 offers a comprehensive toolbox of the latest single cell RNA-seq analysis methodologies packed into an efficient, user friendly, tutorial style web server application (accessible at https://launch.icarus-scrnaseq.cloud.edu.au/) that enables single cell RNA-seq analysis tailored to the user's dataset.

Skin Colonization with S. aureus Can Lead to Increased NLRP1 Inflammasome Activation in Patients with Atopic Dermatitis

The role of NLRP1 inflammasome activation and subsequent production of IL-1 family cytokines in the development of atopic dermatitis (AD) is not clearly understood. Staphylococcus aureus is known to be associated with increased mRNA levels of IL1 family cytokines in the skin and more severe AD. In this study, the altered expression of IL-1 family cytokines and inflammasome-related genes was confirmed, and a positive relationship between mRNA levels of inflammasome sensor NLRP1 and IL1B or IL18 was determined. Enhanced expression of the NLRP1 and PYCARD proteins and increased caspase-1 activity were detected in the skin of patients with AD. The genetic association of IL18R1 and IL18RAP with AD was confirmed, and the involvement of various immune cell types was predicted using published GWAS and expression quantitative trait loci datasets. In keratinocytes, the inoculation with S.aureus led to the increased secretion of IL-1β and IL-18, whereas small interfering RNA silencing of NLRP1 inhibited the production of these cytokines. Our results suggest that skin colonization with S.aureus may cause the activation of the NLRP1 inflammasome in keratinocytes, which leads to the secretion of IL-1β and IL-18 and thereby may contribute to the pathogenesis of AD, particularly in the presence of genetic variations in the IL-18 pathway.

Brain proteome-wide association study linking-genes in multiple sclerosis pathogenesis.

To identify genes that confer MS risk via the alteration of cis-regulated protein abundance and verify their aberrant expression in human brain. Utilizing a two-stage proteome-wide association study (PWAS) design, MS GWAS data (N= 41,505) was respectively integrated with two distinct human brain proteomes from the dorsolateral prefrontal cortex, including ROSMAP (N=376) in the discovery stage and Banner (N=152) in the confirmation stage. In the following, Bayesian colocalization analysis was conducted for GWAS and protein quantitative trait loci signals to prioritize candidate genes. Differential expression analysis was then used to verify the dysregulation of risk genes in white matter and gray matter for evidence at the transcription level. A total of 51 genes whose protein abundance had association with the MS risk were identified, of which 18 genes overlapped in the discovery and confirmation PWAS. Bayesian colocalization indicated six causal genes with genetic risk variants for the MS risk. The differential expression analysis of SHMT1 (PFDR =4.82 × 10-2 ), FAM120B (PFDR =8.13 × 10-4 ) in white matter and ICA1L (PFDR =3.44 × 10-2 ) in gray matter confirmed the dysregulation at the transcription level. Further investigation of expression found SHMT1 significantly up-regulated in white matter lesion, and FAM120B up-regulated in both white matter lesion and normal appearing white matter. ICA1L was down-regulated in both gray matter lesion and normal appearing gray matter. Dysregulation of SHMT1, FAM120B and ICA1L may confer MS risk. Our findings shed new light on the pathogenesis of MS and prioritized promising targets for future therapy research.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment

BackgroundMultiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene–gene closeness.ResultsWe identified 163 MS-associated prioritized risk genes (MS-PRGenes) through the Bayesian framework. We replicated 35 MS-PRGenes through two-sample Mendelian randomization (2SMR) approach by integrating data from GWAS and Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) of 19 tissues. We demonstrated that MS-PRGenes had more substantial deleterious effects and disease risk. Moreover, single-cell enrichment analysis indicated MS-PRGenes were more enriched in activated macrophages and microglia macrophages than non-activated ones in control samples. Biological and drug enrichment analyses highlighted inflammatory signaling pathways.ConclusionsIn summary, we predicted and validated a high-confidence MS risk gene set from diverse genomic, epigenomic, eQTL, single-cell, and drug data. The MS-PRGenes could further serve as a benchmark of MS GWAS risk genes for future validation or genetic studies.

Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

BackgroundIdentifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ.MethodsWe systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI.ResultsBoth Sherlock (P = 3. 38 × 10−6) and SMR (P = 1. 90 × 10−8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus.ConclusionsWe confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.

Renal Biomarker Evaluation using Data Integration in the Age of Precision Medicine

Decades of work have given nephrology a handful of strong biomarkers of acute kidney injury (AKI), including clinical serum creatinine, blood urea nitrogen levels, and urine proteinuria. To refine the location of AKI to different regions of the nephron, proteins such as Albumin, NGAL, and KIM‐1 have been critical. Yet, many of our classical nephrology biomarkers are rarely evaluated considering broad patient pathologies and environmental factors such as viral infection. Millions of data points can inform on AKI in the data age. Thus, we have performed a systematic data analysis of ~10,000 blood sample precision transcriptomes for broad pathologies, ~200 RNA‐Seq samples for human AKI, the Comparative Toxicogenomics Database, Human Protein Atlas, GTEx, and our custom cell culture models of kidney response followed by advanced sequencing including direct RNA‐Seq. This data integration work elucidated biomarkers such as KIM‐1 can be elevated in blood due to viral infections and environmental contributors. In some patients, the elevation of KIM‐1 in the urine occurs due to blood activation and urine proteinuria instead of tubule secretion. However, refining KIM‐1 data with blood RNA‐Seq allows for identifying isoform switches that correlate to AKI and tubule damage.Further work in our data integration shows that RNA specific to tubule cells or podocytes can be observed in patient blood during AKI, suggesting a promising observable measure of detailed cell types damaged that may contribute to AKI. Finally, integrating our human GWAS and expression quantitative trait loci (eQTLs) for kidney traits elucidates the ability to map response eQTLs and mechanisms from easier to obtain blood samples that can be critical in defining AKI and chronic kidney diseases. Thus, continued revaluation of kidney biomarkers in the big data age can bring nephrology into the forefront of precision medicine.

Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility

Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.

Molecular rhythm alterations in prefrontal cortex and nucleus accumbens associated with opioid use disorder

Severe and persistent disruptions to sleep and circadian rhythms are common in people with opioid use disorder (OUD). Preclinical evidence suggests altered molecular rhythms in the brain modulate opioid reward and relapse. However, whether molecular rhythms are disrupted in the brains of people with OUD remained an open question, critical to understanding the role of circadian rhythms in opioid addiction. Using subjects’ times of death as a marker of time of day, we investigated transcriptional rhythms in the brains of subjects with OUD compared to unaffected comparison subjects. We discovered rhythmic transcripts in both the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), key brain areas involved in OUD, that were largely distinct between OUD and unaffected subjects. Fewer rhythmic transcripts were identified in DLPFC of subjects with OUD compared to unaffected subjects, whereas in the NAc, nearly double the number of rhythmic transcripts was identified in subjects with OUD. In NAc of subjects with OUD, rhythmic transcripts peaked either in the evening or near sunrise, and were associated with an opioid, dopamine, and GABAergic neurotransmission. Associations with altered neurotransmission in NAc were further supported by co-expression network analysis which identified OUD-specific modules enriched for transcripts involved in dopamine, GABA, and glutamatergic synaptic functions. Additionally, rhythmic transcripts in DLPFC and NAc of subjects with OUD were enriched for genomic loci associated with sleep-related GWAS traits, including sleep duration and insomnia. Collectively, our findings connect transcriptional rhythm changes in opioidergic, dopaminergic, GABAergic signaling in the human brain to sleep-related traits in opioid addiction.

