The ubiquity of pleiotropy in human disease

A multi-trait GWAS identifies novel genes influencing albuminuria

BACKGROUND

Albuminuria is common and is associated with increased risks of end-stage kidney disease and cardiovascular diseases, yet its underlying mechanism remains obscure. Previous genome-wide association studies (GWAS) for albuminuria did not consider gene pleiotropy and primarily focused on European ancestry populations. This study adopted a multi-trait analysis of GWAS (MTAG) approach to jointly analyze two vital kidney traits, estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) to identify and prioritize the genes associated with UACR.

METHODS

Data from the Taiwan Biobank from 2012 to 2023 were analyzed. GWAS of UACR and eGFR were performed separately and the summary statistics from these GWAS were jointly analyzed using MTAG. The polygenic risk scores (PRS) of UACR were constructed for validation. The UACR-associated loci were further fine-mapped and prioritized based on their deleteriousness, eQTL associations and relatedness to Mendelian kidney diseases.

RESULTS

MTAG analysis of the UACR revealed 15 genetic loci, including 12 novel loci. The PRS for UACR was significantly associated with urinary albumin level (P < .001) and microalbuminuria (P = .001-.045). A list of priority genes was generated. Twelve genes with high priority included the albumin endocytic receptor gene LRP2 and ciliary gene IFT172.

CONCLUSIONS

The findings of this multi-trait GWAS suggest that primary cilia play a role in sensing mechanical stimuli, leading to albumin endocytosis. The priority list of genes warrants further translational investigation to reduce albuminuria.

Association between telomere length and idiopathic normal pressure hydrocephalus: a Mendelian randomization study

Objective

Idiopathic normal pressure hydrocephalus (iNPH) is highly prevalent among elderly individuals, and there is a strong correlation between telomere length and biological aging. However, there is limited evidence to elucidate the relationship between telomere length and iNPH. This study aimed to investigate the associations between telomere length and iNPH using the Mendelian randomization (MR) method.

Methods

The genetic variants of telomere length were obtained from 472,174 UK Biobank individuals. Summary level data of iNPH were acquired from 218,365 individuals of the FinnGen consortium. Five MR estimation methods, including inverse-variance weighting (IVW), MR-Egger regression, weighted median, weighted mode and simple mode, were used for causal inference. Comprehensive sensitivity analyses were conducted to test the robustness of the results. In addition, multivariable MR was further implemented to identify potential mechanisms in the causal pathway from telomere length to iNPH.

Results

Genetically determined longer telomere length was significantly associated with decreased risk of iNPH (OR = 0.44, 95% CI 0.24-0.80; p = 0.008). No evident heterogeneity (Cochran Q = 138.11, p = 0.386) and pleiotropy (MR Egger intercept = 0.01, p = 0.514) were observed in the sensitivity analysis. In addition, multivariable MR indicated that the observed association was attenuated after adjustment for several vascular risk factors, including essential hypertension (IVW OR = 0.55, 95% CI 0.30-1.03; p = 0.061), type 2 diabetes (IVW OR = 0.71, 95% CI 0.09-5.39; p = 0.740) and coronary artery disease (IVW OR = 0.58, 95% CI 0.31-1.07; p = 0.082).

Conclusion

Our MR study revealed a strong negative correlation of telomere length with iNPH. The causal relationship might be driven by several vascular risk factors.

Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics

Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant 'local' genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson's disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

Pleiotropic expression quantitative trait loci are enriched in enhancers and transcription factor binding sites and impact more genes

Integrating expression quantitative trait loci (eQTL) data with genome-wide association studies (GWAS) enables the discovery of pleiotropic gene regulatory variants that influence a wide range of traits and disease susceptibilities. However, a comprehensive understanding of the distribution of pleiotropic QTLs across the genome and their phenotypic associations remain limited. In this study, we systematically annotated genetic variants associated with both trait variation and gene expression changes, focusing specifically on the unique characteristics of pleiotropic eQTLs. By integrating data from 127 eQTL studies and 417 traits from the IEU Open GWAS Project, we identified 476 pleiotropic eQTL variants affecting two or more distinct traits. Our analysis highlighted 5345 eQTL candidates potentially linked to gene expression changes across 293 GWAS traits. Notably, the 476 pleiotropic eQTLs associated with multiple trait categories were localized within a cumulative 2.5 Mbp genomic region. These pleiotropic eQTLs were enriched in enhancer regions and CTCF loops, influencing a larger number of genes in closer genomic proximity. Our findings reveal that pleiotropic eQTLs are concentrated within a small fraction of the genome and exhibit distinct molecular features. Colocalization results are accessible through an interactive web application and UCSC genome browser tracks at https://gwas2eqtl.tagc.univ-amu.fr/gwas2eqtl, facilitating the exploration of pleiotropic eQTLs and their roles in gene regulation and disease susceptibility.

Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making

Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.

