Increased immunoglobulin G (IgG) response to dietary antigens can be associated with gastrointestinal dysfunction and autoimmunity. The underlying processes contributing to these adverse reactions remain largely unknown, and it is likely that genetic factors play a role. Here we estimate heritability and attempt to localize genetic factors influencing IgG antibody levels against food-derived antigens using an integrative genomics approach. IgG antibody levels were determined by ELISA in >1300 Mexican Americans for the following food antigens: wheat gliadin; bovine casein; and two forms of bovine serum albumin (BSA-a and BSA-b). Pedigree-based variance components methods were used to estimate additive genetic heritability (h2), perform genome-wide association analyses, and identify transcriptional signatures (based on 19,858 transcripts from peripheral blood lymphocytes). Heritability estimates were significant for all traits (0.15-0.53), and shared environment (based on shared residency among study participants) was significant for casein (0.09) and BSA-a (0.33). Genome-wide significant evidence of association was obtained only for antibody to gliadin (p=8.57×10-8), mapping to the human leukocyte antigen II region, with HLA-DRA and BTNL2 as the best candidate genes. Lack of association of known celiac disease risk alleles HLA-DQ2.5 and -DQ8 with anti-gliadin antibodies in the studied population suggests a separate genetic etiology. Significant transcriptional signatures were found for all IgG levels except BSA-b. These results demonstrate that individual genetic differences contribute to food antigen antibody measures in this population. Further investigations may elucidate the underlying immunological processes involved.
IgG antibody; gliadin; integrative genomics; association; transcriptional profiling; pedigree study
Linkage studies of alcoholism have implicated several chromosome regions, leading to the successful identification of susceptibility genes, including ADH4 and GABRA2 on chromosome 4. Quantitative endophenotypes that are potentially closer to gene action than clinical endpoints offer a means of obtaining more refined linkage signals of genes that predispose alcohol use disorders (AUD). In this study we examine a self-reported measure of the maximum number of drinks consumed in a 24-hour period (abbreviated Max Drinks), a significantly heritable phenotype (h2 = 0.32 ± 0.05; P = 4.61 × 10−14) with a strong genetic correlation with AUD (ρg = 0.99 ± 0.13) for the San Antonio Family Study (n = 1,203). Genome-wide SNPs were analyzed using variance components linkage methods in the program SOLAR, revealing a novel, genome-wide significant QTL (LOD = 4.17; P = 5.85 × 10−6) for Max Drinks at chromosome 6p22.3, a region with a number of compelling candidate genes implicated in neuronal function and psychiatric illness. Joint analysis of Max Drinks and AUD status shows that the QTL has a significant non-zero effect on diagnosis (P = 4.04 × 10−3), accounting for 8.6% of the total variation. Significant SNP associations for Max Drinks were also identified at the linkage region, including one, rs7761213 (P = 2.14 × 10−4), obtained for an independent sample of Chinese families. Thus, our study identifies a potential risk locus for AUD at 6p22.3, with significant pleiotropic effects on the heaviness of alcohol consumption that may not be population specific.
AUD; alcohol dependence; variance components linkage analysis; pleiotropy; endophenotype ranking value (ERV); SNP association
It is now well established that regional indices of brain structure such as cortical thickness, surface area or grey matter volume exhibit spatially variable patterns of heritability. However, a recent study found these patterns to change with age during development, a result supported by gene expression studies. Changes in heritability have not been investigated in adulthood so far and could have important implications in the study of heritability and genetic correlations in the brain as well as in the discovery of specific genes explaining them. Herein, we tested for genotype by age (G × A) interactions, an extension of genotype by environment interactions, through adulthood and healthy aging in 902 subjects from the Genetics of Brain Structure (GOBS) study. A “jackknife” based method for the analysis of stable cortical thickness clusters (JASC) and scale selection is also introduced. Although additive genetic variance remained constant throughout adulthood, we found evidence for incomplete pleiotropy across age in the cortical thickness of paralimbic and parieto-temporal areas. This suggests that different genetic factors account for cortical thickness heritability at different ages in these regions.
