Min-Tzu Lo

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders.

Personality is influenced by genetic and environmental factors and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genome-wide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit–hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety).

Shared genetic effects between hepatic steatosis and fibrosis: A prospective twin study

Nonalcoholic fatty liver disease is associated with metabolic risk factors including hypertension and dyslipidemia and may progress to liver fibrosis. Studies have shown that hepatic steatosis and fibrosis are heritable, but whether they have a significant shared gene effect is unknown. This study examined the shared gene effects between hepatic steatosis and fibrosis and their associations with metabolic risk factors. This was a cross‐sectional analysis of a prospective cohort of well‐characterized, community‐dwelling twins (45 monozygotic, 20 dizygotic twin pairs, 130 total subjects) from southern California. Hepatic steatosis was assessed with magnetic resonance imaging‐proton density fat fraction and hepatic fibrosis with magnetic resonance elastography. A standard bivariate twin additive genetics and unique environment effects model was used to estimate the proportion of phenotypic variance between two phenotypes accounted for by additive genetic effects and individual‐specific environmental effects. Genetic correlations estimated from this model represent the degree to which the genetic determinants of two phenotypes overlap. Mean (± standard deviation) age and body mass index were 47.1 (±21.9) years and 26.2 (±5.8) kg/m2, respectively. Among the cohort, 20% (26/130) had hepatic steatosis (magnetic resonance imaging‐proton density fat fraction ≥5%), and 8.2% (10/122) had hepatic fibrosis (magnetic resonance elastography ≥3 kPa). Blood pressure (systolic and diastolic), triglycerides, glucose, homeostatic model assessment of insulin resistance, insulin, hemoglobin A1c, and low high‐density lipoprotein had significant shared gene effects with hepatic steatosis. Triglycerides, glucose, homeostatic model assessment of insulin resistance, insulin, hemoglobin A1c, and low high‐density lipoprotein had significant shared gene effects with hepatic fibrosis. Hepatic steatosis and fibrosis had a highly significant shared gene effect of 0.756 (95% confidence interval 0.716‐1, P < 0.0001). Conclusions: Genes involved with steatosis pathogenesis may also be involved with fibrosis pathogenesis. (Hepatology 2016;64:1547‐1558)

Identification of genetic loci shared between schizophrenia and the Big Five personality traits.

Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort (n = 59,225) and schizophrenia in the Psychiatric Genomics Consortium cohort (n = 82,315), we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology.

Estimating Effect Sizes and Expected Replication Probabilities from GWAS Summary Statistics

Genome-wide Association Studies (GWAS) result in millions of summary statistics (“z-scores”) for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N = 82,315) and putamen volume (N = 12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 106 and 105. The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

Conservation of Distinct Genetically-Mediated Human Cortical Pattern

The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10−3), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.

Link between gut-microbiome derived metabolite and shared gene-effects with hepatic steatosis and fibrosis in NAFLD.

Previous studies have shown that gut‐microbiome is associated with nonalcoholic fatty liver disease (NAFLD). We aimed to examine if serum metabolites, especially those derived from the gut‐microbiome, have a shared gene‐effect with hepatic steatosis and fibrosis. This is a cross‐sectional analysis of a prospective discovery cohort including 156 well‐characterized twins and families with untargeted metabolome profiling assessment. Hepatic steatosis was assessed using magnetic‐resonance‐imaging proton‐density‐fat‐fraction (MRI‐PDFF) and fibrosis using MR‐elastography (MRE). A twin additive genetics and unique environment effects (AE) model was used to estimate the shared gene‐effect between metabolites and hepatic steatosis and fibrosis. The findings were validated in an independent prospective validation cohort of 156 participants with biopsy‐proven NAFLD including shotgun metagenomics sequencing assessment in a subgroup of the cohort. In the discovery cohort, 56 metabolites including 6 microbial metabolites had a significant shared gene‐effect with both hepatic steatosis and fibrosis after adjustment for age, sex and ethnicity. In the validation cohort, 6 metabolites were associated with advanced fibrosis. Among them, only one microbial metabolite, 3‐(4‐hydroxyphenyl)lactate, remained consistent and statistically significantly associated with liver fibrosis in the discovery and validation cohort (fold‐change of higher‐MRE versus lower‐MRE: 1.78, P < 0.001 and of advanced versus no advanced fibrosis: 1.26, P = 0.037, respectively). The share genetic determination of 3‐(4‐hydroxyphenyl)lactate with hepatic steatosis was RG:0.57,95%CI:0.27‐0.80, P < 0.001 and with fibrosis was RG:0.54,95%CI:0.036‐1, P = 0.036. Pathway reconstruction linked 3‐(4‐hydroxyphenyl)lactate to several human gut‐microbiome species. In the validation cohort, 3‐(4‐hydroxyphenyl)lactate was significantly correlated with the abundance of several gut‐microbiome species, belonging only to Firmicutes, Bacteroidetes and Proteobacteria phyla, previously reported as associated with advanced fibrosis. Conclusion: This proof of concept study provides evidence of a link between the gut‐microbiome and 3‐(4‐hydroxyphenyl)lactate that shares gene‐effect with hepatic steatosis and fibrosis. (Hepatology 2018).

Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure

Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5′UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs. The method provides increased power to detect associated SNPs by estimating stratum-specific false discovery rate (FDR), where strata are classified according to enrichment scores. Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohort, a total of 15 independent significant SNPs were identified (conditional FDR < 0.05). In contrast, 4 SNPs were found based on standard GWAS analysis (P < 5 × 10−8). These 11 novel loci include GATAD2B, ASCC3, DSCAML1, and HELZ, which are previously implicated in various neural related phenotypes. The current findings demonstrate the boost in power with the annotation-informed FDR method, and provide insight into the genetic architecture of the putamen.

Beyond heritability: improving discoverability in imaging genetics

Structural neuroimaging measures based on magnetic resonance imaging have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability-which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.

947 Shared Genetic Effects Between Hepatic Steatosis and Fibrosis: A Prospective Twin Study

Author(s): Cui, J; Chen, CH; Lo, MT; Schork, N; Bettencourt, R; Gonzalez, MP; Bhatt, A; Hooker, J; Shaffer, K; Nelson, KE; Long, MT; Brenner, DA; Sirlin, CB; Loomba, R | Abstract:

GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies

Motivation: Multiple marker analysis of the genome‐wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high‐dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using non‐local priors in an iterative variable selection framework. Results: We develop a variable selection method, named, iterative non‐local prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen‐and‐select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of non‐local priors. The hallmark of our method is the introduction of ‘structured screen‐and‐select’ strategy, that considers hierarchical screening, which is not only based on response‐predictor associations, but also based on response‐response associations and concatenates variable selection within that hierarchy. Extensive simulation studies with single nucleotide polymorphisms having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype. Availability and implementation: An R‐package for implementing the GWASinlps method is available at https://cran.r‐project.org/web/packages/GWASinlps/index.html. Supplementary information: Supplementary data are available at Bioinformatics online.