Pathogenic variants among females with breast cancer and a non-breast cancer reveal opportunities for cancer interception.

PURPOSE: Herein, we report the frequency and distribution of germline pathogenic variants (PVs) among females with breast cancer (BC) and at least one other non-BC who underwent multi-gene panel testing (MGPT). Among females with PVs diagnosed first with BC or ovarian cancer (OC), we sought to enumerate the frequency of subsequent PV-associated cancers. METHODS: Females with BC and cancer of ≥ 1 other site (multiple primary cancers, MPC) who underwent MGPT through Ambry Genetics from March 2012 to December 2016 were included if they had testing of at least 21 genes of interest (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH, NBN, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, STK11, and TP53). Phenotypic data were abstracted from test requisition forms and clinical notes. RESULTS: Of 6,617 evaluable patients, most were White (70.8%) and median age at first cancer, second cancer, and MGPT was 49 (interquartile range [IQR]: 18), 59 (IQR: 16), and 63 (IQR: 16) years, respectively. PVs were found among 14.1% (932/6617) of the overall cohort and in 16.4% (440/2687) of females who were diagnosed first with BC. Among those, 55.2% (243/440) had an actionable PV associated with a subsequent cancer diagnosis including 150 OCs. Of the 2443 females with breast and ovarian cancer, few (n = 97, 9.5%) were diagnosed first with OC, limiting our analysis. CONCLUSIONS: Females with MPC, including BC, have a high frequency of germline PVs (14.1%). These data delineate the opportunities for intercepting subsequent cancers associated with genetic risk among females diagnosed first with BC.

Clinician-Reported Impact of Germline Multigene Panel Testing on Cancer Risk Management Recommendations.

BACKGROUND: With increased adoption of multi-gene panel testing (MGPT) for hereditary cancer, management guidelines now include a wider range of predisposition genes. Yet little is known about whether MGPT results prompt changes to clinicians' risk management recommendations and whether those recommendations adhere to guidelines. METHODS: We assessed cancer risk management recommendations made by clinicians ordering MGPT for hereditary cancer at a diagnostic laboratory using an internet-based survey. We received paired pre- and posttest responses for 2172 patients (response rate = 14.3%). Unpaired posttest responses were received in 168 additional patients with positive results. All tests were 2-sided. RESULTS: Clinicians reported a change in risk management recommendations for 76.6% of patients who tested positive for a pathogenic or likely pathogenic variant, with changes to surveillance being most common (71.1%), followed by surgical (33.6%), chemoprevention (15.1%), and clinical trial (9.4%) recommendations. Clinicians recommended risk-reducing interventions more often for patients with pathogenic variants in high-risk than moderate-risk genes (P < .001), whereas surveillance recommendations were similar for high-risk and moderate-risk genes. Guideline adherence was high for surveillance (86.3%) and surgical (79.6%) recommendations. Changes to risk management recommendations occurred in 8.8% and 7.6% of patients with uncertain and negative results, respectively. CONCLUSIONS: Clinicians report frequent changes to cancer risk management recommendations based on positive results in both high-risk and moderate-risk genes. Reported introduction of interventions in patients with inconclusive and negative results is rare and adherence to practice guidelines is high in patients with positive results, suggesting a low probability of harm resulting from MGPT.

Prevalence and spectrum of pathogenic variants among patients with multiple primary cancers evaluated by clinical characteristics.