Cortical Thickness; Heritability; Genotype by Age Interaction; Clustering
The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability inhuman brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.
Type 2 diabetes (T2D) is a complex metabolic disease that is more prevalent in ethnic groups such as Mexican Americans, and is strongly associated with the risk factors obesity and insulin resistance. The goal of this study was to perform whole genome gene expression profiling in adipose tissue to detect common patterns of gene regulation associated with obesity and insulin resistance. We used phenotypic and genotypic data from 308 Mexican American participants from the Veterans Administration Genetic Epidemiology Study (VAGES). Basal fasting RNA was extracted from adipose tissue biopsies from a subset of 75 unrelated individuals, and gene expression data generated on the Illumina BeadArray platform. The number of gene probes with significant expression above baseline was approximately 31,000. We performed multiple regression analysis of all probes with 15 metabolic traits. Adipose tissue had 3,012 genes significantly associated with the traits of interest (false discovery rate, FDR ≤ 0.05). The significance of gene expression changes was used to select 52 genes with significant (FDR ≤ 10-4) gene expression changes across multiple traits. Gene sets/Pathways analysis identified one gene, alcohol dehydrogenase 1B (ADH1B) that was significantly enriched (P < 10-60) as a prime candidate for involvement in multiple relevant metabolic pathways. Illumina BeadChip derived ADH1B expression data was consistent with quantitative real time PCR data. We observed significant inverse correlations with waist circumference (2.8 x 10-9), BMI (5.4 x 10-6), and fasting plasma insulin (P < 0.001). These findings are consistent with a central role for ADH1B in obesity and insulin resistance and provide evidence for a novel genetic regulatory mechanism for human metabolic diseases related to these traits.
Schizophrenia genome-wide association studies (GWAS) have identified common SNPs, rare copy number variants (CNVs) and a large polygenic contribution to illness risk, but biological mechanisms remain unclear. Bioinformatic analyses of significantly associated genetic variants point to a large role for regulatory variants. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines (LCLs) from 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate (FDR) of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, microRNA (miRNA) processing and immunity. This functional diversity is consistent with schizophrenia's likely significant pathophysiological heterogeneity. The overall enrichment of immune-related genes among those differentially expressed by affection status is consistent with hypothesized immune contributions to schizophrenia risk. The observed differential expression of extended major histocompatibility complex (xMHC) region histones (HIST1H2BD, HIST1H2BC, HIST1H2BH, HIST1H2BG and HIST1H4K) converges with the genetic evidence from GWAS, which find the xMHC to be the most significant susceptibility locus. Among the differentially expressed immune-related genes, B3GNT2 is implicated in autoimmune disorders previously tied to schizophrenia risk (rheumatoid arthritis and Graves’ disease), and DICER1 is pivotal in miRNA processing potentially linking to miRNA alterations in schizophrenia (e.g. MIR137, the second strongest GWAS finding). Our analysis provides novel candidate genes for further study to assess their potential contribution to schizophrenia.
Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency–large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10−06 (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10−10. Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.
This paper describes genetic investigations of seroreactivity to five common infectious pathogens in the Genetics of Coronary Artery Disease in Alaska Natives (GOCADAN) study. Antibody titers and seroprevalence were available for 495 to 782 (depending on the phenotype) family members at two time points, approximately 15 years apart, for Chlamydophila pneumoniae, Helicobacter pylori, cytomegalovirus (CMV), herpes simplex virus-1 (HSV-1), and herpes simplex virus-2 (HSV-2). Seroprevalence rates indicate that infections with most of these pathogens are common (>=20% for all of them, >80% for H. pylori, CMV and HSV-1). Seropositive individuals typically remain seropositive over time, with seroreversion rates of <1% to 10% over ~15 years. Antibody titers were significantly heritable for most pathogens, with the highest estimate being 0.61 for C. pneumoniae. Significant genome-wide linkage evidence was obtained for C. pneumoniae on chromosome 15 (LOD of 3.13). These results demonstrate that individual host genetic differences influence antibody measures of common infections in this population, and further investigation may elucidate the underlying immunological processes and genes involved.
