Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that “all genes affect every complex trait” complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.     

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability.     

ON THE LOW HERITABILITY OF LIFE-HISTORY TRAITS

Life-history traits such as longevity and fecundity often show low heritability. This is usually interpreted in terms of Fisher's fundamental theorem to mean that populations are near evolutionary equilibrium and genetic variance in total fitness is low. We develop the causal relationship between metric traits and life-history traits to show that a life-history trait is expected to have a low heritability whether or not the population is at equilibrium. This is because it is subject to all the environmental variation in the metric traits that affect it plus additional environmental variation. There is no simple prediction regarding levels of additive genetic variance in life-history traits, which may be high at equilibrium. Several other patterns in the inheritance of life-history traits are readily predicted from the causal model. These include the strength of genetic correlations between life-history traits, levels of nonadditive genetic variance, and the inevitability of genotype-environment interaction.     

Does better education mitigate risky health behavior? A mendelian randomization study

Education and risky health behaviors are strongly negatively correlated. Education may affect health behaviors by enabling healthier choices through higher disposable income, increasing information about the harmful effects of risky health behaviors, or altering time preferences. Alternatively, the observed negative correlation may stem from reverse causality or unobserved confounders. Based on the data from the Cardiovascular Risk in Young Finns Study linked to register-based information on educational attainment and family background, this paper identifies the causal effect of education on risky health behaviors. To examine causal effects, we used a genetic score as an instrument for years of education. We found that individuals with higher education allocated more attention to healthy habits. In terms of health behaviors, highly educated people were less likely to smoke. Some model specifications also indicated that the highly educated consumed more fruit and vegetables, but the results were imprecise in this regard. No causal effect was found between education and abusive drinking. In brief, inference based on genetic instruments showed that higher education leads to better choices in some but not all dimensions of health behaviors.     

Utilizing Population Controls in Rare-Variant Case-Parent Association Tests

There is great interest in detecting associations between human traits and rare genetic variation. To address the low power implicit in single-locus tests of rare genetic variants, many rare-variant association approaches attempt to accumulate information across a gene, often by taking linear combinations of single-locus contributions to a statistic. Using the right linear combination is key—an optimal test will up-weight true causal variants, down-weight neutral variants, and correctly assign the direction of effect for causal variants. Here, we propose a procedure that exploits data from population controls to estimate the linear combination to be used in an case-parent trio rare-variant association test. Specifically, we estimate the linear combination by comparing population control allele frequencies with allele frequencies in the parents of affected offspring. These estimates are then used to construct a rare-variant transmission disequilibrium test (rvTDT) in the case-parent data. Because the rvTDT is conditional on the parents’ data, using parental data in estimating the linear combination does not affect the validity or asymptotic distribution of the rvTDT. By using simulation, we show that our new population-control-based rvTDT can dramatically improve power over rvTDTs that do not use population control information across a wide variety of genetic architectures. It also remains valid under population stratification. We apply the approach to a cohort of epileptic encephalopathy (EE) trios and find that dominant (or additive) inherited rare variants are unlikely to play a substantial role within EE genes previously identified through de novo mutation studies.     

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.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.     

Ecogeographic variation of body size in the spectacled salamanders (Salamandrina): influence of genetic structure and local factors

Aim The patterns and causes of ecogeographical body size variation in ectotherms remain controversial. In amphibians, recent genetic studies are leading to the discovery of many cryptic species. We analysed the relationships between body size and climate for a salamander (Salamandrina) that was recently separated into two sibling species, to evaluate how ignoring interspecific and intraspecific genetic structure may affect the conclusions of ecogeographical studies. We also considered the potential effects of factors acting at a local scale. Location Thirty‐four populations covering the whole range of Salamandrina, which is endemic to peninsular Italy. Methods We pooled original data and data from the literature to obtain information on the snout–vent length (SVL) of 3850 Salamandrina females; we obtained high‐resolution climatic data from the sampled localities. We used an information‐theoretic approach to evaluate the roles of climate, genetic features (mitochondrial haplogroup identity) and characteristics of aquatic oviposition sites. We repeated our analyses three times: in the first analysis we ignored genetic data on intraspecific and interspecific variation; in the second one we considered the recently discovered differences between the two sibling species; in the third one we included information on intraspecific genetic structure within Salamandrina perspicillata (for Salamandrina terdigitata the sample size was too small to perform intraspecific analyses). Results If genetic information was ignored, our analysis suggested the existence of a relationship between SVL and climatic variables, with populations of large body size in areas with high precipitation and high thermal range. If species identity was included in the analysis, the role of climatic features was much weaker. When intraspecific genetic differences were also considered, no climatic feature had an effect. In all analyses, local factors were important and explained a large proportion of the variation; populations spawning in still water had a larger body size. Main conclusions An imperfect knowledge of species boundaries, or overlooking the intraspecific genetic variation can strongly affect the results of analyses of body size variation. Furthermore, local factors can be more important than the large‐scale parameters traditionally considered, particularly in species with a small range.     

