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.
depression; hippocampus; linkage; genome-wide association
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 (N = 887) 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 (h2 = 0.59), and genome-wide linkage analysis resulted in a significant linkage peak at 12q24 (LOD = 3.76). This region has been of significant interest to psychiatric genetics as it contains the Darier’s 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.
Extended pedigrees; magnetic resonance imaging; insula; medial prefrontal cortex; quantitative trait locus
SLC30A8 encodes zinc transporter 8 which is involved in packaging and release of insulin. Evidence for the association of SLC30A8 variants with type 2 diabetes (T2D) is inconclusive. We interrogated single nucleotide polymorphisms (SNPs) around SLC30A8 for association with T2D in high-risk, pedigreed individuals from extended Mexican American families. This study of 118 SNPs within 50 kb of the SLC30A8 locus tested the association with eight T2D-related traits at four levels: (i) each SNP using measured genotype approach (MGA); (ii) interaction of SNPs with age and sex; (iii) combinations of SNPs using Bayesian Quantitative Trait Nucleotide (BQTN) analyses; and (iv) entire gene locus using the gene burden test. Only one SNP (rs7817754) was significantly associated with incident T2D but a summary statistic based on all T2D-related traits identified 11 novel SNPs. Three SNPs and one SNP were weakly but interactively associated with age and sex, respectively. BQTN analyses could not demonstrate any informative combination of SNPs over MGA. Lastly, gene burden test results showed that at best the SLC30A8 locus could account for only 1-2% of the variability in T2D-related traits. Our results indicate a lack of association of the SLC30A8 SNPs with T2D in Mexican American families.
The 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.
GAW19 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 explaining < 1 % 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.
The application of pathway and gene-set based analyses to high-throughput data is increasingly common and represents an effort to understand underlying biology where single-gene or single-marker analyses have failed. Many such analyses rely on the a priori identification of genes associated with the trait of interest. In contrast, this variance-component–based approach creates a similarity matrix of individuals based on the expression of genes in each pathway.
We compared 16 methods of calculating similarity for positive control matrices based on probes for the genes used to model the simulated Genetic Analysis Workshop phenotypes.
A simple correlation matrix outperforms the other methods by identifying pathways associated with the simulated phenotypes at nearly twice the rate expected based on the associations of the component transcripts and an approximate false-positive rate of 0.05.
This method has a number of additional advantages compared to single-transcript and pathway overrepresentation analyses, including the ability to estimate the proportion of variation explained by each pathway and the logistical advantage of only calculating the distance matrices once for each messenger RNA data set regardless of the number of phenotypes. Additionally, it offers a significant reduction in the multiple testing burden over individual consideration of each probe.
Genetic Analysis Workshop 19 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence and gene expression data from a pedigree-based sample, as well as whole-exome sequence data from a large cohort of unrelated individuals. In this article we present an overview of the data sets, the GAW experience, and summaries of the contributions arranged into nine methodological themes.
The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. This method simultaneously accounts for all repeat measurements of a phenotype in families. We applied this method to systolic blood pressure (SBP) measurements from three time points using the Genetic Analysis Workshop 19 (GAW19) whole-genome sequence family simulated data set and 200 simulated replicates. Data consisted of 849 individuals from 20 extended Mexican American pedigrees. Comparisons were made among 3 statistical approaches: (a) constrained, where the effect of a variant or gene region on the mean trait value was constrained to be equal across all measurements; (b) unconstrained, where the variant or gene region effect was estimated separately for each time point; and (c) the average SBP measurement from three time points. These approaches were run for nine genetic variants with known effect sizes (>0.001) for SBP variability and a known gene-centric kernel (MAP4)-based test under the GAW19 simulation model across 200 replicates.
When compared to results using two time points, the constrained method utilizing all 3 time points increased power to detect association. Averaging SBP was equally effective when the variant has a large effect on the phenotype, but less powerful for variants with lower effect sizes. However, averaging SBP was far more effective than either the constrained or unconstrained approaches when using a gene-centric kernel-based test.
We determined that this constrained multivariate approach improves genetic signal over the bivariate method. However, this method is still only effective in those variants that explain a moderate to large proportion of the phenotypic variance but is not as effective for gene-centric tests.