Chlamydophila pneumoniae; Helicobacter pylori; cytomegalovirus; herpes simplex virus-1; herpes simplex virus-2; antibodies; heritability; linkage; Inupiaq
A significant proportion of the variability in carotid artery lumen diameter is attributable to genetic factors.
Carotid ultrasonography and genotyping were performed in the 3,300 American Indian participants in the Strong Heart Family Study (SHFS) to identify chromosomal regions harboring novel genes associated with inter-individual variation in carotid artery lumen diameter. Genome-wide linkage analysis was conducted using standard variance component linkage methods, implemented in SOLAR, based on multipoint identity-by-descent matrices.
Genome-wide linkage analysis revealed a significant evidence for linkage for a locus for left carotid artery diastolic and systolic lumen diameter in Arizona SHFS participants on chromosome 7 at 120 cM (lod=4.85 and 3.77, respectively, after sex and age adjustment, and lod=3.12 and 2.72, respectively, after adjustment for sex, age, height, weight, systolic and diastolic blood pressure, diabetes mellitus and current smoking). Other regions with suggestive evidence of linkage for left carotid artery diastolic and systolic lumen diameter was found on chromosome 12 at 153 cM (lod=2.20 and 2.60, respectively, after sex and age adjustment, and lod=2.44 and 2.16, respectively, after full covariate adjustment) in Oklahoma SHFS participants; suggestive linkage for right carotid artery diastolic and systolic lumen diameter was found on chromosome 9 at 154 cM (lod=2.72 and 3.19, respectively after sex and age adjustment, and lod=2.36 and 2.21, respectively, after full covariate adjustment) in Oklahoma SHFS participants.
We found significant evidence for loci influencing carotid artery lumen diameter on chromosome 7q and suggestive linkage on chromosomes 12q and 9q.
genetics; carotid artery; ultrasonography; linkage analysis; variance components
Statistical genetic analysis of quantitative traits in large pedigrees is a formidable computational task due to the necessity of taking the non-independence among relatives into account. With the growing awareness that rare sequence variants may be important in human quantitative variation, heritability and association study designs involving large pedigrees will increase in frequency due to the greater chance of observing multiple copies of rare variants amongst related individuals. Therefore, it is important to have statistical genetic test procedures that utilize all available information for extracting evidence regarding genetic association. Optimal testing for marker/phenotype association involves the exact calculation of the likelihood ratio statistic which requires the repeated inversion of potentially large matrices. In a whole genome sequence association context, such computation may be prohibitive. Toward this end, we have developed a rapid and efficient eigensimplification of the likelihood that makes analysis of family data commensurate with the analysis of a comparable sample of unrelated individuals. Our theoretical results which are based on a spectral representation of the likelihood yield simple exact expressions for the expected likelihood ratio test statistic (ELRT) for pedigrees of arbitrary size and complexity. For heritability, the ELRT is:
where ĥ2 and λgi are respectively the heritability and eigenvalues of the pedigree-derived genetic relationship kernel (GRK). For association analysis of sequence variants, the ELRT is given by
where ht2,hq2, and hr2 are the total, quantitative trait nucleotide, and residual heritabilities, respectively. Using these results, fast and accurate analytical power analyses are possible, eliminating the need for computer simulation. Additional benefits of eigensimplification include a simple method for calculation of the exact distribution of the ELRT under the null hypothesis which turns out to differ from that expected under the usual asymptotic theory. Further, when combined with the use of empirical GRKs—estimated over a large number of genetic markers— our theory reveals potential problems associated with non positive semi-definite kernels. These procedures are being added to our general statistical genetic computer package, SOLAR.
