Benchmarking of univariate pleiotropy detection methods, with an application to epilepsy phenotypes

Over the past decades, various methods have been used to scan the human genome to identify genetic variations associated with diseases, in particular with common, complex disorders. One of such approaches is the genome-wide association study (GWAS), which compares genetic variation between affected and healthy individuals to find genomic variants in the DNA sequence associated with a trait. GWAS are usually conducted separately for individual traits, and the same single nucleotide polymorphisms (SNP)/loci are associated with different traits in independent studies 7-10. These findings buttress the knowledge that most complex traits are correlated and have shared genetic architecture, therefore, sharing the same heritable risk factors11. Knowledge of the genetic risk factors can directly or indirectly contribute to improvements in risk assessment, drug target development, and ultimately in providing effective therapies to the affected individuals. Pleiotropy is the phenomenon of a hereditary unit affecting more than one trait, and the earliest reported evidence was provided by Mendel when he noted that some set of features were always observed together in a plant. Although this example could have been purely due to linkage and could be regarded as spurious pleiotropy in recent times, it opened up more discussion and research into pleiotropy, which has since been an active area of research12. In this work, I focused on complex epilepsies and the overlap in the genetic factors impacting their phenotypes. Epilepsy is a brain disorder comprising monogenic and common/complex forms characterized by recurrent partial or generalized seizures. However, the extent to which genetic variants contribute to the disorder and how much of the genetic contribution is shared between the different phenotypes is not yet fully understood. This motivated this project, where I benchmarked available pleiotropy detection approaches to select the best performing method in terms of power and false-positive rate to detect true pleiotropy. Then, I applied the selected method to summary statistics of focal epilepsy (FE) and genetic generalized epilepsy (GGE), provided by the International League Against Epilepsy Consortium (ILAE) on complex epilepsies and the EPI25 collaborative, to identify shared genetic factors in both phenotypes of epilepsy. Identifying pleiotropic SNPs or genes is an active area of research with multiple proposed approaches, broadly categorized into univariate and multivariate methods. Multivariate approaches have the limitation that they require all phenotypes to be measured in the same individual and their corresponding genotype data provided, which is often not the case since GWAS are usually performed per specific trait. However, various consortia studying complex traits readily share the summary statistics (effect sizes and p-values) from genome-wide association studies, making it easier to apply univariate pleiotropy detection approaches that combine these statistics to identify SNPs or loci with a concordant or discordant direction of effects. Therefore, in this project, I first compared the relative power and false-positive rate (FPR) performance of five univariate pleiotropy detection approaches, classic meta-analysis, cFDR, PLACO, ASSET, and CPBayes (see section 6.1), through simulation studies. After that, I applied the best-performing method to the analysis of phenotypes of epilepsy using actual data. The data simulation procedure was performed in 3 steps. First, a population of 1 million individuals of European ancestry was simulated via resampling using the HAPGEN2 software13 and haplotypes of central Europeans from the 1000 genomes project14. In the second phase of the simulation, disease SNPs were randomly selected and used for the additive liability threshold model (ALTM)15 to simulate multifactorial disease phenotypes from the simulated genetic data. As expected, the performance of the methods varied in terms of power and false positive rate (FPR). The variability between the methods is higher for FPR, while most methods are comparable in terms of power, especially for larger sample sizes and RR. Although the classical meta-analysis is very powerful, it is also riddled with a very high false-positive rate, making it less suitable for identifying pleiotropic loci. While all the methods performed well in terms of power, the ASSET method gave a better trade-off between power and FPR for the different simulation approaches. Applying ASSET to the two phenotypes of epilepsy, GGE and FE, resulted in identifying a new putative locus 17q21.32 while replicating locus 2q24.3, previously reported by the ILAE consortium 16. Further, applying the ASSET method to summary statistics of larger samples of epilepsy phenotypes resulted in the identification of loci 2q24.3 and 9q21.13. These findings corroborate the result obtained by the ILAE consortium through mega and meta-analysis. Classical meta-analysis (MA) is not recommended for pleiotropy detection, based on the simulation study results. Though MA demonstrated good power to detect pleiotropy, it also recorded high FPR across all simulation scenarios. However, the ASSET method is highly recommended as it kept the FPR low while demonstrating good power to detect pleiotropy. This study also contributed three new pleiotropic loci (2q24.3, 17q21.32, and 9q21.13) to understanding the relationship of genetic variation with epilepsy phenotypes and the inter-relationship between these phenotypes. Although the locus 17q21.32 could not be replicated in the larger sample set, it is not necessarily a false positive discovery. The locus was genome-wide significant for GGE but marginally significant for FE, which confirmed the trend observed in the FE cases in the EPI25 collaborative dataset, where no genome-wide significance result was found. Therefore, replication in an independent sample is desirable. One limitation of using the univariate pleiotropy detection approaches as seen with the classical MA is that one trait with a very low P-value could drive the observed pleiotropic association. Also, methods like cFDR and PLACO could only accommodate two traits, though this was not a challenge in this project. Despite these limitations, the presented work established a benchmark of the relative performance of the assessed methods and could also guide researchers in related fields in their future work. This study also contributed to understanding the shared genetic factors between GGE and FE with the expectation that larger sample sizes will lead to more discoveries