Populations and individuals differ in susceptibility to infections because of a number of factors, including host genetic variation. We previously demonstrated that differences in antibody titer, which reflect infection history, are significantly heritable. Here we attempt to identify the genetic factors influencing variation in these serological phenotypes. Blood samples from >1300 Mexican Americans were quantified for IgG antibody level against 12 common infections, selected on the basis of their reported role in cardiovascular disease risk: Chlamydia pneumoniae; Helicobacter pylori; Toxoplasma gondii; cytomegalovirus; herpes simplex I virus; herpes simplex II virus; human herpesvirus 6 (HHV6); human herpesvirus 8 (HHV8); varicella zoster virus; hepatitis A virus (HAV); influenza A virus; and influenza B virus. Pathogen-specific quantitative antibody levels were analyzed, as were three measures of pathogen burden. Genome-wide linkage and joint linkage and association analyses were performed using ~1 million SNPs. Significant linkage (lod scores >3.0) was obtained for HHV6 (on chromosome 7), HHV8 (on chromosome 6), and HAV (on chromosome 13). SNP rs4812712 on chromosome 20 was significantly associated with C. pneumoniae (P=5.3 × 10−8). However, no genome-wide significant loci were obtained for the other investigated antibodies. We conclude that it is possible to localize host genetic factors influencing some of these antibody traits, but that further larger-scale investigations will be required to elucidate the genetic mechanisms contributing to variation in antibody levels.
Although DNA methylation is now recognized as an important mediator of complex diseases, the extent to which the genetic basis of such diseases is accounted for by DNA methylation is unknown. In the setting of large, extended families representing a minority, high-risk population of the USA, we aimed to characterize the role of epigenome-wide DNA methylation in type 2 diabetes (T2D). Using Illumina HumanMethylation450 BeadChip arrays, we tested for association of DNA methylation at 446 356 sites with age, sex and phenotypic traits related to T2D in 850 pedigreed Mexican-American individuals. Robust statistical analyses showed that (i) 15% of the methylome is significantly heritable, with a median heritability of 0.14; (ii) DNA methylation at 14% of CpG sites is associated with nearby sequence variants; (iii) 22% and 3% of the autosomal CpG sites are associated with age and sex, respectively; (iv) 53 CpG sites were significantly associated with liability to T2D, fasting blood glucose and insulin resistance; (v) DNA methylation levels at five CpG sites, mapping to three well-characterized genes (TXNIP, ABCG1 and SAMD12) independently explained 7.8% of the heritability of T2D (vi) methylation at these five sites was unlikely to be influenced by neighboring DNA sequence variation. Our study has identified novel epigenetic indicators of T2D risk in Mexican Americans who have increased risk for this disease. These results provide new insights into potential treatment targets of T2D.
Only few systematic studies on the contribution of copy number variation to gene expression variation have been published to date. Here we identify effects of copy number variable regions (CNVRs) on nearby gene expression by investigating 909 CNVRs and expression levels of 12059 nearby genes in white blood cells from Mexican-American participants of the San Antonio Family Heart Study. We empirically evaluate our ability to detect the contribution of CNVs to proximal gene expression (presumably in cis) at various window sizes (up to a 10 Mb distance) between the gene and CNV. We found a ~1-Mb window size to be optimal for capturing cis effects of CNVs. Up to 10% of the CNVs in this study were found to be significantly associated with the expression of at least one gene within their vicinity. As expected, we find that CNVs that directly overlap gene sequences have the largest effects on gene expression (compared with non-overlapping CNVRs located nearby), with positive correlation (except for a few exceptions) between estimated genomic dosage and expression level. We find that genes whose expression level is significantly influenced by nearby CNVRs are enriched for immunity and autoimmunity related genes. These findings add to the currently limited catalog of CNVRs that are recognized as expression quantitative trait loci, and have implications for future study designs as well as for prioritizing candidate causal variants in genomic regions associated with disease.
We conducted a genome-wide association study (GWAS) for maximum number of alcoholic drinks consumed in a 24-hour period (“MaxDrinks”), in two independent samples comprised of over 9,500 subjects, following up on our GWAS for alcohol dependence (AD) in European Americans (EAs) and African Americans (AAs).