The role of v-ATPases in cancer biology is being increasingly recognized. Yeast studies indicate that the tyrosine-kinase inhibitor imatinib may interact with the v-ATPase genes and alter the course of cancer progression. Data from humans in this regard is lacking.
We constructed 55 lymphoblastoid cell lines from pedigreed, cancer-free human subjects and treated them with IC20 concentration of imatinib mesylate. Using these cell lines, we: i) estimated the heritability and differential expression of 19 genes encoding several subunits of the v-ATPase protein in response to imatinib treatment; ii) estimated the genetic similarity among these genes and iii) conducted a high-density scan to find cis-regulating genetic variation associated with differential expression of these genes.
We found that the imatinib response of the genes encoding v-ATPase subunits is significantly heritable and can be clustered to identify novel drug targets in imatinib therapy. Further, five of these genes were significantly cis-regulated and together represented nearly half-log fold change in response to imatinib (p = 0.0107) that was homogenous (p = 0.2598).
Our results proffer support to the growing view that personalized regimens using proton pump inhibitors or v-ATPase inhibitors may improve outcomes of imatinib therapy in various cancers.
chronic myeloid leukemia; metastasis; imatinib; microarray
Intima-media thickness (IMT) of the common and internal carotid arteries is an established surrogate for atherosclerosis and predicts risk of stroke and myocardial infarction. Often IMT is measured as the average of these two arteries, yet they are believed to result from separate biological mechanisms. The aim of this study was to conduct a family-based genome-wide association study (GWAS) for IMT to identify polymorphisms influencing IMT and to determine if distinct carotid artery segments are influenced by different genetic components.
Methods and Results
IMT for the common and internal carotid arteries was determined through B-mode ultrasound in 772 Mexican Americans from the San Antonio Family Heart Study. A GWAS utilizing 931,219 single nucleotide polymorphisms (SNPs) was undertaken with six internal and common carotid artery IMT phenotypes utilizing an additive measured genotype model. The most robust association detected was for two SNPs (rs16983261, rs6113474, p=1.60e−7) in complete linkage disequilibrium on chromosome 20p11 for the internal carotid artery near wall, next to the gene PAX1. We also replicated previously reported GWAS regions on chromosomes 19q13 and 7q22. We found no overlapping associations between internal and common carotid artery phenotypes at p<5.0e0−6. The genetic correlation between the two carotid IMT arterial segments was 0.51.
This study represents the first large scale GWAS of carotid IMT in a non-European population and identified several novel loci. We do not detect any shared GWAS signals between common and internal carotid arterial segments but the moderate genetic correlation implies both common and unique genetic components.
intima-media thickness; carotid artery; GWAS; Hispanics
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
Genetics; MRI; GWAS; Consortium; Meta-analysis; Multi-site
Increased serum uric acid (SUA) is a risk factor for gout and renal and cardiovascular disease (CVD). The purpose of this study was to identify genetic factors that affect the variation in SUA in 632 Mexican Americans participants of the San Antonio Family Heart Study (SAFHS). A genome-wide association (GWA) analysis was performed using the Illumina Human Hap 550K single nucleotide polymorphism (SNP) microarray. We used a linear regression-based association test under an additive model of allelic effect, while accounting for non-independence among family members via a kinship variance component. All analyses were performed in the software package SOLAR. SNPs rs6832439, rs13131257, and rs737267 in solute carrier protein 2 family, member 9 (SLC2A9) were associated with SUA at genome-wide significance (p < 1.3 × 10−7). The minor alleles of these SNPs had frequencies of 36.2, 36.2, and 38.2%, respectively, and were associated with decreasing SUA levels. All of these SNPs were located in introns 3–7 of SLC2A9, the location of the previously reported associations in European populations. When analyzed for association with cardiovascular-renal disease risk factors, conditional on SLC2A9 SNPs strongly associated with SUA, significant associations were found for SLC2A9 SNPs with BMI, body weight, and waist circumference (p < 1.4 × 10−3) and suggestive associations with albumin-creatinine ratio and total antioxidant status (TAS). The SLC2A9 gene encodes an urate transporter that has considerable influence on variation in SUA. In addition to the primary association locus, suggestive evidence (p < 1.9 × 10−6) for joint linkage/association (JLA) was found at a previously-reported urate quantitative trait locus (Logarithm of odds score = 3.6) on 3p26.3. In summary, our GWAS extends and confirms the association of SLC2A9 with SUA for the first time in a Mexican American cohort and also shows for the first time its association with cardiovascular-renal disease risk factors.
