Copy number variations (CNVs) and polymorphisms (CNPs) have only recently gained the genetic community's attention. Conservative estimates have shown that CNVs and CNPs might affect more than 10% of the genome and that they may be at least as important as single nucleotide polymorphisms in assessing human variability. Widely used tools for CNP analysis have been implemented in Birdsuite and PLINK for the purpose of conducting genetic association studies based on the unpartitioned total number of CNP copies provided by the intensities from Affymetrix's Genome-Wide Human SNP Array. Here, we are interested in partitioning copy number variations and polymorphisms in extended pedigrees for the purpose of linkage analysis on familial data.
We have developed CNGen, a new software for the partitioning of copy number polymorphism using the integrated genotypes from Birdsuite with the Affymetrix platform. The algorithm applied to familial trios or extended pedigrees can produce partitioned copy number genotypes with distinct parental alleles. We have validated the algorithm using simulations on a complex pedigree structure using frequencies calculated from a real dataset of 300 genotyped samples from 42 pedigrees segregating a congenital heart defect phenotype.
CNGen is the first published software for the partitioning of copy number genotypes in pedigrees, making possible the use CNPs and CNVs for linkage analysis. It was implemented with the Python interpreter version 2.5.2. It was successfully tested on current Linux, Windows and Mac OS workstations.
Knowledge of simulated genetic effects facilitates interpretation of methodological studies. Genetic interactions for common disorders are likely numerous and weak. Using the 200 replicates of the Genetic Analysis Workshop 16 (GAW16) Problem 3 simulated data, we compared the statistical power to detect weak gene-gene interactions using a haplotype-based test in the UNPHASED software with genotypic mixed model (GMM) and additive mixed model (AMM) mixed linear regression model in SAS. We assumed a candidate-gene approach where a single-nucleotide polymorphism (SNP) in one gene is fixed and multiple SNPs are at the second gene. We analyzed the quantitative low-density lipoprotein trait (heritability 0.7%), modulated by simulated interaction of rs4648068 from 4q24 and another gene on 8p22, where we analyzed seven SNPs. We generally observed low power calculated per SNP (≤ 37% at the 0.05 level), with the haplotype-based test being inferior. Over all tests, the haplotype-based test performed within chance, while GMM and AMM had low power (~10%). The haplotype-based and mixed models detected signals at different SNPs. The haplotype-based test detected a signal in 50 unique replicates; GMM and AMM featured both shared and distinct SNPs and replicates (65 replicates shared, 41 GMM, 27 AMM). Overall, the statistical signal for the weak gene-gene interaction appears sensitive to the sample structure of the replicates. We conclude that using more than one statistical approach may increase power to detect such signals in studies with limited number of loci such as replications. There were no results significant at the conservative 10-7 genome-wide level.
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
generalized linear model; machine learning methods
Large genetic association studies based on hundreds of thousands of single-nucleotide polymorphisms (SNPs) are a popular option for the study of complex diseases. The evaluation of gene × gene interactions in such studies is a sensible method of capturing important genetic effects. The number of tests required to consider all pairs of SNPs, however, can lead to a computational burden, and efficient strategies to reduce the number of tests performed are desirable. In this study, we compare two-stage strategies for pairwise SNP interactions testing. Those approaches rely on the selection of SNPs based on the single-locus test results obtained at the first stage. In the simultaneous approach, SNPs that fall below the marginal significance thresholds (p = 0.05 and p = 0.1) in stage 1 are selected and tested for within-group pairwise interaction in stage 2. With the conditional approach, SNPs that reach Bonferroni-adjusted significance at the first stage are tested in pairwise combinations with all SNPs in the data set. We compared the performance of those strategies by using Replicate 1 of the simulated data set of the Genetic Analysis Workshop 15 Problem 3. Most interactions detected resulted from SNP pairs within 1000 kb of each other. The remaining were false positives involving SNPs with excessively strong marginal signals. Our results highlight the need to account for locus proximity in the evaluation of interaction effects and emphasize the importance of marginal signal strength in logistic regression-based interaction modeling. We found that modeling additive genetic effects alone was sufficient to capture underlying dominance interaction effects in the data.