Convergent genetic adaptation of Escherichia coli in minimal media leads to pleiotropic divergence

Adaptation in an environment can either be beneficial, neutral or disadvantageous in another. To test the genetic basis of pleiotropic behaviour, we evolved six lines of E. coli independently in environments where glucose and galactose were the sole carbon sources, for 300 generations. All six lines in each environment exhibit convergent adaptation in the environment in which they were evolved. However, pleiotropic behaviour was observed in several environmental contexts, including other carbon environments. Genome sequencing reveals that mutations in global regulators rpoB and rpoC cause this pleiotropy. We report three new alleles of the rpoB gene, and one new allele of the rpoC gene. The novel rpoB alleles confer resistance to Rifampicin, and alter motility. Our results show how single nucleotide changes in the process of adaptation in minimal media can lead to wide-scale pleiotropy, resulting in changes in traits that are not under direct selection.

Patterns and evolutionary consequences of pleiotropy

Pleiotropy refers to the phenomenon of one gene or one mutation affecting multiple phenotypic traits. While the concept of pleiotropy is as old as Mendelian genetics, functional genomics has finally allowed the first glimpses of the extent of pleiotropy for a large fraction of genes in a genome. After describing conceptual and operational difficulties in quantifying pleiotropy and the pros and cons of various methods for measuring pleiotropy, I review empirical data on pleiotropy, which generally show an L-shaped distribution of the degree of pleiotropy (i.e., the number of traits affected) with most genes having low pleiotropy. I then review the current understanding of the molecular basis of pleiotropy. The rest of the review discusses evolutionary consequences of pleiotropy, focusing on advances in topics including the cost of complexity, regulatory vs. coding evolution, environmental pleiotropy and adaptation, evolution of ageing and other seemingly harmful traits, and evolutionary resolution of pleiotropy.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

POIROT: a powerful test for parent-of-origin effects in unrelated samples leveraging multiple phenotypes

MOTIVATION

There is widespread interest in identifying genetic variants that exhibit parent-of-origin effects (POEs) wherein the effect of an allele on phenotype expression depends on its parental origin. POEs can arise from different phenomena including genomic imprinting and have been documented for many complex traits. Traditional tests for POEs require family data to determine parental origins of transmitted alleles. As most genome-wide association studies (GWAS) sample unrelated individuals (where allelic parental origin is unknown), the study of POEs in such datasets requires sophisticated statistical methods that exploit genetic patterns we anticipate observing when POEs exist. We propose a method to improve discovery of POE variants in large-scale GWAS samples that leverages potential pleiotropy among multiple correlated traits often collected in such studies. Our method compares the phenotypic covariance matrix of heterozygotes to homozygotes based on a Robust Omnibus Test. We refer to our method as the Parent of Origin Inference using Robust Omnibus Test (POIROT) of multiple quantitative traits.

RESULTS

Through simulation studies, we compared POIROT to a competing univariate variance-based method which considers separate analysis of each phenotype. We observed POIROT to be well-calibrated with improved power to detect POEs compared to univariate methods. POIROT is robust to non-normality of phenotypes and can adjust for population stratification and other confounders. Finally, we applied POIROT to GWAS data from the UK Biobank using BMI and two cholesterol phenotypes. We identified 338 genome-wide significant loci for follow-up investigation.

AVAILABILITY AND IMPLEMENTATION

The code for this method is available at https://github.com/staylorhead/POIROT-POE.

An adaptive test based on principal components for detecting multiple phenotype associations using GWAS summary data

Extensive evidence from genome-wide association studies (GWAS) has shown that jointly analyzing multiple phenotypes can improve the power of the association test compared to the traditional single variant versus single trait approach. Here we propose an adaptive test based on principal components (ATPC) that is powerful and efficient for discovering the association between a single variant and multiple traits. Our method only needs GWAS summary statistics that are often available. We first estimate the trait correlation matrix by LD score regression. Then, based on the correlation matrix, we construct a series of test statistics that contain different numbers of principal components. The ultimate test statistic combines the P values of these principal component-based statistics by using the aggregated Cauchy association test. The analytical P-value of the test statistic can be computed quickly without the permutation process, which is the notable feature of our proposed method. The extensive simulation studies demonstrate that ATPC can control the type I error rates and have powerful and robust performance compared to several existing tests in a wide range of simulation settings. The analysis of the lipids GWAS summary data from the Global Lipids Genetics Consortium shows that ATPC identifies 230 new SNPs that are missed by the original single trait association analysis. By searching the GWAS Catalog, some SNPs and mapped genes identified by ATPC are reported to be associated with lipid traits. Through further analysis for GWAS results, we also find some Gene Ontology terms and biological pathways related to lipids.

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.

Genetic analyses implicate complex links between adult testosterone levels and health and disease

BACKGROUND

Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes.

METHODS

Leveraging genetic and health registry data from the UK Biobank and FinnGen (total N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality.

RESULTS

We show testosterone and SHBG levels are intricately tied to metabolic health, but report lack of causality behind most associations, including type 2 diabetes (T2D). Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes.

CONCLUSIONS

Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences.