variance components decomposition approach; joint linkage/association analysis; kinship; hyperuricemia
Several studies have identified effects of genetic variation on DNA methylation patterns and associated heritability, with research primarily focused on Caucasian individuals. In this paper, we examine the evidence for genetic effects on DNA methylation in a Mexican American cohort, a population burdened by a high prevalence of obesity. Using an Illumina-based platform and following stringent quality control procedures, we assessed a total of 395 CpG sites in peripheral blood samples obtained from 183 Mexican American individuals for evidence of heritability, proximal genetic regulation and association with age, sex and obesity measures (i.e. waist circumference and body mass index). We identified 16 CpG sites (∼4%) that were significantly heritable after Bonferroni correction for multiple testing and 27 CpG sites (∼6.9%) that showed evidence of genetic effects. Six CpG sites (∼2%) were associated with age, primarily exhibiting positive relationships, including CpG sites in two genes that have been implicated in previous genome-wide methylation studies of age (FZD9 and MYOD1). In addition, we identified significant associations between three CpG sites (∼1%) and sex, including DNA methylation in CASP6, a gene that may respond to estradiol treatment, and in HSD17B12, which encodes a sex steroid hormone. Although we did not identify any significant associations between DNA methylation and the obesity measures, several nominally significant results were observed in genes related to adipogenesis, obesity, energy homeostasis and glucose homeostasis (ARHGAP9, CDKN2A, FRZB, HOXA5, JAK3, MEST, NPY, PEG3 and SMARCB1). In conclusion, we were able to replicate several findings from previous studies in our Mexican American cohort, supporting an important role for genetic effects on DNA methylation. In addition, we found a significant influence of age and sex on DNA methylation, and report on trend-level, novel associations between DNA methylation and measures of obesity.
Individual differences in biological ageing (i.e., the rate of physiological response to the passage of time) may be due in part to genotype-specific variation in gene action. However, the sources of heritable variation in human age-related gene expression profiles are largely unknown. We have profiled genome-wide expression in peripheral blood mononuclear cells from 1,240 individuals in large families and found 4,472 human autosomal transcripts, representing ~4,349 genes, significantly correlated with age. We identified 623 transcripts that show genotype by age interaction in addition to a main effect of age, defining a large set of novel candidates for characterization of the mechanisms of differential biological ageing. We applied a novel SNP genotype×age interaction test to one of these candidates, the ubiquilin-like gene UBQLNL, and found evidence of joint cis-association and genotype by age interaction as well as trans-genotype by age interaction for UBQLNL expression. Both UBQLNL expression levels at recruitment and cis genotype are associated with longitudinal cancer risk in our study cohort.
