Serum phosphatidylinositol as a biomarker for bipolar disorder liability.

OBJECTIVES: Individuals with bipolar disorder (BPD) exhibit alterations in their phospholipid levels. It is unclear whether these alterations are a secondary consequence of illness state, or if phospholipids and illness risk overlap genetically. If the latter were true, then phospholipids might provide key insights into the pathophysiology of the illness. Therefore, we rank-ordered phospholipid classes by their genetic overlap with BPD risk in order to establish which class might be most informative in terms of increasing our understanding of illness pathophysiology. METHODS: Analyses were conducted in a sample of 558 individuals, unselected for BPD, from 38 extended pedigrees (average family size=14.79, range=2-82). We calculated a coefficient of relatedness for all family members of nine individuals with BPD in the sample (N=185); this coefficient was set to be zero in unrelated individuals (N=373). Then, under an endophenotype ranking value (ERV) approach, this scalar index was tested against 13 serum-based phospholipid concentrations in order to rank-order lipid classes by their respective overlap with BPD risk. RESULTS: The phosphatidylinositol class was significantly heritable (h2 =0.26, P=6.71 × 10-05 ). It was the top-ranked class, and was significantly associated with BPD risk after correction for multiple testing (ß=-1.18, P=2.10 × 10-03 , ERV=0.49). CONCLUSIONS: We identified a peripheral biomarker, serum-based phosphatidylinositol, which exhibits a significant association with BPD risk. Therefore, given that phosphatidylinositol and BPD risk share partially common etiology, it seems that this lipid class warrants further investigation, not only in terms of treatment, but also as a promising diagnostic and risk marker.

Genetic loci for Epstein-Barr virus nuclear antigen-1 are associated with risk of multiple sclerosis.

BACKGROUND: Infection with the Epstein-Barr virus (EBV) is associated with an increased risk of multiple sclerosis (MS). OBJECTIVE: We sought genetic loci influencing EBV nuclear antigen-1 (EBNA-1) IgG titers and hypothesized that they may play a role in MS risk. METHODS: We performed a genome-wide association study (GWAS) of anti-EBNA-1 IgG titers in 3599 individuals from an unselected twin family cohort, followed by a meta-analysis with data from an independent EBNA-1 GWAS. We then examined the shared polygenic risk between the EBNA-1 GWAS (effective sample size (Neff) = 5555) and a large MS GWAS (Neff = 15,231). RESULTS: We identified one locus of strong association within the human leukocyte antigen (HLA) region, of which the most significantly associated genotyped single nucleotide polymorphism (SNP) was rs2516049 (p = 4.11 × 10-9). A meta-analysis including data from another EBNA-1 GWAS in a cohort of Mexican-American families confirmed that rs2516049 remained the most significantly associated SNP (p = 3.32 × 10-20). By examining the shared polygenic risk, we show that the genetic risk for elevated anti-EBNA-1 titers is positively correlated with the development of MS, and that elevated EBNA-1 titers are not an epiphenomena secondary to MS. In the joint meta-analysis of EBNA-1 titers and MS, loci at 1p22.1, 3p24.1, 3q13.33, and 10p15.1 reached genome-wide significance (p < 5 × 10-8). CONCLUSIONS: Our results suggest that apart from the confirmed HLA region, the association of anti-EBNA-1 IgG titer with MS risk is also mediated through non-HLA genes, and that studies aimed at identifying genetic loci influencing EBNA immune response provides a novel opportunity to identify new and characterize existing genetic risk factors for MS.

Evaluation of estimated genetic values and their application to genome-wide investigation of systolic blood pressure.

The concept of breeding values, an individual's phenotypic deviation from the population mean as a result of the sum of the average effects of the genes they carry, is of great importance in livestock, aquaculture, and cash crop industries where emphasis is placed on an individual's potential to pass desirable phenotypes on to the next generation. As breeding or genetic values (as referred to here) cannot be measured directly, estimated genetic values (EGVs) are based on an individual's own phenotype, phenotype information from relatives, and, increasingly, genetic data. Because EGVs represent additive genetic variation, calculating EGVs in an extended human pedigree is expected to provide a more refined phenotype for genetic analyses. To test the utility of EGVs in genome-wide association, EGVs were calculated for 847 members of 20 extended Mexican American families based on 100 replicates of simulated systolic blood pressure. Calculations were performed in GAUSS to solve a variation on the standard Best Linear Unbiased Predictor (BLUP) mixed model equation with age, sex, and the first 3 principal components of sample-wide genetic variability as fixed effects and the EGV as a random effect distributed around the relationship matrix. Three methods of calculating kinship were considered: expected kinship from pedigree relationships, empirical kinship from common variants, and empirical kinship from both rare and common variants. Genome-wide association analysis was conducted on simulated phenotypes and EGVs using the additive measured genotype approach in the SOLAR software package. The EGV-based approach showed only minimal improvement in power to detect causative loci.

