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Ali Gharavi and colleagues report a genome-wide association analysis of IgA nephropathy in over 20,000 individuals of European and East Asian ancestry. They identify genome-wide significant signals at three new loci near VAV3 , CARD9 and ITGAM - ITGAX and correlations between genetic risk and pathogen diversity. We performed a genome-wide association study (GWAS) of IgA nephropathy (IgAN), the most common form of glomerulonephritis, with discovery and follow-up in 20,612 individuals of European and East Asian ancestry. We identified six new genome-wide significant associations, four in ITGAM - ITGAX , VAV3 and CARD9 and two new independent signals at HLA-DQB1 and DEFA . We replicated the nine previously reported signals, including known SNPs in the HLA-DQB1 and DEFA loci. The cumulative burden of risk alleles is strongly associated with age at disease onset. Most loci are either directly associated with risk of inflammatory bowel disease (IBD) or maintenance of the intestinal epithelial barrier and response to mucosal pathogens. The geospatial distribution of risk alleles is highly suggestive of multi-locus adaptation, and genetic risk correlates strongly with variation in local pathogens, particularly helminth diversity, suggesting a possible role for host–intestinal pathogen interactions in shaping the genetic landscape of IgAN.

Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bi-directional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring causal relationships in complex data sets such as those generated from modern \"omics\" technologies.

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the ‘no horizontal pleiotropy’ assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from –131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances. The MR-PRESSO test detects and corrects horizontal pleiotropy in multi-instrument Mendelian randomization (MR) analyses. Applying the MR-PRESSO test to 4,250 MR tests of complex traits and diseases finds horizontal pleiotropy in >48% of causal relationships.

AbstractObjectiveTo determine whether endogenous testosterone has a causal role in thromboembolism, heart failure, and myocardial infarction.DesignTwo sample mendelian randomisation study using genetic variants as instrumental variables, randomly allocated at conception, to infer causality as additional randomised evidence.SettingReduction by Dutasteride of Prostate Cancer Events (REDUCE) randomised controlled trial, UK Biobank, and CARDIoGRAMplusC4D 1000 Genomes based genome wide association study.Participants3225 men of European ancestry aged 50-75 in REDUCE; 392 038 white British men and women aged 40-69 from the UK Biobank; and 171 875 participants of about 77% European descent, from CARDIoGRAMplusC4D 1000 Genomes based study for validation.Main outcome measuresThromboembolism, heart failure, and myocardial infarction based on self reports, hospital episodes, and death.ResultsOf the UK Biobank participants, 13 691 had thromboembolism (6208 men, 7483 women), 1688 had heart failure (1186, 502), and 12 882 had myocardial infarction (10 136, 2746). In men, endogenous testosterone genetically predicted by variants in the JMJD1C gene region was positively associated with thromboembolism (odds ratio per unit increase in log transformed testosterone (nmol/L) 2.09, 95% confidence interval 1.27 to 3.46) and heart failure (7.81, 2.56 to 23.8), but not myocardial infarction (1.17, 0.78 to 1.75). Associations were less obvious in women. In the validation study, genetically predicted testosterone (based on JMJD1C gene region variants) was positively associated with myocardial infarction (1.37, 1.03 to 1.82). No excess heterogeneity was observed among genetic variants in their associations with the outcomes. However, testosterone genetically predicted by potentially pleiotropic variants in the SHBG gene region had no association with the outcomes.ConclusionsEndogenous testosterone was positively associated with thromboembolism, heart failure, and myocardial infarction in men. Rates of these conditions are higher in men than women. Endogenous testosterone can be controlled with existing treatments and could be a modifiable risk factor for thromboembolism and heart failure.

We generated a global genetic interaction network for Saccharomyces cerevisiae , constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

Joseph Pickrell and colleagues analyze genome-wide association data for 42 human phenotypes or diseases and identify several hundred loci influencing multiple traits. They also find several traits with overlapping genetic architectures as well as pairs of traits showing evidence of a causal relationship. We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10 −12 ) and Parkinson disease (log OR = −0.15, P = 1.6 × 10 −7 ), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.

