Marcio Almeida

Omics-squared: Human genomic, transcriptomic and phenotypic data for genetic analysis workshop 19

BackgroundThe Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data.MethodsGAW19 data were donated by the T2D-GENES Consortium and the San Antonio Family Heart Study and included whole genome and exome sequences for odd-numbered autosomes, measures of gene expression, systolic and diastolic blood pressures, and related covariates in two Mexican American samples. These two samples were a collection of 20 large families with whole genome sequence and transcriptomic data and a set of 1943 unrelated individuals with exome sequence. For each sample, simulated phenotypes were constructed based on the real sequence data. ‘Functional’ genes and variants for the simulations were chosen based on observed correlations between gene expression and blood pressure. The simulations focused primarily on additive genetic models but also included a genotype-by-medication interaction. A total of 245 genes were designated as ‘functional’ in the simulations with a few genes of large effect and most genes explainingu2009<u20091xa0% of the trait variation. An additional phenotype, Q1, was simulated to be correlated among related individuals, based on theoretical or empirical kinship matrices, but was not associated with any sequence variants. Two hundred replicates of the phenotypes were simulated. The GAW19 data are an expansion of the data used at GAW18, which included the family-based whole genome sequence, blood pressure, and simulated phenotypes, but not the gene expression data or the set of 1943 unrelated individuals with exome sequence.

Recurrent major depression and right hippocampal volume: A bivariate linkage and association study

Previous work has shown that the hippocampus is smaller in the brains of individuals suffering from major depressive disorder (MDD) than those of healthy controls. Moreover, right hippocampal volume specifically has been found to predict the probability of subsequent depressive episodes. This study explored the utility of right hippocampal volume as an endophenotype of recurrent MDD (rMDD). We observed a significant genetic correlation between the two traits in a large sample of Mexican American individuals from extended pedigrees (ρg = −0.34, p = 0.013). A bivariate linkage scan revealed a significant pleiotropic quantitative trait locus on chromosome 18p11.31‐32 (LOD = 3.61). Bivariate association analysis conducted under the linkage peak revealed a variant (rs574972) within an intron of the gene SMCHD1 meeting the corrected significance level (χ2 = 19.0, p = 7.4 × 10−5). Univariate association analyses of each phenotype separately revealed that the same variant was significant for right hippocampal volume alone, and also revealed a suggestively significant variant (rs12455524) within the gene DLGAP1 for rMDD alone. The results implicate right‐hemisphere hippocampal volume as a possible endophenotype of rMDD, and in so doing highlight a potential gene of interest for rMDD risk. Hum Brain Mapp 37:191–202, 2016.

Genome-wide linkage on chromosome 10q26 for a dimensional scale of major depression

Major depressive disorder (MDD) is a common and potentially life-threatening mood disorder. Identifying genetic markers for depression might provide reliable indicators of depression risk, which would, in turn, substantially improve detection, enabling earlier and more effective treatment. The aim of this study was to identify rare variants for depression, modeled as a continuous trait, using linkage and post-hoc association analysis. The sample comprised 1221 Mexican-American individuals from extended pedigrees. A single dimensional scale of MDD was derived using confirmatory factor analysis applied to all items from the Past Major Depressive Episode section of the Mini-International Neuropsychiatric Interview. Scores on this scale of depression were subjected to linkage analysis followed by QTL region-specific association analysis. Linkage analysis revealed a single genome-wide significant QTL (LOD=3.43) on 10q26.13, QTL-specific association analysis conducted in the entire sample revealed a suggestive variant within an intron of the gene LHPP (rs11245316, p=7.8×10(-04); LD-adjusted Bonferroni-corrected p=8.6×10(-05)). This region of the genome has previously been implicated in the etiology of MDD; the present study extends our understanding of the involvement of this region by highlighting a putative gene of interest (LHPP).

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

Significance Contributions of rare variants to common and complex traits such as type 2 diabetes (T2D) are difficult to measure. This paper describes our results from deep whole-genome analysis of large Mexican-American pedigrees to understand the role of rare-sequence variations in T2D and related traits through enriched allele counts in pedigrees. Our study design was well-powered to detect association of rare variants if rare variants with large effects collectively accounted for large portions of risk variability, but our results did not identify such variants in this sample. We further quantified the contributions of common and rare variants in gene expression profiles and concluded that rare expression quantitative trait loci explain a substantive, but minor, portion of expression heritability. A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

TRAK2, a novel regulator of ABCA1 expression, cholesterol efflux and HDL biogenesis

AimsnThe recent failures of HDL-raising therapies have underscored our incomplete understanding of HDL biology. Therefore there is an urgent need to comprehensively investigate HDL metabolism to enable the development of effective HDL-centric therapies. To identify novel regulators of HDL metabolism, we performed a joint analysis of human genetic, transcriptomic, and plasma HDL-cholesterol (HDL-C) concentration data and identified a novel association between trafficking protein, kinesin binding 2 (TRAK2) and HDL-C concentration. Here we characterize the molecular basis of the novel association between TRAK2 and HDL-cholesterol concentration.nnnMethods and resultsnAnalysis of lymphocyte transcriptomic data together with plasma HDL from the San Antonio Family Heart Study (nu2009=u20091240) revealed a significant negative correlation between TRAK2 mRNA levels and HDL-C concentration, HDL particle diameter and HDL subspecies heterogeneity. TRAK2 siRNA-mediated knockdown significantly increased cholesterol efflux to apolipoprotein A-I and isolated HDL from human macrophage (THP-1) and liver (HepG2) cells by increasing the mRNA and protein expression of the cholesterol transporter ATP-binding cassette, sub-family A member 1 (ABCA1). The effect of TRAK2 knockdown on cholesterol efflux was abolished in the absence of ABCA1, indicating that TRAK2 functions in an ABCA1-dependent efflux pathway. TRAK2 knockdown significantly increased liver X receptor (LXR) binding at the ABCA1 promoter, establishing TRAK2 as a regulator of LXR-mediated transcription of ABCA1.nnnConclusionnWe show, for the first time, that TRAK2 is a novel regulator of LXR-mediated ABCA1 expression, cholesterol efflux, and HDL biogenesis. TRAK2 may therefore be an important target in the development of anti-atherosclerotic therapies.