BACKGROUND: Multiple primary cancers (MPCs) are a hallmark of cancer predisposition syndromes. Here the frequency of germline pathogenic variants (PVs) among patients with MPCs is reported. METHODS: Patients with MPCs who underwent multigene panel testing from March 2012 to December 2016 were studied. Eligible patients had an analysis of 21 genes: ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, MUTYH, NBN, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, STK11, and TP53. The frequencies of PVs by sex, number of cancers, and age at diagnosis were compared with 2-sided χ2 tests or Fisher exact tests when the number was <10. RESULTS: Among the 9714 patients analyzed, most were female (91.1%) and White (71.0%); the median age at testing was 63 years, and the median ages at first and second cancer diagnoses were 49 and 58 years, respectively. Overall, 1320 (13.6%) had PVs. The prevalence of PVs increased with the number of primary cancers (PCs): 13.1% with 2 PCs, 15.9% with 3 PCs, and 18.0% with ≥4 PCs (P = .00056). Differences in the prevalence of PVs by age at diagnosis were significant: 14.7% with 2 PCs at an age < 50 years, 15.8% with 1 PC at an age < 50 years, and 12.0% with all PCs at an age ≥ 50 years (P = 2.07E-05). PVs by the age at second cancer diagnosis were also significant: 14.7% at an age < 50 years, 13.9% at an age of 50 to 69 years, and 11.4% at an age ≥ 70 years (P for trend = .005). CONCLUSIONS: Among patients with MPCs, there is a high frequency of germline PVs, with a higher frequency found among patients with a higher number of PCs. These findings suggest that genetic testing should be considered even among patients who are older at the diagnosis of an additional primary malignancy.

Similar Genetic Architecture of Alzheimer's Disease and Differential APOE Effect Between Sexes.

Sex differences have been observed in the clinical manifestations of Alzheimer's disease (AD) and elucidating their genetic basis is an active research topic. Based on autosomal genotype data of 7,216 men and 10,680 women, including 8,136 AD cases and 9,760 controls, we explored sex-related genetic heterogeneity in AD by investigating SNP heritability, genetic correlation, as well as SNP- and gene-based genome-wide analyses. We found similar SNP heritability (men: 19.5%; women: 21.5%) and high genetic correlation (R g = 0.96) between the sexes. The heritability of APOE ε4-related risks for AD, after accounting for effects of all SNPs excluding chromosome 19, was nominally, but not significantly, higher in women (10.6%) than men (9.7%). In age-stratified analyses, ε3/ε4 was associated with a higher risk of AD among women than men aged 65-75 years, but not in the full sample. Apart from APOE, no new significant locus was identified in sex-stratified gene-based analyses. Our result of the high genetic correlation indicates overall similar genetic architecture of AD in both sexes at the genome-wide averaged level. Our study suggests that clinically observed sex differences may arise from sex-specific variants with small effects or more complicated mechanisms involving epigenetic alterations, sex chromosomes, or gene-environment interactions.

Causal association of cognitive reserve on Alzheimer's disease with putative sex difference.

INTRODUCTION: Sex-dependent risk factors may underlie sex differences in Alzheimer's disease (AD). METHODS: Using sex-stratified genome-wide association studies (GWAS) of AD, we evaluated associations of 12 traits with AD through polygenic risk scores (PRS) and Mendelian randomization (MR), and explored joint genetic architecture among significant traits by genomic structural equation modeling and network analysis. RESULTS: AD was associated with lower PRS for premorbid cognitive performance, intelligence, and educational attainment. MR showed a causal role for the cognition-related traits in AD, particularly among females. Their joint genetic components encompassed RNA processing, neuron projection development, and cell cycle pathways that overlap with cellular senescence. Cholesterol and C-reactive protein showed pleiotropy but no causality with AD. DISCUSSION: Lower cognitive reserve is causally related to AD. The stronger causal link between cognitive performance and AD in females, despite similar PRS between sexes, suggest these differences may result from gene-environmental interactions accumulated over the lifespan.

Validation of a prostate cancer polygenic risk score.