Mapping of a blood pressure QTL on chromosome 17 in American Indians of the strong heart family study.

BACKGROUND: Blood pressure (BP) is a complex trait, with a heritability of 30 to 40%. Several genome wide associated BP loci explain only a small fraction of the phenotypic variation. Family studies can provide an important tool for gene discovery by utilizing trait and genetic transmission information among relative-pairs. We have previously described a quantitative trait locus at chromosome 17q25.3 influencing systolic BP in American Indians of the Strong Heart Family Study (SHFS). This locus has been reported to associate with variation in BP traits in family studies of Europeans, African Americans and Hispanics. METHODS: To follow-up persuasive linkage findings at this locus, we performed comprehensive genotyping in the 1-LOD unit support interval region surrounding this QTL using a multi-step strategy. We first genotyped 1,334 single nucleotide polymorphisms (SNPs) in 928 individuals from families that showed evidence of linkage for BP. We then genotyped a second panel of 306 SNPs in all SHFS participants (N = 3,807) for genes that displayed the strongest evidence of association in the region, and, in a third step, included additional genotyping to better cover the genes of interest and to interrogate plausible candidate genes in the region. RESULTS: Three genes had multiple SNPs marginally associated with systolic BP (TBC1D16, HRNBP3 and AZI1). In BQTN analysis, used to estimate the posterior probability that any variant in each gene had an effect on the phenotype, AZI1 showed the most prominent findings (posterior probability of 0.66). Importantly, upon correction for multiple testing, none of our study findings could be distinguished from chance. CONCLUSION: Our findings demonstrate the difficulty of follow-up studies of linkage studies for complex traits, particularly in the context of low powered studies and rare variants underlying linkage peaks.

Genetic epidemiology of left ventricular hypertrophy.

Left ventricular (LV) hypertrophy is a strong independent predictor of increased cardiovascular morbidity and mortality in clinical and population-based samples. Clinical and hemodynamic stimuli to LV hypertrophy induce not only an increase in cardiac mass and wall thickness but also a fundamental reconfiguration of the protein, cellular and molecular components of the myocardium. Several studies have indicated that LV mass is influenced by genetic factors. The substantial heritability (h(2)) for LV mass in population-based samples of varying ethnicity indicates robust genetic influences on LV hypertrophy. Genome-wide linkage and association studies in diverse populations have been performed to identify genes influencing LV mass, and although several chromosomal regions have been found to be significantly associated with LV mass, the specific genes and functional variants contained in these chromosomal regions have yet to be identified. In addition, multiple studies have tried to link single-nucleotide polymorphisms (SNPs) in regulatory and pathway genes with common forms of LV hypertrophy, but there is little evidence that these genetic variations are functional. Up to this point in time, the results obtained in genetic studies are of limited clinical value. Much of the heritability remains unexplained, the identity of the underlying gene pathways, genes, and functional variants remains unknown, and the promise of genetically-based risk prediction and personalized medicine remain unfulfilled. However, molecular biological technologies continue to improve rapidly, and the long-term potential of sophisticated genetic investigations using these modern genomic technologies, coupled with smart study designs, remains intact. Ultimately, genetic investigations offer much promise for future prevention, early intervention and treatment of this major public health issue.

Seroprevalence of 13 common pathogens in a rapidly growing U.S. minority population: Mexican Americans from San Antonio, TX.