The samples included our GWAS samples (Yale-UPenn) recruited for studies of the genetics of drug or alcohol dependence, and a public available sample: the Study of Addiction: Genetics and Environment (SAGE). Genome-wide association analysis was performed for ∼890,000 single nucleotide polymorphisms (SNPs) using linear association random effects models. European Americans and African Americans were separately analyzed.
The results confirmed significant associations of the well-known functional loci at ADH1B with MaxDrinks in EAs (rs1229984 Arg48His p= 5.96 x10-15) and AAs (rs2066207 Arg370Cys, p=2.50 x 10-10). The region of significant association on chromosome 4 was extended to LOC100507053 in AAs but not EAs. We also identified potentially novel significant common SNPs for MaxDrinks in EAs in the Yale-UPenn sample: rs1799876 at SERPINC1 on chromosome 1 (4.00 x 10-8) and rs2309169 close to ANKRD36 on chromosome 2 (p=5.58 x 10-9). After adjusting for the peak SNP rs1229984 on ADH1B, rs1799876 was nearly significant (p= 1.99 x 10-7) and rs2309169 remained highly significant (2.12 x 10-9).
The results provide further support that ADH1B modulates alcohol consumption. Future replications of potential novel loci are warranted. This is the largest MaxDrinks GWAS to date, the first in AAs.
Alcohol maximum drinks; genome-wide association; African American; European American
Progranulin (GRN) loss-of-function mutations leading to progranulin protein (PGRN) haploinsufficiency are prevalent genetic causes of frontotemporal dementia. Reports also indicated PGRN-mediated neuroprotection in models of Alzheimer's and Parkinson's disease; thus, increasing PGRN levels is a promising therapeutic for multiple disorders. To uncover novel PGRN regulators, we linked whole-genome sequence data from 920 individuals with plasma PGRN levels and identified the prosaposin (PSAP) locus as a new locus significantly associated with plasma PGRN levels. Here we show that both PSAP reduction and overexpression lead to significantly elevated extracellular PGRN levels. Intriguingly, PSAP knockdown increases PGRN monomers, whereas PSAP overexpression increases PGRN oligomers, partly through a protein–protein interaction. PSAP-induced changes in PGRN levels and oligomerization replicate in human-derived fibroblasts obtained from a GRN mutation carrier, further supporting PSAP as a potential PGRN-related therapeutic target. Future studies should focus on addressing the relevance and cellular mechanism by which PGRN oligomeric species provide neuroprotection.
Increasing progranulin (PGRN) levels is a promising approach for treating frontotemporal dementia and other neurodegenerative diseases. Here Nicholson et al. show that the prosaposin (PSAP) locus is associated with plasma PGRN levels and demonstrate that PSAP can alter PGRN levels and its oligomerization.
The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2=0.53–0.90, p<10−5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.
Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening.
Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia – the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D.
The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention.
Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.
Electronic supplementary material
The online version of this article (doi:10.1186/s12944-016-0234-3) contains supplementary material, which is available to authorized users.
Diabetes; Endocrine disorders; Lipidomics; Diagnostic tools; Genetics
The variation in serum uric acid concentrations is under significant genetic influence. Elevated SUA concentrations have been linked to increased risk for gout, kidney stones, chronic kidney disease, and cardiovascular disease whereas reduced serum uric acid concentrations have been linked to multiple sclerosis, Parkinson’s disease and Alzheimer’s disease. Previously, we identified a novel locus on chromosome 3p26 affecting serum uric acid concentrations in Mexican Americans from San Antonio Family Heart Study. As a follow up, we examined genome-wide single nucleotide polymorphism data in an extended cohort of 1281 Mexican Americans from multigenerational families of the San Antonio Family Heart Study and the San Antonio Family Diabetes/Gallbladder Study. We used a linear regression-based joint linkage/association test under an additive model of allelic effect, while accounting for non-independence among family members via a kinship variance component.
Univariate genetic analysis indicated serum uric acid concentrations to be significant heritable (h2 = 0.50 ± 0.05, p < 4 × 10−35), and linkage analysis of serum uric acid concentrations confirmed our previous finding of a novel locus on 3p26 (LOD = 4.9, p < 1 × 10−5) in the extended sample. Additionally, we observed strong association of serum uric acid concentrations with variants in following candidate genes in the 3p26 region; inositol 1,4,5-trisphosphate receptor, type 1 (ITPR1), contactin 4 (CNTN4), decapping mRNA 1A (DCP1A); transglutaminase 4 (TGM4) and rho guanine nucleotide exchange factor (GEF) 26 (ARHGEF26) [p < 3 × 10−7; minor allele frequencies ranged between 0.003 and 0.42] and evidence of cis-regulation for ITPR1 transcripts.