Summary: Genetic association studies making use of high-throughput genotyping arrays need to process large amounts of data in the order of millions of markers per experiment. The first step of any analysis with genotyping arrays is typically the conduct of a thorough data clean up and quality control to remove poor quality genotypes and generate metrics to inform and select individuals for downstream statistical analysis. We have developed pyGenClean, a bioinformatics tool to facilitate and standardize the genetic data clean up pipeline with genotyping array data. In conjunction with a source batch-queuing system, the tool minimizes data manipulation errors, accelerates the completion of the data clean up process and provides informative plots and metrics to guide decision making for statistical analysis.
Availability and implementation:
pyGenClean is an open source Python 2.7 software and is freely available, along with documentation and examples, from http://www.statgen.org.
email@example.com or firstname.lastname@example.org
Nanophthalmos is a rare genetic ocular disorder in which the eyes of affected individuals are abnormally small. Patients suffer from severe hyperopia as a result of their markedly reduced axial lengths, but otherwise are capable of seeing well unlike other more general forms of microphthalmia. To date one gene for nanophthalmos has been identified, encoding the membrane-type frizzled related protein MFRP. Identification of additional genes for nanophthalmos will improve our understanding of normal developmental regulation of eye growth.
We ascertained a cohort of families from eastern Canada and Mexico with familial nanophthalmos. We performed high density microsatellite and high density single nucleotide polymorphism (SNP) genotyping to identify potential chromosomal regions of linkage. We sequenced coding regions of genes in the linked interval by traditional PCR-based Sanger capillary electrophoresis methods. We cloned and sequenced a novel cDNA from a putative causal gene to verify gene structure.
We identified a linked locus on chromosome 2q37 with a peak logarithm (base 10) of odds (LOD) score of 4.7. Sequencing of coding exons of all genes in the region identified multiple segregating variants in one gene, recently annotated as serine protease gene (PRSS56), coding for a predicted trypsin serine protease-like protein. One of our families was homozygous for a predicted pathogenic missense mutation, one family was compound heterozygous for two predicted pathogenic missense mutations, and one family was compound heterozygous for a predicted pathogenic missense mutation plus a frameshift leading to obligatory truncation of the predicted protein. The PRSS56 gene structure in public databases is based on a virtual transcript assembled from overlapping incomplete cDNA clones; we have now validated the structure of a full-length transcript from embryonic mouse brain RNA.
PRSS56 is a good candidate for the causal gene for nanophthalmos in our families.
The Electronic Medical Record (EMR) is a potential source for high throughput phenotyping to conduct genome-wide association studies (GWAS), including those of medically relevant quantitative traits. We describe use of the Mayo Clinic EMR to conduct a GWAS of red blood cell (RBC) traits in a cohort of patients with peripheral arterial disease (PAD) and controls without PAD.
Methodology and Principal Findings
Results for hemoglobin level, hematocrit, RBC count, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration were extracted from the EMR from January 1994 to September 2009. Out of 35,159 RBC trait values in 3,411 patients, we excluded 12,864 values in 1,165 patients that had been measured during hospitalization or in the setting of hematological disease, malignancy, or use of drugs that affect RBC traits, leaving a final genotyped sample of 3,012, 80% of whom had ≥2 measurements. The median of each RBC trait was used in the genetic analyses, which were conducted using an additive model that adjusted for age, sex, and PAD status. We identified four genomic loci that were associated (P<5×10−8) with one or more of the RBC traits (HBLS1/MYB on 6q23.3, TMPRSS6 on 22q12.3, HFE on 6p22.1, and SLC17A1 on 6p22.2). Three of these loci (HBLS1/MYB, TMPRSS6, and HFE) had been identified in recent GWAS and the allele frequencies, effect sizes, and the directions of effects of the replicated SNPs were similar to the prior studies.
Our results demonstrate feasibility of using the EMR to conduct high throughput genomic studies of medically relevant quantitative traits.
To conduct a candidate gene study focusing on key elements of the inflammation, platelet aggregation, endothelial function and omega-3 and –6 fatty acid metabolism pathways to identify genetic predictors of depressive symptoms in cardiac patients.
Numerous studies suggest that the prevalence of depression is greater among cardiac patients than in the general population. Although several biological mechanisms have been proposed to account for this effect, little attention has been paid to the possibility of genetic contributions to depressive symptoms in cardiac patients.
Over 700 single nucleotide polymorphisms were successfully genotyped on 17 different chromosomes in 59 genes among 977 cardiac patients of French-Canadian descent, all of whom had completed the Beck Depression Inventory – II (BDI-II).