Genetics of callous-unemotional behavior in children

Callous-unemotional behavior (CU) is currently under consideration as a subtyping index for conduct disorder diagnosis. Twin studies routinely estimate the heritability of CU as greater than 50%. It is now possible to estimate genetic influence using DNA alone from samples of unrelated individuals, not relying on the assumptions of the twin method. Here we use this new DNA method (implemented in a software package called Genome-wide Complex Trait Analysis, GCTA) for the first time to estimate genetic influence on CU. We also report the first genome-wide association (GWA) study of CU as a quantitative trait. We compare these DNA results to those from twin analyses using the same measure and the same community sample of 2,930 children rated by their teachers at ages 7, 9 and 12. GCTA estimates of heritability were near zero, even though twin analysis of CU in this sample confirmed the high heritability of CU reported in the literature, and even though GCTA estimates of heritability were substantial for cognitive and anthropological traits in this sample. No significant associations were found in GWA analysis, which, like GCTA, only detects additive effects of common DNA variants. The phrase ‘missing heritability’ was coined to refer to the gap between variance associated with DNA variants identified in GWA studies versus twin study heritability. However, GCTA heritability, not twin study heritability, is the ceiling for GWA studies because both GCTA and GWA are limited to the overall additive effects of common DNA variants, whereas twin studies are not. This GCTA ceiling is very low for CU in our study, despite its high twin study heritability estimate. The gap between GCTA and twin study heritabilities will make it challenging to identify genes responsible for the heritability of CU

Common DNA markers can account for more than half of the genetic influence on cognitive abilities

For nearly a century, twin and adoption studies have yielded substantial estimates of heritability for cognitive abilities, although it has proved difficult for genomewide-association studies to identify the genetic variants that account for this heritability (i.e., the missing-heritability problem). However, a new approach, genomewide complex-trait analysis (GCTA), forgoes the identification of individual variants to estimate the total heritability captured by common DNA markers on genotyping arrays. In the same sample of 3,154 pairs of 12-year-old twins, we directly compared twin-study heritability estimates for cognitive abilities (language, verbal, nonverbal, and general) with GCTA estimates captured by 1.7 million DNA markers. We found that DNA markers tagged by the array accounted for .66 of the estimated heritability, reaffirming that cognitive abilities are heritable. Larger sample sizes alone will be sufficient to identify many of the genetic variants that influence cognitive abilities

A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data

It has been known since 1904 that, in humans, diverse cognitive traits are positively inter correlated. This forms the basis for the general factor of intelligence (g). Here, we directly test whether there is a partial genetic basis for individual differences in g using data from seven different cognitive tests (N = 11,263 to N = 331,679) and genome-wide autosomal single nucleotide polymorphisms. A genetic g factor accounts for an average of 58.4% (SE = 4.8%) of the genetic variance in the cognitive traits, with the proportion varying widely across traits (range: 9% to 95%). We distill genetic loci that are broadly relevant for many cognitive traits (g) from loci associated specifically with individual cognitive traits. These results contribute to elucidating the etiology of a long-known yet poorly-understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits

The IMAGE project: methodological issues for the molecular genetic analysis of ADHD