Our results confirm the importance of the chromosome 3p26 locus and genetic variants in this region in the regulation of serum uric acid concentrations.
Joint linkage/association approach; CNTN4; ITPR1; Family-based study
While the role of type 2 diabetes (T2D) in inducing endothelial dysfunction is fairly well-established the etiological role of endothelial dysfunction in the onset of T2D is still a matter of debate. In the light of conflicting evidence in this regard, we conducted a prospective study to determine the association of circulating levels of soluble intercellular adhesion molecule 1 (sICAM-1) and soluble vessel cell adhesion molecule 1 (sVCAM-1) with incident T2D.
Data from this study came from 1,269 Mexican Americans of whom 821 initially T2D-free individuals were longitudinally followed up in the San Antonio Family Heart Study. These individuals were followed for 9752.95 person-years for development of T2D. Prospective association of sICAM-1 and sVCAM-1 with incident T2D was studied using Kaplan-Meier survival plots and mixed effects Cox proportional hazards modeling to account for relatedness among study participants. Incremental value of adhesion molecule biomarkers was studied using integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indexes.
Decreasing median values for serum concentrations of sICAM-1 and sVCAM-1 were observed in the following groups in this order: individuals with T2D at baseline, individuals who developed T2D during follow-up, individuals with prediabetes at baseline and normal glucose tolerant (NGT) individuals who remained T2D-free during follow-up. Top quartiles for sICAM-1 and sVCAM-1 were strongly and significantly associated with homeostatic model of assessment—insulin resistance (HOMA-IR). Mixed effects Cox proportional hazards modeling revealed that after correcting for important clinical confounders, high sICAM-1 and sVCAM-1 concentrations were associated with 2.52 and 1.99 times faster progression to T2D as compared to low concentrations, respectively. Individuals with high concentrations for both sICAM-1 and sVCAM-1 progressed to T2D 3.42 times faster than those with low values for both sICAM-1 and sVCAM-1. The results were similar in women in reproductive age group and the remainder of the cohort. Inclusion of sICAM-1 and sVCAM-1 in predictive models significantly improved reclassification and discrimination. The majority of these results were seen even when the analyses were restricted to NGT individuals.
Serum concentrations of sICAM-1 and sVCAM-1 independently and additively predict future T2D and represent important candidate biomarkers of T2D.
We report a genome-wide association study (GWAS) of nicotine dependence defined on the basis of scores on the Fagerström Test for Nicotine Dependence in European-American (EA) and African-American (AA) populations.
Our sample, from the one used in our previous GWAS, included only subjects who had smoked >100 cigarettes lifetime (2114 EA and 2602 AA subjects) and an additional 927 AA and 2003 EA subjects from the Study of Addiction: Genetics and Environment project [via the database of Genotypes and Phenotypes (dbGAP)]. GWAS analysis considered Fagerström Test for Nicotine Dependence score as an ordinal trait, separately in each population and sample and by combining the results in meta-analysis. We also conducted analyses that were adjusted for other substance use disorder criteria in a single nucleotide polymorphism (SNP) subset.
In EAs, one chromosome 7 intergenic region was genome-wide significant (GWS): rs13225753, p = 3.48 × 10−8 (adjusted). In AAs, GWS associations were observed at numerous SNPs mapped to a region on chromosome 14 of >305,000 base pairs (minimal p = 4.74 × 10−10). Two chromosome 8 regions were associated: p = 4.45 × 10−8 at DLC1 SNP rs289519 (unadjusted) and p = 1.10 × 10−9 at rs6996964 (adjusted for other substances), located between CSGALNACT1 and INTS10. No GWS associations were observed at the chromosome 15 nicotinic receptor gene cluster (CHRNA5-CHRNA3-CHRNB4) previously associated with nicotine dependence and smoking quantity traits. TSNAX-DISC1 SNP rs821722 (p = 1.46 × 10−7) was the most significant result with substantial contributions from both populations; we previously identified DISC1 associations with opioid dependence. Pathway analysis identified association with nitric oxide synthase and adenosine monophosphate-activated protein kinase pathways in EAs.