Transcriptional ageing; genotype by age interaction; ubiquitins; UBQLNL; cancer risk gene
Whole-transcriptome expression profiling provides novel phenotypes for analysis of complex traits. Gene expression measurements reflect quantitative variation in transcript-specific messenger RNA levels and represent phenotypes lying close to the action of genes. Understanding the genetic basis of gene expression will provide insight into the processes that connect genotype to clinically significant traits representing a central tenet of system biology. Synchronous in vivo expression profiles of lymphocytes, muscle, and subcutaneous fat were obtained from healthy Mexican men. Most genes were expressed at detectable levels in multiple tissues, and RNA levels were correlated between tissue types. A subset of transcripts with high reliability of expression across tissues (estimated by intraclass correlation coefficients) was enriched for cis-regulated genes, suggesting that proximal sequence variants may influence expression similarly in different cellular environments. This integrative global gene expression profiling approach is proving extremely useful for identifying genes and pathways that contribute to complex clinical traits. Clearly, the coincidence of clinical trait quantitative trait loci and expression quantitative trait loci can help in the prioritization of positional candidate genes. Such data will be crucial for the formal integration of positional and transcriptomic information characterized as genetical genomics.
Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer’s disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).
Metabolic syndrome (MetS) is an aberration associated with increased risk for cancer and inflammation. Adiponectin, an adipocyte-produced abundant protein hormone, has countering effect on the diabetogenic and atherogenic components of MetS. Plasma levels of adiponectin are negatively correlated with onset of cancer and cancer patient mortality. We previously performed microsatellite linkage analyses using adiponectin as a surrogate marker and revealed two QTLs on chr5 (5p14) and chr14 (14q13).
Using individuals from 85 extended families that contributed to the linkage and who were measured for 42 clinical and biologic MetS phenotypes, we tested QTL-based SNP associations, peripheral white blood cell (PWBC) gene expression, and the effects of cis-acting SNPs on gene expression to discover genomic elements that could affect the pathophysiology and complications of MetS.
Adiponectin levels were found to be highly intercorrelated phenotypically with the majority of MetS traits. QTL-specific haplotype-tagging SNPs associated with MetS phenotypes were annotated to 14 genes whose function could influence MetS biology as well as oncogenesis or inflammation. These were mechanistically categorized into four groups: cell-cell adhesion and mobility, signal transduction, transcription and protein sorting. Four genes were highly prioritized: cadherin 18 (CDH18), myosin X (MYO10), anchor protein 6 of AMPK (AKAP6), and neuronal PAS domain protein 3 (NPAS3). PWBC expression was detectable only for the following genes with multi-organ or with multi-function properties: NPAS3, MARCH6, MYO10 and FBXL7. Strong evidence of cis-effects on the expression of MYO10 in PWBC was found with SNPs clustered near the gene’s transcription start site. MYO10 expression in PWBC was marginally correlated with body composition (p= 0.065) and adipokine levels in the periphery (p = 0.064). Variants of genes AKAP6, NPAS3, MARCH6 and FBXL7 have been previously reported to be associated with insulin resistance, inflammatory markers or adiposity studies using genome-wide approaches whereas associations of CDH18 and MYO10 with MetS traits have not been reported before.
Adiponectin QTLs-based SNP association and mRNA expression identified genes that could mediate the association between MetS and cancer or inflammation.
Adiponectin; Metabolic syndrome; Cancer risk; Inflammation
Infection with Epstein-Barr virus (EBV) is highly prevalent worldwide, and it has been associated with infectious mononucleosis and severe diseases including Burkitt lymphoma, Hodgkin lymphoma, nasopharyngeal lymphoma, and lymphoproliferative disorders. Although EBV has been the focus of extensive research, much still remains unknown concerning what makes some individuals more sensitive to infection and to adverse outcomes as a result of infection. Here we use an integrative genomics approach in order to localize genetic factors influencing levels of Epstein Barr virus (EBV) nuclear antigen-1 (EBNA-1) IgG antibodies, as a measure of history of infection with this pathogen, in large Mexican American families. Genome-wide evidence of both significant linkage and association was obtained on chromosome 6 in the human leukocyte antigen (HLA) region and replicated in an independent Mexican American sample of large families (minimum p-value in combined analysis of both datasets is 1.4×10−15 for SNPs rs477515 and rs2516049). Conditional association analyses indicate the presence of at least two separate loci within MHC class II, and along with lymphocyte expression data suggest genes HLA-DRB1 and HLA-DQB1 as the best candidates. The association signals are specific to EBV and are not found with IgG antibodies to 12 other pathogens examined, and therefore do not simply reveal a general HLA effect. We investigated whether SNPs significantly associated with diseases in which EBV is known or suspected to play a role (namely nasopharyngeal lymphoma, Hodgkin lymphoma, systemic lupus erythematosus, and multiple sclerosis) also show evidence of associated with EBNA-1 antibody levels, finding an overlap only for the HLA locus, but none elsewhere in the genome. The significance of this work is that a major locus related to EBV infection has been identified, which may ultimately reveal the underlying mechanisms by which the immune system regulates infection with this pathogen.