EP327: A quantitative trait GWAS on lens thickness identifies risk loci on PTPRM in the narrow-angle individuals anatomically susceptible to PACG

Quantitative traits (QTs) are highly heritable. Estimation of QT is important for the risk factor characterization that would help in localising the genetic susceptibility factors. Primary angle closure glaucoma (PACG) is one of the leading causes of blindness worldwide, especially in the east and southeast Asia. A couple of risk factors including narrow-angle between iridotrabecular contacts and increased lens thickness leading to shallow anterior chambers are present in the anatomically predisposed eyes, which could raise intraocular pressure followed by optic neuropathy and subsequent blindness.

Genetically Determined Obesity and Adipose Distribution Impact Human Blood Trait Variation across Cell Lineages

IntroductionGenome wide association studies (GWAS) have catalogued thousands of loci that influence blood traits, but genetic mechanisms and impacted cell types are often unknown. While some regulatory factors are blood cell-autonomous, other extrinsic regulatory factors respond to systemic physiology. Epidemiologic studies have correlated generalized obesity (increased body mass index, BMI) and high cholesterol with anemia, but cell types and mechanisms that mediate these effects are incompletely understood, and these studies do not provide evidence for causality. Thus, we wanted to use genetic effects of BMI and cholesterol on hemoglobin level (HGB) and other blood traits to infer causal relationships, identify relevant cell types, and propose genetic mechanisms.MethodsMendelian randomization (MR), akin to a “genetic randomized controlled trial”, uses variants linked to an exposure trait to estimate causal effects of that exposure on a given outcome. 1 Random variant allele allocation at meiosis enables this approach to address confounding and reverse causality that can otherwise preclude causal inference from epidemiologic and cohort studies. Further, effects of multiple factors can be parsed using multivariable MR and causal mediation analyses to help explain genetic associations. 2 Our study used gender- and age-adjusted GWAS summary statistics from European individuals.ResultsWe hypothesized that increased genetically determined BMI would decrease HGB. Using a MR framework, we found that a 5 kg/m 2 (1 standard deviation unit) increase in BMI decreased hemoglobin by 0.06±0.01 g/dL (p=1x10 -5, Fig). Increased BMI also decreased erythrocyte count, and unexpectedly also decreased platelet and white blood cell counts (all with p<5x10 -3). These data showed that obesity-related mechanisms extended beyond the erythroid lineage, perhaps impacting multipotent hematopoietic progenitor cells (HPCs).Similar to BMI, a 1 SD unit increase in total cholesterol decreased HGB (0.10±0.03 g/dL, p=2x10 -3, Fig). However, genetic effects of cholesterol were restricted to erythroid traits (HGB and hematocrit). Multivariable and mediation analyses confirmed that effects of BMI and cholesterol functioned through distinct genetic mechanisms.We speculated that multilineage effects from BMI could reflect a genetic predisposition to accumulate bone marrow adipose tissue, which can impact HPCs and hematopoiesis. 3 A tendency for ‘central’ adiposity increases one's waist-hip ratio (WHR), and increases cardiovascular disease risk concordant with BMI. Unexpectedly, we found that increased WHR, in contrast to BMI, increased HGB (0.08±0.02 g/dL, p=9x10 -6) as well as erythrocyte, platelet, and white blood cell counts (all with p<4x10 -3, Fig). In multivariable experiments, the effects of WHR were exacerbated after accounting for BMI at the individual or population level. Thus, obesity impacts blood traits through genetically determined adipose distribution.ConclusionsOur results confirm that BMI and cholesterol negatively impact HGB at a genetic level, consistent with clinical observations. The unexpected multilineage effects of genetically determined BMI most likely reflects a tendency to accumulate bone marrow adipose tissue, which in turn impacts HPCs and downstream blood cell production. Our findings suggest that adjustment for BMI and adiposity traits may be considered in blood trait GWAS analyses and illuminate opportunities to functionally dissect related genes and molecular pathways.

Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants.

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.