The genetic mechanisms involved in attention deficit hyperactivity disorder (ADHD) are being studied with considerable success by several centres worldwide. These studies confirm prior hypotheses about the role of genetic variation within genes involved in the regulation of dopamine, norepinephrine and serotonin neurotransmission in susceptibility to ADHD. Despite the importance of these findings, uncertainties remain due to the very small effects sizes that are observed. We discuss possible reasons for why the true strength of the associations may have been underestimated in research to date, considering the effects of linkage disequilibrium, allelic heterogeneity, population differences and gene by environment interactions. With the identification of genes associated with ADHD, the goal of ADHD genetics is now shifting from gene discovery towards gene functionality – the study of intermediate phenotypes ('endophenotypes'). We discuss methodological issues relating to quantitative genetic data from twin and family studies on candidate endophenotypes and how such data can inform attempts to link molecular genetic data to cognitive, affective and motivational processes in ADHD. The International Multi-centre ADHD Gene (IMAGE) project exemplifies current collaborative research efforts on the genetics of ADHD. This European multi-site project is well placed to take advantage of the resources that are emerging following the sequencing of the human genome and the development of international resources for whole genome association analysis. As a result of IMAGE and other molecular genetic investigations of ADHD, we envisage a rapid increase in the number of identified genetic variants and the promise of identifying novel gene systems that we are not currently investigating, opening further doors in the study of gene functionality

GENETIC ARCHITECTURE OF BONE STRENGTH RELATED PHENOTYPES: TOBAGO FAMILY HEALTH STUDY

Background: Populations of African ancestry have greater bone strength and lower osteoporotic fracture risk than other ethnic groups but there is little information about skeletal health among individuals of African heritage.Methods: Univariate, bivariate and multivariate analytical methods under the variance components framework were employed to dissect the genetic and environment determinants for DXA and pQCT measured bone strength related phenotypes. Our analyses were performed on phenotypic and genotypic data on 471 individuals aged 18+ from 8 large, multigenerational Afro-Caribbean families.Results: The major conclusions of this study are that (1) compared to Caucasians and African Americans, Afro-Caribbeans have the highest peak areal BMD and slowest bone loss rate, but heritabilities of many bone strength related traits are similar among different populations, and (2) genes and environmental factors differentially affect trabecular versus cortical traits, and also BMD versus bone size. These conclusions are supported by differences in heritability and genetic correlation estimates among these bone categories, differential effects of environmental risk factors, as well as associations with different candidate genes. We also evaluated the capability of two multivariate analysis methods for uncovering underlying genetic factors using both simulated and real family data. We concluded Factor Analysis behaves better for both simulated and real data compare to Principal Component Analysis. The residual strategy increases the probability that composite phenotypes detect underlying genetic components if no gene-environment interaction is involved. And most importantly, composite phenotypes from multivariate analysis demonstrated their capabilities to capture more and stronger association signals in real data analysis. Public health significance: Our work has identified the facts that environmental risk factors and genetic determinants may differentially affect various bone compartments and types of bone phenotypes. This information will contribute to the understanding of the underlying genetic architecture of osteoporosis and hence lead to better methods of treatment and prevention of the disease

Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Genetic influences on externalizing psychopathology overlap with cognitive functioning and show developmental variation

Background: Questions remain regarding whether genetic influences on early life psychopathology overlap with cognition and show developmental variation. Methods: Using data from 9,421 individuals aged 8-21 from the Philadelphia Neurodevelopmental Cohort, factors of psychopathology were generated using a bifactor model of item-level data from a psychiatric interview. Five orthogonal factors were generated: anxious-misery (mood and anxiety), externalizing (attention deficit hyperactivity and conduct disorder), fear (phobias), psychosis-spectrum, and a general factor. Genetic analyses were conducted on a subsample of 4,662 individuals of European American ancestry. A genetic relatedness matrix was used to estimate heritability of these factors, and genetic correlations with executive function, episodic memory, complex reasoning, social cognition, motor speed, and general cognitive ability. Gene × Age analyses determined whether genetic influences on these factors show developmental variation. Results: Externalizing was heritable (h2 = 0.46, p = 1 × 10-6), but not anxious-misery (h2 = 0.09, p = 0.183), fear (h2 = 0.04, p = 0.337), psychosis-spectrum (h2 = 0.00, p = 0.494), or general psychopathology (h2 = 0.21, p = 0.040). Externalizing showed genetic overlap with face memory (ρg = -0.412, p = 0.004), verbal reasoning (ρg = -0.485, p = 0.001), spatial reasoning (ρg = -0.426, p = 0.010), motor speed (ρg = 0.659, p = 1x10-4), verbal knowledge (ρg = -0.314, p = 0.002), and general cognitive ability (g)(ρg = -0.394, p = 0.002). Gene × Age analyses revealed decreasing genetic variance (γg = -0.146, p = 0.004) and increasing environmental variance (γe = 0.059, p = 0.009) on externalizing. Conclusions: Cognitive impairment may be a useful endophenotype of externalizing psychopathology and, therefore, help elucidate its pathophysiological underpinnings. Decreasing genetic variance suggests that gene discovery efforts may be more fruitful in children than adolescents or young adults