Many factors influence individual differences in susceptibility to infectious disease, including genetic factors of the host. Here we use several genome-wide investigative tools (linkage, association, joint linkage and association, and the analysis of gene expression data) to search for host genetic factors influencing Epstein-Barr virus (EBV) infection. EBV is a human herpes virus that infects up to 90% of adults worldwide, infection with which has been associated with severe complications including malignancies and autoimmune disorders. In a sample of >1,300 Mexican American family members, we found significant evidence of association of anti–EBV antibody levels with loci on chromosome 6 in the human leukocyte antigen region, which contains genes related to immune function. The top two independent loci in this region were HLA-DRB1 and HLA-DQB1, both of which are involved in the presentation of foreign antigens to T cells. This finding was specific to EBV and not to 12 other pathogens we examined. We also report an overlap of genetic factors influencing both EBV antibody level and EBV–related cancers and autoimmune disorders. This work demonstrates the presence of EBV susceptibility loci and provides impetus for further investigation to better understand the underlying mechanisms related to differences in disease progression among individuals infected with this pathogen.
nuclear factor kappa B; gene expression network; principal components factor analysis; linkage analysis; systems genetics
A large number of genome-wide association studies have been performed during the past five years to identify associations between SNPs and human complex diseases and traits. The assignment of a functional role for the identified disease-associated SNP is not straight-forward. Genome-wide expression quantitative trait locus (eQTL) analysis is frequently used as the initial step to define a function while allele-specific gene expression (ASE) analysis has not yet gained a wide-spread use in disease mapping studies. We compared the power to identify cis-acting regulatory SNPs (cis-rSNPs) by genome-wide allele-specific gene expression (ASE) analysis with that of traditional expression quantitative trait locus (eQTL) mapping. Our study included 395 healthy blood donors for whom global gene expression profiles in circulating monocytes were determined by Illumina BeadArrays. ASE was assessed in a subset of these monocytes from 188 donors by quantitative genotyping of mRNA using a genome-wide panel of SNP markers. The performance of the two methods for detecting cis-rSNPs was evaluated by comparing associations between SNP genotypes and gene expression levels in sample sets of varying size. We found that up to 8-fold more samples are required for eQTL mapping to reach the same statistical power as that obtained by ASE analysis for the same rSNPs. The performance of ASE is insensitive to SNPs with low minor allele frequencies and detects a larger number of significantly associated rSNPs using the same sample size as eQTL mapping. An unequivocal conclusion from our comparison is that ASE analysis is more sensitive for detecting cis-rSNPs than standard eQTL mapping. Our study shows the potential of ASE mapping in tissue samples and primary cells which are difficult to obtain in large numbers.
Antibodies against infectious pathogens provide information on past or present exposure to infectious agents. While host genetic factors are known to affect the immune response, the influence of genetic factors on antibody levels to common infectious agents is largely unknown. Here we test whether antibody levels for 13 common infections are significantly heritable.