The key risk loci identified, which require replication, offer novel insights into nicotine dependence biology.
AMPK pathway; DISC1; DLC1; eNOS pathway; FTND; GWAS; Nicotine dependence; Population differences
The role of the amygdala in emotion recognition is well established and separately each trait has been shown to be highly heritable, but the potential role of common genetic influences on both traits has not been explored. Here we present an investigation of the pleiotropic influences of amygdala and emotion recognition in a sample of randomly selected, extended pedigrees (N = 858). Using a combination of univariate and bivariate linkage we found a pleiotropic region for amygdala and emotion recognition on 4q26 (LOD = 4.34). Association analysis conducted in the region underlying the bivariate linkage peak revealed a variant meeting the corrected significance level (pBonferroni = 5.01×10−05) within an intron of PDE5A (rs2622497, Χ2 =16.67, p = 4.4×10−05) as being jointly influential on both traits. PDE5A has been implicated previously in recognition-memory deficits and is expressed in subcortical structures that are thought to underlie memory ability including the amygdala. The present paper extends our understanding of the shared etiology between amygdala and emotion recognition by showing that the overlap between the two traits is due, at least in part, to common genetic influences. Moreover, the present paper identifies a pleiotropic locus for the two traits and an associated variant, which localizes the genetic signal even more precisely. These results, when taken in the context of previous research, highlight the potential utility of PDE5-inhibitors for ameliorating emotion-recognition deficits in populations including, but not exclusively, those individuals suffering from mental or neurodegenerative illness.
There is growing interest in the hypertriglyceridemic waist (HTGW) phenotype, defined as high waist circumference (≥95 cm in males and ≥80 cm in females) combined with high serum triglyceride concentration (≥2.0 mmol/L in males and ≥1.5 mmol/L in females) as a marker of type 2 diabetes (T2D) and cardiovascular disease. However, the prevalence of this phenotype in high-risk populations, its association with T2D, and the genetic or epigenetic influences on HTGW are not well explored. Using data from large, extended families of Mexican Americans (a high-risk minority population in the USA) we aimed to: (1) estimate the prevalence of this phenotype, (2) test its association with T2D and related traits, and (3) dissect out the genetic and epigenetic associations with this phenotype using genome-wide and epigenome-wide studies, respectively.
Data for this study was from 850 Mexican American participants (representing 39 families) recruited under the ongoing San Antonio Family Heart Study, 26 % of these individuals had HTGW. This phenotype was significantly heritable (h2r = 0.52, p = 1.1 × 10−5) and independently associated with T2D as well as fasting glucose levels and insulin resistance. We conducted genome-wide association analyses using 759,809 single nucleotide polymorphisms (SNPs) and epigenome-wide association analyses using 457,331 CpG sites. There was no evidence of any SNP associated with HTGW at the genome-wide level but two CpG sites (cg00574958 and cg17058475) in CPT1A and one CpG site (cg06500161) in ABCG1 were significantly associated with HTGW and remained significant after adjusting for the closely related components of metabolic syndrome. CPT1A holds a cardinal position in the metabolism of long-chain fatty acids while ABCG1 plays a role in triglyceride metabolism.
Our results reemphasize the value of HTGW as a marker of T2D. This phenotype shows association with DNA methylation within CPT1A and ABCG1, genes involved in fatty acid and triglyceride metabolism. Our results underscore the importance of epigenetics in a clinically informative phenotype.
Electronic supplementary material
The online version of this article (doi:10.1186/s13148-016-0173-x) contains supplementary material, which is available to authorized users.
Brain abnormalities of subcortical and limbic nuclei are common in schizophrenia and variation in these structures is considered a putative endophenotype for the disorder. Multiplex-Multigenerational families afflicted by schizophrenia provide an opportunity to investigate the impact of shared genetic ancestry, but have not been previously examined to study structural brain abnormalities. Here we estimate the heritability of subcortical and hippocampal brain volumes in such families and the heritability of sub-regions using advanced shape analysis.