Hemizygosity can reveal variant pathogenicity on the X-chromosome

Pathogenic variants on the X-chromosome can have more severe consequences for hemizygous males, while heterozygote females can avoid severe consequences due to diploidy and the capacity for nonrandom expression. Thus, when an allele is more common in females this could indicate that it increases the probability of early death in the male hemizygous state, which can be considered a measure of pathogenicity. Importantly, large-scale genomic data now makes it possible to compare allele proportions between the sexes. To discover pathogenic variants on the X-chromosome, we analyzed exome data from 125,748 ancestrally diverse participants in the Genome Aggregation Database (gnomAD). After filtering out duplicates and extremely rare variants, 44,606 of the original 348,221 remained for analysis. We divided the proportion of variant alleles in females by the proportion in males for all variant sites, and then placed each variant into one of three a priori categories: (1) Reference (Primarily synonymous and intronic), (2) Unlikely-to-be-tolerated (Primarily missense), and (3) Least-likely-to-be-tolerated (Primarily frameshift). To assess the impact of ploidy, we compared the distribution of these ratios between pseudoautosomal and non-pseudoautosomal regions. In the non-pseudoautosomal regions, mean female-to-male ratios were lowest among Reference (2.40), greater for Unlikely-to-be-tolerated (2.77) and highest for Least-likely-to-be-tolerated (3.28) variants. Corresponding ratios were lower in the pseudoautosomal regions (1.52, 1.57, and 1.68, respectively), with the most extreme ratio being just below 11. Because pathogenic effects in the pseudoautosomal regions should not drive ratio increases, this maximum ratio provides an upper bound for baseline noise. In the non-pseudoautosomal regions, 319 variants had a ratio over 11. In sum, we identified a measure with a dataset specific threshold for identifying pathogenicity in non-pseudoautosomal X-chromosome variants: the female-to-male allele proportion ratio.

Collective genomic segments with differential pleiotropic patterns between cognitive dimensions and psychopathology

Cognitive deficits are known to be related to most forms of psychopathology. Here, we perform local genetic correlation analysis as a means of identifying independent segments of the genome that show biologically interpretable pleiotropic associations between cognitive dimensions and psychopathology. We identify collective segments of the genome, which we call "meta-loci", showing differential pleiotropic patterns for psychopathology relative to either cognitive task performance (CTP) or performance on a non-cognitive factor (NCF) derived from educational attainment. We observe that neurodevelopmental gene sets expressed during the prenatal-early childhood period predominate in CTP-relevant meta-loci, while post-natal gene sets are more involved in NCF-relevant meta-loci. Further, we demonstrate that neurodevelopmental gene sets are dissociable across CTP meta-loci with respect to their spatial distribution across the brain. Additionally, we find that GABA-ergic, cholinergic, and glutamatergic genes drive pleiotropic relationships within dissociable meta-loci.

Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.

Sex differences in the association of fasting glucose with HbA1c, and their consequences for mortality: A Mendelian randomization study

BACKGROUND

Hemoglobin A1c (HbA1c) is used for diabetes diagnosis and management. HbA1c also represents iron-related erythrocyte properties which differ by sex. We investigated erythrocyte properties on HbA1c and glucose, and whether corresponding consequences for mortality differed by sex.

METHODS

In this two-sample Mendelian randomization study using the largest publicly available European descent summary statistics, we assessed sex-specific associations of iron (n=163,511) and hemoglobin (188,076 women/162,398 men) with HbA1c (185,022 women/159,160 men) and fasting glucose (73,089 women/67,506 men), of fasting glucose with HbA1c and diabetes (cases=6,589 women/10,686 men, controls=187,137 women/155,780 men), and of fasting glucose (n=140,595), HbA1c (n=146,806) and liability to diabetes (74,124 cases/824,006 controls) with parental attained age (412,937 mothers/415,311 fathers).

FINDINGS

Iron and hemoglobin were inversely associated with HbA1c but not fasting glucose. Fasting glucose was more strongly associated with HbA1c and diabetes in women (1.65 standard deviation (SD) per mmol/L [95% confidence interval 1.58, 1.72]; odds ratio (OR) 7.36 per mmol/L [4.12, 10.98]) than men (0.89 [0.81, 0.98]; OR 2.79 [1.96, 4.98]). The inverse associations of HbA1c and liability to diabetes with lifespan were possibly stronger in men (-1.80 years per percentage [-2.77, -0.42]; -0.93 years per logOR [-1.23, -0.59]) than women (-0.80 [-2.69, 0.66]; -0.44 [-0.62, -0.26]).

INTERPRETATION

HbA1c underestimates fasting glucose in men compared with women, possibly due to erythrocyte properties. Whether HbA1c and liability to diabetes reduce lifespan more in men than women because diagnostic and management criteria involving HbA1c mean that glycemia in men is under-treated compared to women needs urgent investigation.

FUNDING

None.

eQTLs as causal instruments for the reconstruction of hormone linked gene networks

Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.

Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

This article has an associated First Person interview with the first author of the paper.

Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.

Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders

Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.