Filtering genetic variants and placing informative priors based on putative biological function

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.

Modeling methylation data as an additional genetic variance component

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20’s organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a random-effect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era.

Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24.

The insula and medial prefrontal cortex (mPFC) share functional, histological, transcriptional, and developmental characteristics, and they serve higher cognitive functions of theoretical relevance to schizophrenia and related disorders. Meta‐analyses and multivariate analysis of structural magnetic resonance imaging (MRI) scans indicate that gray matter density and volume reductions in schizophrenia are the most consistent and pronounced in a network primarily composed of the insula and mPFC. We used source‐based morphometry, a multivariate technique optimized for structural MRI, in a large sample of randomly ascertained pedigrees (Nu2009=u2009887) to derive an insula–mPFC component and to investigate its genetic determinants. Firstly, we replicated the insula–mPFC gray matter component as an independent source of gray matter variation in the general population, and verified its relevance to schizophrenia in an independent case‐control sample. Secondly, we showed that the neuroanatomical variation defined by this component is largely determined by additive genetic variation (h2u2009=u20090.59), and genome‐wide linkage analysis resulted in a significant linkage peak at 12q24 (LODu2009=u20093.76). This region has been of significant interest to psychiatric genetics as it contains the Dariers disease locus and other proposed susceptibility genes (e.g., DAO, NOS1), and it has been linked to affective disorders and schizophrenia in multiple populations. Thus, in conjunction with previous clinical studies, our data imply that one or more psychiatric risk variants at 12q24 are co‐inherited with reductions in mPFC and insula gray matter concentration.

Pleiotropy of cardiometabolic syndrome with obesity-related anthropometric traits determined using empirically derived kinships from the Busselton Health Study

Over two billion adults are overweight or obese and therefore at an increased risk of cardiometabolic syndrome (CMS). Obesity-related anthropometric traits genetically correlated with CMS may provide insight into CMS aetiology. The aim of this study was to utilise an empirically derived genetic relatedness matrix to calculate heritabilities and genetic correlations between CMS and anthropometric traits to determine whether they share genetic risk factors (pleiotropy). We used genome-wide single nucleotide polymorphism (SNP) data on 4671 Busselton Health Study participants. Exploiting both known and unknown relatedness, empirical kinship probabilities were estimated using these SNP data. General linear mixed models implemented in SOLAR were used to estimate narrow-sense heritabilities (h2) and genetic correlations (rg) between 15 anthropometric and 9 CMS traits. Anthropometric traits were adjusted by body mass index (BMI) to determine whether the observed genetic correlation was independent of obesity. After adjustment for multiple testing, all CMS and anthropometric traits were significantly heritable (h2 range 0.18–0.57). We identified 50 significant genetic correlations (rg range: −xa00.37 to 0.75) between CMS and anthropometric traits. Five genetic correlations remained significant after adjustment for BMI [high density lipoprotein cholesterol (HDL-C) and waist–hip ratio; triglycerides and waist–hip ratio; triglycerides and waist–height ratio; non-HDL-C and waist–height ratio; insulin and iliac skinfold thickness]. This study provides evidence for the presence of potentially pleiotropic genes that affect both anthropometric and CMS traits, independently of obesity.

Data on genetic associations of carotid atherosclerosis markers in Mexican American and European American rheumatoid arthritis subjects

Carotid Intima-media thickness (CIMT) and plaque are well established markers of subclinical atherosclerosis and are widely used for identifying subclinical atherosclerotic disease. We performed association analyses using Metabochip array to identify genetic variants that influence variation in CIMT and plaque, measured using B-mode ultrasonography, in rheumatoid arthritis (RA) patients. Data on genetic associations of common variants associated with both CIMT and plaque in RA subjects involving Mexican Americans (MA) and European Americans (EA) populations are presented in this article. Strong associations were observed after adjusting for covariate effects including baseline clinical characteristics and statin use. Susceptibility loci and genes and/or nearest genes associated with CIMT in MAs and EAs with RA are presented. In addition, common susceptibility loci influencing CIMT and plaque in both MAs and EAs have been presented. Polygenic Risk Score (PRS) plots showing complementary evidence for the observed CIMT and plaque association signals are also shown in this article. For further interpretation and details, please see the research article titled “A Genetic Association Study of Carotid Intima-Media Thickness (CIMT) and Plaque in Mexican Americans and European Americans with Rheumatoid Arthritis” which is being published in Atherosclerosis (Arya et al., 2018) [1].(Arya et al., in press) Thus, common variants in several genes exhibited significant associations with CIMT and plaque in both MAs and EAs as presented in this article. These findings may help understand the genetic architecture of subclinical atherosclerosis in RA populations.