BACKGROUND: Genome-wide association studies have identified over 100 single-nucleotide polymorphisms (SNPs) associated with prostate cancer (PrCa), and polygenic risk scores (PRS) based on their combined genotypes have been developed for risk stratification. We aimed to assess the contribution of PRS to PrCa risk in a large multisite study. METHODS: The sample included 1972 PrCa cases and 1919 unaffected controls. Next-generation sequencing was used to assess pathogenic variants in 14 PrCa-susceptibility genes and 72 validated PrCa-associated SNPs. We constructed a population-standardized PRS and tested its association with PrCa using logistic regression adjusted for age and family history of PrCa. RESULTS: The mean age of PrCa cases at diagnosis and age of controls at testing/last clinic visit was 59.5 ± 7.2 and 57.2 ± 13.0 years, respectively. Among 1740 cases with pathology data, 57.4% had Gleason score ≤ 6, while 42.6% had Gleason score ≥ 8. In addition, 39.6% cases and 20.1% controls had a family history of PrCa. The PRS was significantly higher in cases than controls (mean ± SD: 1.42 ± 1.11 vs 1.02 ± 0.76; P < .0001). Compared with men in the 1st quartile of age-adjusted PRS, those in the 2nd, 3rd, and 4th quartile were 1.58 (95% confidence interval [CI]: 1.31-1.90), 2.36 (95% CI: 1.96-2.84), and 3.98 (95% CI: 3.29-4.82) times as likely to have PrCa (all P < .0001). Adjustment for family history yielded similar results. PRS predictive performance was consistent with prior literature (area under the receiver operating curve = 0.64; 95% CI: 0.62-0.66). CONCLUSIONS: These data suggest that a 72-SNP PRS is predictive of PrCa, supporting its potential use in clinical risk assessment.

Identification of genetic heterogeneity of Alzheimer's disease across age.

The risk of APOE for Alzheimer's disease (AD) is modified by age. Beyond APOE, the polygenic architecture may also be heterogeneous across age. We aim to investigate age-related genetic heterogeneity of AD and identify genomic loci with differential effects across age. Stratified gene-based genome-wide association studies and polygenic variation analyses were performed in the younger (60-79 years, N = 14,895) and older (≥80 years, N = 6559) age-at-onset groups using Alzheimer's Disease Genetics Consortium data. We showed a moderate genetic correlation (rg = 0.64) between the two age groups, supporting genetic heterogeneity. Heritability explained by variants on chromosome 19 (harboring APOE) was significantly larger in younger than in older onset group (p < 0.05). APOE region, BIN1, OR2S2, MS4A4E, and PICALM were identified at the gene-based genome-wide significance (p < 2.73 × 10-6) with larger effects at younger age (except MS4A4E). For the novel gene OR2S2, we further performed leave-one-out analyses, which showed consistent effects across subsamples. Our results suggest using genetically more homogeneous individuals may help detect additional susceptible loci.

Collagen Formation Assessed by N-Terminal Propeptide of Type 3 Procollagen Is a Heritable Trait and Is Associated With Liver Fibrosis Assessed by Magnetic Resonance Elastography.

N-terminal propeptide of type 3 procollagen (PRO-C3) is a biomarker of liver fibrosis in nonalcoholic fatty liver disease (NAFLD). This study examines the association between PRO-C3 concentration and liver fibrosis assessed by magnetic resonance elastography (MRE)-measured stiffness (MRE-stiffness) and the heritability of PRO-C3 concentration in a cohort of twins and families with and without NAFLD. We performed a cross-sectional analysis of a well-characterized prospective cohort of 306 participants, including 44 probands with NAFLD-cirrhosis and their 72 first-degree relatives, 24 probands with NAFLD without advanced fibrosis and their 24 first-degree relatives, and 72 controls without NAFLD and their 72 first-degree relatives. Liver steatosis was assessed by magnetic resonance imaging proton density fat fraction, and liver fibrosis was assessed by MRE-stiffness. Serum PRO-C3 was assessed by competitive, enzyme-linked immunosorbent assay. We assessed the familial correlation of PRO-C3 concentration, the shared gene effects between PRO-C3 concentration and liver steatosis and fibrosis, and the association between PRO-C3 concentration and genetic variants in the patatin-like phospholipase domain-containing 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), membrane-bound O-acyltransferase domain-containing (MBOAT), and glucokinase regulator (CGKR) genes. In multivariable-adjusted models including age, sex, body mass index, and ethnicity, serum PRO-C3 correlated strongly with liver fibrosis (r2  = 0.50, P < 0.001) and demonstrated robust heritability (h2 , 0.36; 95% confidence interval [CI], 0.07, 0.59; P = 0.016). PRO-C3 concentration and steatosis had a strong genetic correlation (shared genetic determination: 0.62; 95% CI, 0.236, 1.001; P = 0.002), whereas PRO-C3 concentration and fibrosis had a strong environmental correlation (shared environmental determination: 0.55; 95% CI, 0.317, 0.717; P < 0.001). PRO-C3 concentrations were higher in carriers of the TM6SF2 rs58542926-T allele compared with noncarriers: 15.7 (± 10.5) versus 10.8 (± 5.7) ng/L (P = 0.047). Conclusion: Serum PRO-C3 correlates with MRE-assessed fibrosis, is heritable, shares genetic correlation with liver steatosis and shares environmental correlation with liver fibrosis. PRO-C3 concentration appears to be linked to both fibrosis and steatosis and increased in carriers of the TM6SF2 rs58542926 risk allele.