Systems genetics in the rat HXB/BXH family identifies Tti2 as a pleiotropic quantitative trait gene for adult hippocampal neurogenesis and serum glucose

Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetics methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular diseases and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival on Chromosome 16: 62.1-66.3 Mb. We subsequently fine-mapped the key phenotype to a locus that includes the Telo2-interacting protein 2 gene (Tti2)-a chaperone that modulates the activity and stability of PIKK kinases. To verify the hypothesis that differences in neurogenesis and glucose levels are caused by a polymorphism in Tti2, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

An integrated framework for local genetic correlation analysis

Genetic correlation (r_g) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, r_g is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the r_g is confined to particular genomic regions or in opposing directions at different loci. Current tools for local r_g analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local r_g analysis that, in addition to testing the standard bivariate local r_gs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local r_gs across the genome, which is often masked by the global r_g patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.

Mendelian Randomization: A Review of Methods for the Prevention, Assessment, and Discussion of Pleiotropy in Studies Using the Fat Mass and Obesity-Associated Gene as an Instrument for Adiposity

Pleiotropy assessment is critical for the validity of Mendelian randomization (MR) analyses, and its management remains a challenging task for researchers. This review examines how the authors of MR studies address bias due to pleiotropy in practice. We reviewed Pubmed, Medline, Embase and Web of Science for MR studies published before 21 May 2020 that used at least one single-nucleotide polymorphism (SNP) in the fat mass and obesity-associated (FTO) gene as instrumental variable (IV) for body mass index, irrespective of the outcome. We reviewed: 1) the approaches used to prevent pleiotropy, 2) the methods cited to detect or control the independence or the exclusion restriction assumption highlighting whether pleiotropy assessment was explicitly stated to justify the use of these methods, and 3) the discussion of findings related to pleiotropy. We included 128 studies, of which thirty-three reported one approach to prevent pleiotropy, such as the use of multiple (independent) SNPs combined in a genetic risk score as IVs. One hundred and twenty studies cited at least one method to detect or account for pleiotropy, including robust and other IV estimation methods (n = 70), methods for detection of heterogeneity between estimated causal effects across IVs (n = 72), methods to detect or account associations between IV and outcome outside thought the exposure (n = 85), and other methods (n = 5). Twenty-one studies suspected IV invalidity, of which 16 explicitly referred to pleiotropy, and six incriminating FTO SNPs. Most reviewed MR studies have cited methods to prevent or to detect or control bias due to pleiotropy. These methods are heterogeneous, their triangulation should increase the reliability of causal inference.

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies

Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation, and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multitrait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between nonpleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

Two-Sample Multivariable Mendelian Randomization Analysis Using R

Mendelian randomization is a framework that uses measured variation in genes for assessing and estimating the causal effect of an exposure on an outcome. Multivariable Mendelian randomization is an extension that can assess the causal effect of multiple exposures on an outcome, and can be advantageous when considering a set (>1) of potentially correlated candidate risk factors in evaluating the causal effect of each on a health outcome, accounting for measured pleiotropy. This can be seen, for example, in determining the causal effects of lipids and cholesterol on type 2 diabetes risk, where the correlated risk factors share genetic predictors. Similar to univariate Mendelian randomization, multivariable Mendelian randomization can be conducted using two-sample summary-level data where the gene-exposure and gene-outcome associations are derived from separate samples from the same underlying population. Here, we present a protocol for conducting a two-sample multivariable Mendelian randomization study using the 'MVMR' package in R and summary-level genetic data. We also provide a protocol for searching and obtaining instruments using available data sources in the 'MRInstruments' R package. Finally, we provide general guidelines and discuss the utility of performing a multivariable Mendelian randomization analysis for simultaneously assessing causality of multiple exposures. © 2021 Wiley Periodicals LLC. Basic Protocol: Performing a two-sample multivariable Mendelian randomization analysis using the 'MVMR' package in R and summarized genetic data Support Protocol 1: Installing the 'MVMR' R package Support Protocol 2: Obtaining instruments from the 'MRInstruments' R package.

Biologically grounded scientific methods: The challenges ahead for combating epidemics

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

Partitioned glioma heritability shows subtype-specific enrichment in immune cells

BACKGROUND

Epidemiological studies of adult glioma have identified genetic syndromes and 25 heritable risk loci that modify individual risk for glioma, as well increased risk in association with exposure to ionizing radiation and decreased risk in association with allergies. In this analysis, we assess whether there is a shared genome-wide genetic architecture between glioma and atopic/autoimmune diseases.

METHODS

Using summary statistics from a glioma genome-wide association studies (GWAS) meta-analysis, we identified significant enrichment for risk variants associated with gene expression changes in immune cell populations. We also estimated genetic correlations between glioma and autoimmune, atopic, and hematologic traits using linkage disequilibrium score regression (LDSC), which leverages genome-wide single-nucleotide polymorphism (SNP) associations and patterns of linkage disequilibrium.