439 participants from two sites completed 3-Tesla structural magnetic resonance imaging. They included 190 European-Americans from 32 Multiplex- Multigenerational families with schizophrenia and 249 healthy comparison subjects. Subcortical and hippocampal volume and shape were measured in 14 brain structures. Heritability was estimated for volume and shape.
Volume and shape were heritable in families. Estimates of heritability in subcortical and limbic volumes ranged from 0.45 in the right hippocampus up to 0.84 in the left putamen. The shape of these structures was heritable (range: 0.40–0.49) and specific sub-regional shape estimates of heritability tended to exceed heritability estimates of volume alone.
These results demonstrate that volume and shape of subcortical and limbic brain structures are potential endophenotypic markers in schizophrenia. The specificity obtained using shape analysis may improve selection of imaging phenotypes that better reflect the underlying neurobiology. Our findings can aid in the identification of specific genetic targets that affect brain structure and function in schizophrenia.
heritability; schizophrenia; hippocampus; neuroimaging-genetics; endophenotypes; structural MRI
Alcohol abuse and dependence (alcohol use disorders, AUDs) are associated with brain shrinkage. Subcortical structures including the amygdala, hippocampus, ventral striatum, dorsal striatum, and thalamus subserve reward functioning and may be particularly vulnerable to alcohol-related damage. These structures may also show pre-existing deficits impacting the development and maintenance of AUD. It remains unclear whether there are common genetic features underlying both subcortical volumes and AUD. In this study, structural brain images were acquired from 872 Mexican-American individuals from extended pedigrees. Subcortical volumes were obtained using FreeSurfer, and quantitative genetic analyses were performed in SOLAR. We hypothesized the following: (1) reduced subcortical volumes in individuals with lifetime AUD relative to unrelated controls; (2) reduced subcortical volumes in individuals with current relative to past AUD; (3) in non-AUD individuals, reduced subcortical volumes in those with a family history of AUD compared to those without; and (4) evidence for common genetic underpinnings (pleiotropy) between AUD risk and subcortical volumes. Results showed that individuals with lifetime AUD showed larger ventricular and smaller amygdala volumes compared to non-AUD individuals. For the amygdala, there were no differences in volume between current vs past AUD, and non-AUD individuals with a family history of AUD demonstrated reductions compared to those with no such family history. Finally, amygdala volume was genetically correlated with the risk for AUD. Together, these results suggest that reduced amygdala volume reflects a pre-existing difference rather than alcohol-induced neurotoxic damage. Our genetic correlation analysis provides evidence for a common genetic factor underlying both reduced amygdala volumes and AUD risk.
Although case-control approaches are beginning to disentangle schizophrenia’s complex polygenic burden, other methods will likely be necessary to fully identify and characterize risk genes. Endophenotypes, traits genetically correlated with an illness, can help characterize the impact of risk genes by providing genetically relevant traits that are more tractable than the behavioral symptoms that classify mental illness. Here we present an analytic approach for discovering and empirically validating endophenotypes in extended pedigrees with very few affected individuals. Our approach indexes each family member’s risk as a function of shared genetic kinship with an affected individual, often referred to as the coefficient of relatedness. To demonstrate the utility of this approach, we search for neurocognitive and neuroanatomic endophenotypes for schizophrenia in large unselected multigenerational pedigrees.
A fixed effect test within the variance component framework was performed on neurocognitive and cortical surface area traits in 1,606 Mexican-American individuals from large, randomly ascertained extended pedigrees who participate in the “Genetics of Brain Structure and Function” study. As affecteds are excluded from analyses, results are not influenced by disease state or medication usage.
Despite having sampled just 6 individuals with schizophrenia, our sample provided 233 individuals at various levels of genetic risk for the disorder. We identified three neurocognitive measures (digit-symbol substitution, facial memory, and emotion recognition) and six medial temporal and prefrontal cortical surfaces associated with liability for schizophrenia.
With our novel analytic approach one can discover and rank endophenotypes for schizophrenia, or any heritable disease, in randomly ascertained pedigrees.
endophenotype; schizophrenia; family study; coefficient of relatedness; cognition; cortical surface area
Cardiovascular disease (CVD) is the most common cause of death in the United States and is associated with a high economic burden. Prevention of CVD focuses on controlling or improving the lipid profile of patients at risk. The human lipidome is made up of thousands of ubiquitous lipid species. By studying biologically simple canonical lipid species, we investigated whether the lipidome is genetically redundant and whether its genetic influences can provide clinically relevant clues of CVD risk.