BACKGROUND: Infection risks vary among individuals and between populations. Here we present information on the seroprevalence of 13 common infectious agents in a San Antonio-based sample of Mexican Americans. Mexican Americans represent the largest and most rapidly growing minority population in the U.S., and they are also considered a health disparities population. METHODS: We analyzed 1227 individuals for antibody titer to Chlamydophila pneumoniae, Helicobacter pylori, Toxoplasma gondii, cytomegalovirus, Epstein-Barr virus, herpes simplex virus-1, herpes simplex virus-2 (HSV-2), human herpesvirus-6 (HHV-6), varicella zoster virus (VZV), adenovirus-36, hepatitis A virus, and influenza A and B. Seroprevalence was examined as a function of sex, age, household income, and education. RESULTS: Seroprevalence estimates ranged from 9% for T. gondii to 92% for VZV, and were similar in both sexes except for HSV-2, which was more prevalent in women. Many pathogens exhibited a significant seroprevalence change over the examined age range (15-94 years), with 7 pathogens increasing and HHV-6 decreasing with age. Socioeconomic status significantly correlated with serostatus for some pathogens. CONCLUSIONS: Our findings demonstrate substantial seroprevalence rates of these common infections in this sample of Mexican Americans from San Antonio, Texas that suffers from high rates of chronic diseases including obesity and type-2 diabetes.

Do rare variant genotypes predict common variant genotypes?

The synthetic association hypothesis proposes that common genetic variants detectable in genome-wide association studies may reflect the net phenotypic effect of multiple rare polymorphisms distributed broadly within the focal gene rather than, as often assumed, the effect of common functional variants in high linkage disequilibrium with the focal marker. In a recent study, Dickson and colleagues demonstrated synthetic association in simulations and in two well-characterized, highly polymorphic human disease genes. The converse of this hypothesis is that rare variant genotypes must be correlated with common variant genotypes often enough to make the phenomenon of synthetic association possible. Here we used the exome genotype data provided for Genetic Analysis Workshop 17 to ask how often, how well, and under what conditions rare variant genotypes predict the genotypes of common variants within the same gene. We found nominal evidence of correlation between rare and common variants in 21-30% of cases examined for unrelated individuals; this rate increased to 38-44% for related individuals, underscoring the segregation that underlies synthetic association.

Transcriptomic epidemiology of smoking: the effect of smoking on gene expression in lymphocytes.

BACKGROUND: This investigation offers insights into system-wide pathological processes induced in response to cigarette smoke exposure by determining its influences at the gene expression level. METHODS: We obtained genome-wide quantitative transcriptional profiles from 1,240 individuals from the San Antonio Family Heart Study, including 297 current smokers. Using lymphocyte samples, we identified 20,413 transcripts with significantly detectable expression levels, including both known and predicted genes. Correlation between smoking and gene expression levels was determined using a regression model that allows for residual genetic effects. RESULTS: With a conservative false-discovery rate of 5% we identified 323 unique genes (342 transcripts) whose expression levels were significantly correlated with smoking behavior. These genes showed significant over-representation within a range of functional categories that correspond well with known smoking-related pathologies, including immune response, cell death, cancer, natural killer cell signaling and xenobiotic metabolism. CONCLUSIONS: Our results indicate that not only individual genes but entire networks of gene interaction are influenced by cigarette smoking. This is the largest in vivo transcriptomic epidemiological study of smoking to date and reveals the significant and comprehensive influence of cigarette smoke, as an environmental variable, on the expression of genes. The central importance of this manuscript is to provide a summary of the relationships between gene expression and smoking in this exceptionally large cross-sectional data set.

Toward the identification of causal genes in complex diseases: a gene-centric joint test of significance combining genomic and transcriptomic data.

BACKGROUND: Gene identification using linkage, association, or genome-wide expression is often underpowered. We propose that formal combination of information from multiple gene-identification approaches may lead to the identification of novel loci that are missed when only one form of information is available. METHODS: Firstly, we analyze the Genetic Analysis Workshop 16 Framingham Heart Study Problem 2 genome-wide association data for HDL-cholesterol using a "gene-centric" approach. Then we formally combine the association test results with genome-wide transcriptional profiling data for high-density lipoprotein cholesterol (HDL-C), from the San Antonio Family Heart Study, using a Z-transform test (Stouffer's method). RESULTS: We identified 39 genes by the joint test at a conservative 1% false-discovery rate, including 9 from the significant gene-based association test and 23 whose expression was significantly correlated with HDL-C. Seven genes identified as significant in the joint test were not independently identified by either the association or expression tests. CONCLUSION: This combined approach has increased power and leads to the direct nomination of novel candidate genes likely to be involved in the determination of HDL-C levels. Such information can then be used as justification for a more exhaustive search for functional sequence variation within the nominated genes. We anticipate that this type of analysis will improve our speed of identification of regulatory genes causally involved in disease risk.