RESULTS

Nominally significant negative correlations were observed for glioblastoma (GB) and primary biliary cirrhosis (rg = -0.26, P = .0228), and for non-GB gliomas and celiac disease (rg = -0.32, P = .0109). Our analyses implicate dendritic cells (GB pHM = 0.0306 and non-GB pHM = 0.0186) in mediating both GB and non-GB genetic predisposition, with GB-specific associations identified in natural killer (NK) cells (pHM = 0.0201) and stem cells (pHM = 0.0265).

CONCLUSIONS

This analysis identifies putative new associations between glioma and autoimmune conditions with genomic architecture that is inversely correlated with that of glioma and that T cells, NK cells, and myeloid cells are involved in mediating glioma predisposition. This provides further evidence that increased activation of the acquired immune system may modify individual susceptibility to glioma.

Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Haplotype Shuffling and Dimorphic Transposable Elements in the Human Extended Major Histocompatibility Complex Class II Region

The major histocompatibility complex (MHC) on chromosome 6p21 is one of the most single-nucleotide polymorphism (SNP)-dense regions of the human genome and a prime model for the study and understanding of conserved sequence polymorphisms and structural diversity of ancestral haplotypes/conserved extended haplotypes. This study aimed to follow up on a previous analysis of the MHC class I region by using the same set of 95 MHC haplotype sequences downloaded from a publicly available BioProject database at the National Center for Biotechnology Information to identify and characterize the polymorphic human leukocyte antigen (HLA)-class II genes, the MTCO3P1 pseudogene alleles, the indels of transposable elements as haplotypic lineage markers, and SNP-density crossover (XO) loci at haplotype junctions in DNA sequence alignments of different haplotypes across the extended class II region (∼1 Mb) from the telomeric PRRT1 gene in class III to the COL11A2 gene at the centromeric end of class II. We identified 42 haplotypic indels (20 Alu, 7 SVA, 13 LTR or MERs, and 2 indels composed of a mosaic of different transposable elements) linked to particular HLA-class II alleles. Comparative sequence analyses of 136 haplotype pairs revealed 98 unique XO sites between SNP-poor and SNP-rich genomic segments with considerable haplotype shuffling located in the proximity of putative recombination hotspots. The majority of XO sites occurred across various regions including in the vicinity of MTCO3P1 between HLA-DQB1 and HLA-DQB3, between HLA-DQB2 and HLA-DOB, between DOB and TAP2, and between HLA-DOA and HLA-DPA1, where most XOs were within a HERVK22 sequence. We also determined the genomic positions of the PRDM9-recombination suppression sequence motif ATCCATG/CATGGAT and the PRDM9 recombination activation partial binding motif CCTCCCCT/AGGGGAG in the class II region of the human reference genome (NC_ 000006) relative to published meiotic recombination positions. Both the recombination and anti-recombination PRDM9 binding motifs were widely distributed throughout the class II genomic regions with 50% or more found within repeat elements; the anti-recombination motifs were found mostly in L1 fragmented repeats. This study shows substantial haplotype shuffling between different polymorphic blocks and confirms the presence of numerous putative ancestral recombination sites across the class II region between various HLA class II genes.

Shared genetic architecture underlying sleep and weight in children

Meta-analyses suggest shorter sleep as a risk factor for obesity in children. The prevailing hypothesis is that shorter sleep causes obesity by impacting homeostatic processes. Sleep duration and adiposity are both heritable, and the association may reflect shared genetic aetiology. We examined the association between a body mass index (BMI) genetic risk score (GRS) and objectively-measured total sleep time (TST) in a cohort of Norwegian children (enrolled at age four in 2007-2008) using cross-sectional data at age six. The analytical sample included 452 six-year old children with complete genotype and phenotype data. The outcome was actigraphic total sleep time (TST) measured at age six years. Genetic risk of obesity was inferred using a 32-single nucleotide polymorphism (SNP) weighted GRS of BMI. Covariates were BMI-Standard deviation scores (SDS) (which takes into account age and sex) and, in a sensitivity analysis socioeconomic status. Analyses consisted of Pearson's correlations and linear regressions. In our sample, 54% of participants were male; mean (SD) TST, age and BMI were 9.6 (0.8) hours, 6.0 (0.2) years and 15.3 (1.2) kg/m², respectively. BMI and TST were not correlated, r = -0.003, p = 0.946. However, the BMI GRS was associated with TST after adjusting for BMI-SDS, standardised β = -0.11; 95% confidence interval (CI) = -0.22, -0.01. To our knowledge, this is the first study to establish a relationship between genetic risk of obesity and objective sleep duration in children. Findings suggest some shared genetic aetiology underlying these traits. Future research could identify the common biological pathways through which common genes predispose to both shorter sleep and increased risk of obesity.

Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity

COVID-19 presents with a wide range of severity, from asymptomatic in some individuals to fatal in others. Based on a study of 1,051,032 23andMe research participants, we report genetic and nongenetic associations with testing positive for SARS-CoV-2, respiratory symptoms and hospitalization. Using trans-ancestry genome-wide association studies, we identified a strong association between blood type and COVID-19 diagnosis, as well as a gene-rich locus on chromosome 3p21.31 that is more strongly associated with outcome severity. Hospitalization risk factors include advancing age, male sex, obesity, lower socioeconomic status, non-European ancestry and preexisting cardiometabolic conditions. While non-European ancestry was a significant risk factor for hospitalization after adjusting for sociodemographics and preexisting health conditions, we did not find evidence that these two primary genetic associations explain risk differences between populations for severe COVID-19 outcomes.

Understanding the molecular mechanisms and role of autophagy in obesity

Vital for growth, proliferation, subsistence, and thermogenesis, autophagy is the biological cascade, which confers defence against aging and various pathologies. Current research has demonstrated de novo activity of autophagy in stimulation of biological events. There exists a significant association between autophagy activation and obesity, encompassing expansion of adipocytes which facilitates β cell activity. The main objective of the manuscript is to enumerate intrinsic role of autophagy in obesity and associated complications. The peer review articles published till date were searched using medical databases like PubMed and MEDLINE for research, primarily in English language. Obesity is characterized by adipocytic hypertrophy and hyperplasia, which leads to imbalance of lipid absorption, free fatty acid release, and mitochondrial activity. Detailed evaluation of obesity progression is necessary for its treatment and related comorbidities. Data collected in regard to etiological sustaining of obesity, has revealed hypothesized energy misbalance and neuro-humoral dysfunction, which is stimulated by autophagy. Autophagy regulates chief salvaging events for protein clustering, excessive triglycerides, and impaired mitochondria which is accompanied by oxidative and genotoxic stress in mammals. Autophagy is a homeostatic event, which regulates biological process by eliminating lethal cells and reprocessing physiological constituents, comprising of proteins and fat. Unquestionably, autophagy impairment is involved in metabolic syndromes, like obesity. According to an individual's metabolic outline, autophagy activation is essential for metabolism and activity of the adipose tissue and to retard metabolic syndrome i.e. obesity. The manuscript summarizes the perception of current knowledge on autophagy stimulation and its effect on the obesity.

The SNP rs7865618 of 9p21.3 locus emerges as the most promising marker of coronary artery disease in the southern Indian population

Development of coronary artery disease (CAD) is primarily due to the process of atherosclerosis, however the prognosis of CAD depends on pleiotropic effects of the genes located at 9p21.3 region. Genome wide association studies revealed association of variants in this region with CAD pathology. However, specific marker in predicting CAD development or progression is not yet identified. In the present study, 35 SNPs at 9p21.3 region, located in the cyclin dependent kinase inhibitor (CDKN2A/CDKN2B) genes, were genotyped among 350 CAD cases and 480 controls from the southern Indian population of Hyderabad using fluidigm nanofluidic SNP genotyping system and the data were analyzed using PLINK and R softwares. Of the 35 SNPs analysed, only one SNP, rs7865618, was found to be highly significantly associated with CAD, even after correction for multiple testing (p = 0.008). The AG and GG genotypes of this SNP conferred 3.08 and 1.93 folds increased risk for CAD respectively. In particular, this SNP was significantly associated with severe anatomic (triple vessel disease p = 0.023) and phenotypic (acute coronary syndrome p = 0.007) categories of CAD. Pair wise SNP interaction analysis between the SNPs of 9p21.3 and 11q23.3 regions revealed significantly increased risk of three SNPs of 11q23.3 region that were not associated individually, in conjunction with rs7865618 of 9p21.3.

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.

Genetic stratification of depression by neuroticism: revisiting a diagnostic tradition

BACKGROUND

Major depressive disorder and neuroticism (Neu) share a large genetic basis. We sought to determine whether this shared basis could be decomposed to identify genetic factors that are specific to depression.

METHODS

We analysed summary statistics from genome-wide association studies (GWAS) of depression (from the Psychiatric Genomics Consortium, 23andMe and UK Biobank) and compared them with GWAS of Neu (from UK Biobank). First, we used a pairwise GWAS analysis to classify variants as associated with only depression, with only Neu or with both. Second, we estimated partial genetic correlations to test whether the depression's genetic link with other phenotypes was explained by shared overlap with Neu.

RESULTS

We found evidence that most genomic regions (25/37) associated with depression are likely to be shared with Neu. The overlapping common genetic variance of depression and Neu was genetically correlated primarily with psychiatric disorders. We found that the genetic contributions to depression, that were not shared with Neu, were positively correlated with metabolic phenotypes and cardiovascular disease, and negatively correlated with the personality trait conscientiousness. After removing shared genetic overlap with Neu, depression still had a specific association with schizophrenia, bipolar disorder, coronary artery disease and age of first birth. Independent of depression, Neu had specific genetic correlates in ulcerative colitis, pubertal growth, anorexia and education.

CONCLUSION

Our findings demonstrate that, while genetic risk factors for depression are largely shared with Neu, there are also non-Neu-related features of depression that may be useful for further patient or phenotypic stratification.

Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders

Current genetic studies of monogenic and complex bone diseases have broadened our understanding of disease pathophysiology, highlighting the need for medical interventions and treatments tailored to the characteristics of patients. As genomic research progresses, novel insights into the molecular mechanisms are starting to provide support to clinical decision-making; now offering ample opportunities for disease screening, diagnosis, prognosis and treatment. Drug targets holding mechanisms with genetic support are more likely to be successful. Therefore, implementing genetic information to the drug development process and a molecular redefinition of skeletal disease can help overcoming current shortcomings in pharmaceutical research, including failed attempts and appalling costs. This review summarizes the achievements of genetic studies in the bone field and their application to clinical care, illustrating the imminent advent of the genomic medicine era.

Adaptation via pleiotropy and linkage: Association mapping reveals a complex genetic architecture within the stickleback Eda locus

Genomic mapping of the loci associated with phenotypic evolution has revealed genomic "hotspots," or regions of the genome that control multiple phenotypic traits. This clustering of loci has important implications for the speed and maintenance of adaptation and could be due to pleiotropic effects of a single mutation or tight genetic linkage of multiple causative mutations affecting different traits. The threespine stickleback (Gasterosteus aculeatus) is a powerful model for the study of adaptive evolution because the marine ecotype has repeatedly adapted to freshwater environments across the northern hemisphere in the last 12,000 years. Freshwater ecotypes have repeatedly fixed a 16 kilobase haplotype on chromosome IV that contains Ectodysplasin (Eda), a gene known to affect multiple traits, including defensive armor plates, lateral line sensory hair cells, and schooling behavior. Many additional traits have previously been mapped to a larger region of chromosome IV that encompasses the Eda freshwater haplotype. To identify which of these traits specifically map to this adaptive haplotype, we made crosses of rare marine fish heterozygous for the freshwater haplotype in an otherwise marine genetic background. Further, we performed fine-scale association mapping in a fully interbreeding, polymorphic population of freshwater stickleback to disentangle the effects of pleiotropy and linkage on the phenotypes affected by this haplotype. Although we find evidence that linked mutations have small effects on a few phenotypes, a small 1.4-kb region within the first intron of Eda has large effects on three phenotypic traits: lateral plate count, and both the number and patterning of the posterior lateral line neuromasts. Thus, the Eda haplotype is a hotspot of adaptation in stickleback due to both a small, pleiotropic region affecting multiple traits as well as multiple linked mutations affecting additional traits.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

Empirical comparisons of multiple Mendelian randomization approaches in the presence of assortative mating

BACKGROUND

Mendelian randomization (MR) is widely used to unravel causal relationships in epidemiological studies. Whereas multiple MR methods have been developed to control for bias due to horizontal pleiotropy, their performance in the presence of other sources of bias, like non-random mating, has been mostly evaluated using simulated data. Empirical comparisons of MR estimators in such scenarios have yet to be conducted. Pleiotropy and non-random mating have been shown to account equally for the genetic correlation between height and educational attainment. Previous studies probing the causal nature of this association have produced conflicting results.

METHODS

We estimated the causal effect of height on educational attainment in various MR models, including the MR-Egger and the MR-Direction of Causation (MR-DoC) models that correct for, or explicitly model, horizontal pleiotropy.

RESULTS

We reproduced the weak but positive association between height and education in the Netherlands Twin Register sample (P= 3.9 × 10-6). All MR analyses suggested that height has a robust, albeit small, causal effect on education. We showed via simulations that potential assortment for height and education had no effect on the causal parameter in the MR-DoC model. With the pleiotropic effect freely estimated, MR-DoC yielded a null finding.

CONCLUSIONS

Non-random mating may have a bearing on the results of MR studies based on unrelated individuals. Family data enable tests of causal relationships to be conducted more rigorously, and are recommended to triangulate results of MR studies assessing pairs of traits leading to non-random mate selection.

The Gene scb-1 Underlies Variation in Caenorhabditis elegans Chemotherapeutic Responses

Pleiotropy, the concept that a single gene controls multiple distinct traits, is prevalent in most organisms and has broad implications for medicine and agriculture. The identification of the molecular mechanisms underlying pleiotropy has the power to reveal previously unknown biological connections between seemingly unrelated traits. Additionally, the discovery of pleiotropic genes increases our understanding of both genetic and phenotypic complexity by characterizing novel gene functions. Quantitative trait locus (QTL) mapping has been used to identify several pleiotropic regions in many organisms. However, gene knockout studies are needed to eliminate the possibility of tightly linked, non-pleiotropic loci. Here, we use a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to identify a single large-effect QTL on the center of chromosome V associated with variation in responses to eight chemotherapeutics. We validate this QTL with near-isogenic lines and pair genome-wide gene expression data with drug response traits to perform mediation analysis, leading to the identification of a pleiotropic candidate gene, scb-1, for some of the eight chemotherapeutics. Using deletion strains created by genome editing, we show that scb-1, which was previously implicated in response to bleomycin, also underlies responses to other double-strand DNA break-inducing chemotherapeutics. This finding provides new evidence for the role of scb-1 in the nematode drug response and highlights the power of mediation analysis to identify causal genes.