IgG antibodies to Chlamydophila pneumoniae, Helicobacter pylori, Toxoplasma gondii, adenovirus 36 (Ad36), hepatitis A virus, influenza A and B, cytomegalovirus, Epstein-Barr virus, herpes simplex virus (HSV)-1 and −2, human herpesvirus-6, and varicella zoster virus were determined for 1,227 Mexican Americans. Both quantitative and dichotomous (seropositive/seronegative) traits were analyzed. Influences of genetic and shared environmental factors were estimated using variance components pedigree analysis, and sharing of underlying genetic factors among traits was investigated using bivariate analyses.
Serological phenotypes were significantly heritable for most pathogens (h2 = 0.17–0.39), except for Ad36 and HSV-2. Shared environment was significant for several pathogens (c2 = 0.10–0.32). The underlying genetic etiology appears to be largely different for most pathogens.
Our results demonstrate, for the first time for many of these pathogens, that individual genetic differences of the human host contribute substantially to antibody levels to many common infectious agents, providing impetus for the identification of underlying genetic variants, which may be of clinical importance.
Pathogen; Infection; Antibody; Serology; Genetics; Heritability; Mexican Americans
A decade ago, there was widespread enthusiasm for the prospects of genome-wide association studies to identify common variants related to common chronic diseases using samples of unrelated individuals from populations. Although technological advancements allow us to query more than a million SNPs across the genome at low cost, a disappointingly small fraction of the genetic portion of common disease etiology has been uncovered. This has led to the hypothesis that less frequent variants might be involved, stimulating a renaissance of the traditional approach of seeking genes using multiplex families from less diverse populations. However, by using the modern genotyping and sequencing technology, we can now look not just at linkage, but jointly at linkage and linkage disequilibrium (LD) in such samples. Software methods that can look simultaneously at linkage and LD in a powerful and robust manner have been lacking. Most algorithms cannot jointly analyze datasets involving families of varying structures in a statistically or computationally efficient manner. We have implemented previously proposed statistical algorithms in a user-friendly software package, PSEUDOMARKER. This paper is an announcement of this software package. We describe the motivation behind the approach, the statistical methods, and software, and we briefly demonstrate PSEUDOMARKER's advantages over other packages by example.
Computer software; Family-based association; Genome-wide association; Likelihood methods; Linkage analysis; Linkage disequilibrium; Study design
Imatinib mesylate is currently the drug of choice to treat chronic myeloid leukemia. However, patient resistance and cytotoxicity make secondary lines of treatment, such as omacetaxine mepesuccinate, a necessity. Given that drug cytotoxicity represents a major problem during treatment, it is essential to understand the biological pathways affected to better predict poor drug response and prioritize a treatment regime.
We conducted cell viability and gene expression assays to determine heritability and gene expression changes associated with imatinib and omacetaxine treatment of 55 non-cancerous lymphoblastoid cell lines, derived from 17 pedigrees. In total, 48,803 transcripts derived from Illumina Human WG-6 BeadChips were analyzed for each sample using SOLAR, whilst correcting for kinship structure.
Cytotoxicity within cell lines was highly heritable following imatinib treatment (h2 = 0.60-0.73), but not omacetaxine treatment. Cell lines treated with an IC20 dose of imatinib or omacetaxine showed differential gene expression for 956 (1.96%) and 3,892 transcripts (7.97%), respectively; 395 of these (0.8%) were significantly influenced by both imatinib and omacetaxine treatment. k-means clustering and DAVID functional annotation showed expression changes in genes related to kinase binding and vacuole-related functions following imatinib treatment, whilst expression changes in genes related to cell division and apoptosis were evident following treatment with omacetaxine. The enrichment scores for these ontologies were very high (mostly >10).
Induction of gene expression changes related to different pathways following imatinib and omacetaxine treatment suggests that the cytotoxicity of such drugs may be differentially tolerated by individuals based on their genetic background.
Chronic myeloid leukemia; Microarray; Toxicity; Gene expression; Imatinib; Omacetaxine