Between the 1930s and 1950s, scientists developed key principles of population genetics to try and explain the aging process. Almost a century later, these aging theories, including antagonistic pleiotropy and mutation accumulation, have been experimentally validated in animals. Although the theories have been much harder to test in humans despite research dating back to the 1970s, recent research is closing this evidence gap. Here we examine the strength of evidence for antagonistic pleiotropy in humans, one of the leading evolutionary explanations for the retention of genetic risk variation for non-communicable diseases. We discuss the analytical tools and types of data that are used to test for patterns of antagonistic pleiotropy and provide a primer of evolutionary theory on types of selection as a guide for understanding this mechanism and how it may manifest in other diseases. We find an abundance of non-experimental evidence for antagonistic pleiotropy in many diseases. In some cases, several studies have independently found corroborating evidence for this mechanism in the same or related sets of diseases including cancer and neurodegenerative diseases. Recent studies also suggest antagonistic pleiotropy may be involved in cardiovascular disease and diabetes. There are also compelling examples of disease risk variants that confer fitness benefits ranging from resistance to other diseases or survival in extreme environments. This provides increasingly strong support for the theory that antagonistic pleiotropic variants have enabled improved fitness but have been traded for higher burden of disease later in life. Future research in this field is required to better understand how this mechanism influences contemporary disease and possible consequences for their treatment.

Abstract Context Understanding phenotypic connection between type II diabetes (T2D) mellitus and amyotrophic lateral sclerosis (ALS) can offer valuable sight into shared disease etiology and have important implication in drug repositioning and therapeutic intervention. Objective This work aims to disentangle the nature of the inverse relationship between T2D mellitus and ALS. Methods Depending on summary statistics of T2D (n = 898 130) and ALS (n = 80 610), we estimated the genetic correlation between them and prioritized pleiotropic genes through a multiple-tissue expression quantitative trait loci–weighted integrative analysis and the conjunction conditional false discovery rate (ccFDR) method. We implemented mendelian randomization (MR) analyses to evaluate the causal relationship between the 2 diseases. A mediation analysis was performed to assess the mediating role of T2D in the pathway from T2D-related glycemic/anthropometric traits to ALS. Results We found supportive evidence of a common genetic foundation between T2D and ALS (rg = –0.223, P = .004) and identified 8 pleiotropic genes (ccFDR < 0.10). The MR analyses confirmed that T2D exhibited a neuroprotective effect on ALS, leading to an approximately 5% (95% CI, 0% ~ 9.6%, P = .038) reduction in disease risk. In contrast, no substantial evidence was observed that supported the causal influence of ALS on T2D. The mediation analysis revealed T2D can also serve as an active mediator for several glycemic/anthropometric traits, including high-density lipoprotein cholesterol, overweight, body mass index, obesity class 1, and obesity class 2, with the mediation effect estimated to be 0.024, –0.022, –0.041, –0.016, and –0.012, respectively. Conclusion We provide new evidence supporting the observed inverse link between T2D and ALS, and revealed that a shared genetic component and causal association commonly drove such a relationship. We also demonstrate the mediating role of T2D standing in the pathway from T2D-related glycemic/anthropometric traits to ALS.

Recent studies have led to the identification of genetic loci that are shared between psychiatric disorders. Here O’Donovan and Owen argue that it is unlikely that risk alleles exist that are singular to any one such disorder. Recent genomic studies have revealed the highly polygenic nature of psychiatric disorders, including schizophrenia, bipolar disorder and major depressive disorder. Many of the individual genetic associations are shared across multiple disorders in a way that points to extensive biological pleiotropy and further challenges the biological validity of existing diagnostic approaches. Here we argue that the existence of risk alleles specific to a single diagnostic category is unlikely. We also highlight some of the important clinical repercussions of pleiotropy.