Association between Adult Height and Risk of Colorectal,Lung, and Prostate Cancer: Results from Meta-analyses of Prospective Studies and Mendelian Randomization Analyses

BackgroundObservational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers.ConclusionsOur study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.     

Mendelian randomization analysis of the causal impact of body mass index and waist-hip ratio on rates of hospital admission

We analyze how measures of adiposity – body mass index (BMI) and waist hip ratio (WHR) – causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission.     

Host genetics and Chlamydia disease: prediction and validation of disease severity mechanisms

Genetic mapping studies may provide association between sequence variants and disease susceptibility that can, with further experimental and computational analysis, lead to discovery of causal mechanisms and effective intervention. We have previously demonstrated that polymorphisms in immunity-related GTPases (IRG) confer a significant difference in susceptibility to Chlamydia psittaci infection in BXD recombinant mice. Here we combine genetic mapping and network modeling to identify causal pathways underlying this association. We infected a large panel of BXD strains with C. psittaci and assessed host genotype, IRG protein polymorphisms, pathogen load, expression of 32 cytokines, inflammatory cell populations, and weight change. Proinflammatory cytokines correlated with each other and were controlled by a novel genetic locus on chromosome 1, but did not affect disease status, as quantified by weight change 6 days after infection In contrast, weight change correlated strongly with levels of inflammatory cell populations and pathogen load that were controlled by an IRG encoding genetic locus (Ctrq3) on chromosome 11. These data provided content to generate a predictive model of infection using a Bayesian framework incorporating genotypes, immune system parameters, and weight change as a measure of disease severity. Two predictions derived from the model were tested and confirmed in a second round of experiments. First, strains with the susceptible IRG haplotype lost weight as a function of pathogen load whereas strains with the resistant haplotype were almost completely unaffected over a very wide range of pathogen load. Second, we predicted that macrophage activation by Ctrq3 would be central in conferring pathogen tolerance. We demonstrated that macrophage depletion in strains with the resistant haplotype led to neutrophil influx and greater weight loss despite a lower pathogen burden. Our results show that genetic mapping and network modeling can be combined to identify causal pathways underlying chlamydial disease susceptibility.     

Genetic and environmental influences on body fat distribution, fasting insulin levels and CVD: are the influences shared?

Central body fat distribution has been shown to be related to hyperinsulinemia, insulin resistance, hypertriglyceridemia, and atherosclerosis to a greater degree than general obesity. There are known to be both genetic and environmental effects on all components of this clustering. Whether these genetic effects are due to one set of genes in common to the components or whether genetic influences on insulin resistance and/or general/abdominal fatness 'turn on' other genes that affect other components of the syndrome is not clear. We analyzed data from the Swedish Adoption/Twin Study of Aging (60% female; monozygotic = 116, dizygotic = 202; average age 65 years) to determine whether there were genetic and/or environmental factors shared among general body fat distribution, abdominal body fat distribution, fasting insulin levels and cardiovascular disease. We found additive genetic effects in males to be significantly different from those in females with genetic effects accounting for variance in waist-hip ratio (males = 28%; females = 49%), body mass index (males = 58%; females = 73%), fasting insulin levels (FI) (males = 27%; females = 49%), and cardiovascular disease (CVD) (males = 18%; females = 37%). There were also shared genetic and environmental effects among all the variables except CVD, but a majority of the genetic variance for these measures was trait specific.     

Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies

Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO.     

Landscape effects on gene flow for a climate‐sensitive montane species,the American pika

Climate change is arguably the greatest challenge to conservation of our time. Most vulnerability assessments rely on past and current species distributions to predict future persistence but ignore species' abilities to disperse through landscapes, which may be particularly important in fragmented habitats and crucial for long‐term persistence in changing environments. Landscape genetic approaches explore the interactions between landscape features and gene flow and can clarify how organisms move among suitable habitats, but have suffered from methodological uncertainties. We used a landscape genetic approach to determine how landscape and climate‐related features influence gene flow for American pikas (Ochotona princeps) in Crater Lake National Park. Pikas are heat intolerant and restricted to cool microclimates; thus, range contractions have been predicted as climate changes. We evaluated the correlation between landscape variables and genetic distance using partial Mantel tests in a causal modelling framework, and used spatially explicit simulations to evaluate methods of model optimization including a novel approach based on relative support and reciprocal causal modelling. We found that gene flow was primarily restricted by topographic relief, water and west‐facing aspects, suggesting that physical restrictions related to small body size and mode of locomotion, as well as exposure to relatively high temperatures, limit pika dispersal in this alpine habitat. Our model optimization successfully identified landscape features influencing resistance in the simulated data for this landscape, but underestimated the magnitude of resistance. This is the first landscape genetic study to address the fundamental question of what limits dispersal and gene flow in the American pika.     

Political Attitudes Develop Independently of Personality Traits

The primary assumption within the recent personality and political orientations literature is that personality traits cause people to develop political attitudes. In contrast, research relying on traditional psychological and developmental theories suggests the relationship between most personality dimensions and political orientations are either not significant or weak. Research from behavioral genetics suggests the covariance between personality and political preferences is not causal, but due to a common, latent genetic factor that mutually influences both. The contradictory assumptions and findings from these research streams have yet to be resolved. This is in part due to the reliance on cross-sectional data and the lack of longitudinal genetically informative data. Here, using two independent longitudinal genetically informative samples, we examine the joint development of personality traits and attitude dimensions to explore the underlying causal mechanisms that drive the relationship between these features and provide a first step in resolving the causal question. We find change in personality over a ten-year period does not predict change in political attitudes, which does not support a causal relationship between personality traits and political attitudes as is frequently assumed. Rather, political attitudes are often more stable than the key personality traits assumed to be predicting them. Finally, the results from our genetic models find that no additional variance is accounted for by the causal pathway from personality traits to political attitudes. Our findings remain consistent with the original construction of the five-factor model of personality and developmental theories on attitude formation, but challenge recent work in this area.     

Social evolution and the two elements of causation

The kin selection theory has recently been criticised on the basis of claiming that genetic relatedness does not play a causal role in the social evolution among individuals of insect societies. We outline here a line of criticism of this view by demonstrating two things. First, there are strong conceptual, theoretical and empirical reasons to think that close genetic relatedness has been necessary for the rise of the helper castes of social insects. And second, once we understand how causal explanation itself results from an interplay of two logically distinct elements, necessity and sufficiency, we can also understand the scenarios in which relatedness does not seem to play a causal role for evolution of helper castes. The result of this analysis is that we should be more careful about the way we frame the empirical data on the evolution of social behaviour.     

A genetic instrumental variables analysis of the effects of prenatal smoking on birth weight: evidence from two samples

There is a large literature showing the detrimental effects of prenatal smoking on birth and childhood health outcomes. It is somewhat unclear though, whether these effects are causal or reflect other characteristics and choices by mothers who choose to smoke that may also affect child health outcomes or biased reporting of smoking. In this paper we use genetic markers that predict smoking behaviors as instruments to address the endogeneity of smoking choices in the production of birth and childhood health outcomes. Our results indicate that prenatal smoking produces more dramatic declines in birth weight than estimates that ignore the endogeneity of prenatal smoking, which is consistent with previous studies with non-genetic instruments. We use data from two distinct samples from Norway and the United States with different measured instruments and find nearly identical results. The study provides a novel application that can be extended to study several behavioral impacts on health and social and economic outcomes.