Methods and Results
We performed a genetic study of the human lipidome in 1,212 individuals from 42 extended Mexican American families. High-throughput mass spectrometry enabled rapid capture of precise lipidomic profiles, providing 319 unique species. Using variance-component based heritability analyses and bivariate trait analyses, we detected significant genetic influences on each lipid assayed. Median heritability of the plasma lipid species was 0.37. Hierarchical clustering based on complex genetic correlation patterns identified 12 genetic clusters that characterized the plasma lipidome. These genetic clusters were differentially but consistently associated with risk factors of CVD, including central obesity, obesity, type 2 diabetes, raised serum triglycerides and metabolic syndrome. Also these clusters consistently predicted occurrence of cardiovascular deaths during follow-up.
The human plasma lipidome is heritable. Shared genetic influences reduce the dimensionality of the human lipidome into clusters that are associated with risk factors of CVD.
lipids; genetics; cardiovascular disease; family study; genetic correlation
Working memory, a theoretical construct from the field of cognitive psychology, is crucial to everyday life. It refers to the ability to temporarily store and manipulate task-relevant information. The identification of genes for working memory might shed light on the molecular mechanisms of this important cognitive ability and—given the genetic overlap between, for example, schizophrenia risk and working-memory ability—might also reveal important candidate genes for psychiatric illness. A number of genome-wide searches for genes that influence working memory have been conducted in recent years. Interestingly, the results of those searches converge on the mediating role of neuronal excitability in working-memory performance, such that the role of each gene highlighted by genome-wide methods plays a part in ion channel formation and/or dopaminergic signaling in the brain, with either direct or indirect influence on dopamine levels in the prefrontal cortex. This result dovetails with animal models of working memory that highlight the role of dynamic network connectivity, as mediated by dopaminergic signaling, in the dorsolateral prefrontal cortex. Future work, which aims to characterize functional variants influencing working-memory ability, might choose to focus on those genes highlighted in the present review and also those networks in which the genes fall. Confirming gene associations and highlighting functional characterization of those associations might have implications for the understanding of normal variation in working-memory ability and also for the development of drugs for mental illness.
Working memory; Genomics; Cognition; GWA; Dynamic network connectivity
Adolescent drinking is an important public health concern, one that is influenced by both genetic and environmental factors. The functional variant rs1229984 in alcohol dehydrogenase 1B (ADH1B) has been associated at a genome-wide level with alcohol use disorders in diverse adult populations. However, few data are available regarding whether this variant influences early drinking behaviors and whether social context moderates this effect. This study examines the interplay between rs1229984 and peer drinking in the development of adolescent drinking milestones.
1,550 European and African American individuals who had a full drink of alcohol before age 18 were selected from a longitudinal study of youth as part of the Collaborative Study on the Genetics of Alcoholism (COGA). Cox proportional hazards regression, with GxE product terms in the final models, was used to study two primary outcomes during adolescence: age of first intoxication and age of first DSM-5 alcohol use disorder symptom.
The minor A allele of rs1229984 was associated with a protective effect for first intoxication (HR=0.56, 95% CI 0.41–0.76) and first DSM-5 symptom (HR=0.45, 95% CI 0.26–0.77) in the final models. Reporting that most or all best friends drink was associated with a hazardous effect for first intoxication (HR=1.81, 95% CI 1.62–2.01) and first DSM-5 symptom (HR=2.17, 95% 1.88–2.50) in the final models. Furthermore, there was a significant GxE interaction for first intoxication (p=.002) and first DSM-5 symptom (p=.01). Among individuals reporting none or few best friends drinking, the ADH1B variant had a protective effect for adolescent drinking milestones, but for those reporting most or all best friends drinking, this effect was greatly reduced.
Our results suggest that the risk factor of best friends drinking attenuates the protective effect of a well-established ADH1B variant for two adolescent drinking behaviors. These findings illustrate the interplay between genetic and environmental factors in the development of drinking milestones during adolescence.
Gene-Environment Interaction; Adolescent; Alcohol Dehydrogenase; Peer drinking