A genome-wide scan for pleiotropy between bone mineral density and nonbone phenotypes

Osteoporosis is the most common metabolic bone disorder globally and is characterized by skeletal fragility and microarchitectural deterioration. Genetic pleiotropy occurs when a single genetic element is associated with more than one phenotype. We aimed to identify pleiotropic loci associated with bone mineral density (BMD) and nonbone phenotypes in genome-wide association studies. In the discovery stage, the NHGRI-EBI Catalog was searched for genome-wide significant associations (P value < 5 × 10-8), excluding bone-related phenotypes. SNiPA was used to identify proxies of the significantly associated single nucleotide polymorphisms (SNPs) (r 2 = 1). We then assessed putative genetic associations of this set of SNPs with femoral neck (FN) and lumbar spine (LS) BMD data from the GEFOS Consortium. Pleiotropic variants were claimed at a false discovery rate < 1.4 × 10-3 for FN-BMD and < 1.5 × 10-3 for LS-BMD. Replication of these genetic markers was performed among more than 400 000 UK Biobank participants of European ancestry with available genetic and heel bone ultrasound data. In the discovery stage, 72 BMD-related pleiotropic SNPs were identified, and 12 SNPs located in 11 loci on 8 chromosomes were replicated in the UK Biobank. These SNPs were associated, in addition to BMD, with 14 different phenotypes. Most pleiotropic associations were exhibited by rs479844 (AP5B1, OVOL1 genes), which was associated with dermatological and allergic diseases, and rs4072037 (MUC1 gene), which was associated with magnesium levels and gastroenterological cancer. In conclusion, 12 BMD-related genome-wide significant SNPs showed pleiotropy with nonbone phenotypes. Pleiotropic associations can deepen the genetic understanding of bone-related diseases by identifying shared biological mechanisms with other diseases or traits.

The dark genome and pleiotropy: challenges for precision medicine

Surprisingly we remain ignorant of the function of the majority of genes in the human and mouse genomes. The dark genome is a major obstacle to the interpretation of the function of human genetic variation and its impact on disease. At the same time, pleiotropy, how individual variants influence multiple phenotypes, is key to understanding gene function and the role of genes and genetic networks in disease systems. Both understanding the genetics of disease and developing new therapeutic approaches and advances in precision medicine are all compromised by our limited knowledge of gene function and pleiotropic effects. Illuminating the dark genome and revealing pleiotropy across the genome requires a highly coordinated and international effort to acquire and analyse high-dimensional phenotype data from model organisms. We describe briefly how the International Mouse Phenotyping Consortium is addressing these challenges and the novel features of the pleiotropic landscape that are revealed by functional genomics programmes at genome-wide scale.

Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics

BACKGROUND

Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.

RESULTS

We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.

CONCLUSION

This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology

Nonalcoholic fatty liver disease (NAFLD) affects around a quarter of the global population, paralleling worldwide increases in obesity and metabolic syndrome. NAFLD arises in the context of systemic metabolic dysfunction that concomitantly amplifies the risk of cardiovascular disease and diabetes. These interrelated conditions have long been recognized to have a heritable component, and advances using unbiased association studies followed by functional characterization have created a paradigm for unravelling the genetic architecture of these conditions. A novel perspective is to characterize the shared genetic basis of NAFLD and other related disorders. This information on shared genetic risks and their biological overlap should in future enable the development of precision medicine approaches through better patient stratification, and enable the identification of preventive and therapeutic strategies. In this Review, we discuss current knowledge of the genetic basis of NAFLD and of possible pleiotropy between NAFLD and other liver diseases as well as other related metabolic disorders. We also discuss evidence of causality in NAFLD and other related diseases and the translational significance of such evidence, and future challenges from the study of genetic pleiotropy.

A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in southern Chinese men

BACKGROUND

Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort.

RESULT

A total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson's test, Jaccard's index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10^- 4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10^- 8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3 T3-L1 preadipocytes.

CONCLUSION

All these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.

HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.

PleioNet: a web-based visualization tool for exploring pleiotropy across complex traits

SUMMARY

Pleiotropy plays an important role in furthering our understanding of the shared genetic architecture of different human diseases and traits. However, exploring and visualizing pleiotropic information with currently publicly available tools is limiting and challenging. To aid researchers in constructing and digesting pleiotropic networks, we present PleioNet, a web-based visualization tool for exploring this information across human diseases and traits. This program provides an intuitive and interactive web interface that seamlessly integrates large database queries with visualizations that enable users to quickly explore complex high-dimensional pleiotropic information. PleioNet works on all modern computer and mobile web browsers, making pleiotropic information readily available to a broad range of researchers and clinicians with diverse technical backgrounds. We expect that PleioNet will be an important tool for studying the underlying pleiotropic connections among human diseases and traits.

AVAILABILITY AND IMPLEMENTATION

PleioNet is hosted on Google cloud and freely available at http://www.pleionet.com/.

Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk

Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.