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.

Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia.

OBJECTIVE: Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product. METHOD: The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328). RESULTS: Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms. CONCLUSIONS: The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.

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.

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.

Modeling prior information of common genetic variants improves gene discovery for neuroticism.

Neuroticism reflects emotional instability, and is related to various mental and physical health issues. However, the majority of genetic variants associated with neuroticism remain unclear. Inconsistent genetic variants identified by different genome-wide association studies (GWAS) may be attributable to low statistical power. We proposed a novel framework to improve the power for gene discovery by incorporating prior information of single nucleotide polymorphisms (SNPs) and combining two relevant existing tools, relative enrichment score (RES) and conditional false discovery rate (FDR). Here, SNP's conditional FDR was estimated given its RES based on SNP prior information including linkage disequilibrium (LD)-weighted genic annotation scores, total LD scores and heterozygosity. A known significant locus in chromosome 8p was excluded before estimating FDR due to long-range LD structure. Only one significant LD-independent SNP was detected by analyses of unconditional FDR and traditional GWAS in the discovery sample (N = 59 225), and notably four additional SNPs by conditional FDR. Three of the five SNPs, all identified by conditional FDR, were replicated (P < 0.05) in an independent sample (N = 170 911). These three SNPs are located in intronic regions of CADM2, LINGO2 and EP300 which have been reported to be associated with autism, Parkinson's disease and schizophrenia, respectively. Our approach using a combination of RES and conditional FDR improved power of traditional GWAS for gene discovery providing a useful framework for the analysis of GWAS summary statistics by utilizing SNP prior information, and helping to elucidate the links between neuroticism and complex diseases from a genetic perspective.

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.

Simple obstructive sleep apnea patients without hypertension or diabetes accelerate kidney dysfunction: a population follow-up cohort study from Taiwan.

BACKGROUNDS: Obstructive sleep apnea (OSA) is common in patients on hemodialysis, but its correlation with chronic kidney disease (CKD) is not clear. We aimed to clarify the relationship between OSA without hypertension or diabetes and incidence of CKD in Taiwan. METHODS: This population-based cohort study consisted of patients with newly diagnosed OSA between 2000 and 2009. The comparison cohort was matched for age, sex, diabetes mellitus, and hypertension. All subjects previously diagnosed with acute or chronic kidney disease were excluded. The primary end point was newly diagnosed CKD. RESULTS: We identified 6866 subjects with OSA during the 10-year study period. The median duration until development of CKD in the OSA cohort was 3.2 years, 2.5 months earlier than that in the non-OSA cohort. After exclusion of hypertension and diabetes, 4319 OSA patients was identified and the hazard ratio (HR) of CKD with OSA was 1.37 (95 % confidence interval [CI], 1.05-1.77; p = 0.019). In the subgroup analysis, an increased incidence of CKD in OSA was observed in women (HR, 1.41; 95 % CI, 1.12-1.78; p = 0.0036). CONCLUSIONS: This longitudinal population-based cohort study provides evidence that patients with OSA even without diabetes or hypertension are at higher risk of developing CKD over the next 3 years and nearly 2.5 months earlier than the non-OSA cohort, particularly women.

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).

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.

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).