One SNP, rs216873, within the von Willebrand factor gene (VWF) was significantly associated with BDI – II scores following statistical correction for multiple comparisons. Several additional SNPs related to endothelial dysfunction, platelet aggregation, inflammation and/or previously associated with depression in the literature were identified as suggestive of association (p values < 0.01).
These results suggest that genetic variation related to endothelial dysfunction is predictive of depressive symptoms in cardiac patients and that endothelial dysfunction may be a novel mechanism contributing to depressive symptoms in this patient population.
Genetics; coronary disease; depression; endothelium
In families segregating a monogenic genetic disorder with a single disease gene introduction, patients share a mutation-carrying chromosomal interval with identity-by-descent (IBD). Such a shared chromosomal interval or haplotype, surrounding the actual pathogenic mutation, is typically detected and defined by multipoint linkage and phased haplotype analysis using microsatellite or SNP genotype data. High-density SNP genotype data presents a computational challenge for conventional genetic analyses. A novel non-parametric method termed Homozygosity Haplotype (HH) was recently proposed for the genome-wide search of the autosomal segments shared among patients using high density SNP genotype data.
The applicability and the effectiveness of HH in identifying the potential linkage of disease causative gene with high-density SNP genotype data were studied with a series of monogenic disorders ascertained in eastern Canadian populations. The HH approach was validated using the genotypes of patients from a family affected with a rare autosomal dominant disease Schnyder crystalline corneal dystrophy. HH accurately detected the ∼1 Mb genomic interval encompassing the causative gene UBIAD1 using the genotypes of only four affected subjects. The successful application of HH to identify the potential linkage for a family with pericentral retinal disorder indicates that HH can be applied to perform family-based association analysis by treating affected and unaffected family members as cases and controls respectively. A new strategy for the genome-wide screening of known causative genes or loci with HH was proposed, as shown the applications to a myoclonus dystonia and a renal failure cohort.
Our study of the HH approach demonstrates that HH is very efficient and effective in identifying potential disease linked region. HH has the potential to be used as an efficient alternative approach to sequencing or microsatellite-based fine mapping for screening the known causative genes in genetic disease study.
In this summary paper, we describe the contributions included in the Multistage Design group (Group 14) at the Genetic Analysis Workshop 15, which was held during November 12-14, 2006. Our group contrasted and compared different approaches to reducing complexity in a genetic study through implementation of staged designs. Most groups used the simulated dataset (problem 3), which provided ample opportunities for evaluating various staged designs. A wide range of multistage designs that targeted different aspects of complexity were explored. We categorized these approaches as reducing phenotypic complexity, model complexity, analytic complexity or genetic complexity. In general we learned that: (1) when staged designs are carefully planned and implemented, the power loss compared to a single-stage analysis can be minimized and study cost is greatly reduced; (2) a joint analysis of the results from each stage is generally more powerful than treating the second stage as a replication analysis.
two-stage study design; replication; joint analysis; statistical power; genetic association
Skewing of X chromosome inactivation (XCI) can occur in normal females and increases in tissues with age. The mechanisms underlying skewing in normal females, however, remain controversial. To better understand the phenomenon of XCI in nondisease states, we evaluated XCI patterns in epithelial and hematopoietic cells of over 500 healthy female mother-neonate pairs. The incidence of skewing observed in mothers was twice that observed in neonates, and in both cohorts, the incidence of XCI was lower in epithelial cells than hematopoietic cells. These results suggest that XCI incidence varies by tissue type and that age-dependent mechanisms can influence skewing in both epithelial and hematopoietic cells. In both cohorts, a correlation was identified in the direction of skewing in epithelial and hematopoietic cells, suggesting common underlying skewing mechanisms across tissues. However, there was no correlation between the XCI patterns of mothers and their respective neonates, and skewed mothers gave birth to skewed neonates at the same frequency as nonskewed mothers. Taken together, our data suggest that in humans, the XCI pattern observed at birth does not reflect a single heritable genetic locus, but rather corresponds to a complex trait determined, at least in part, by selection biases occurring after XCI.
Schnyder crystalline corneal dystrophy (SCCD, MIM 121800) is a rare autosomal dominant disease characterized by progressive opacification of the cornea resulting from the local accumulation of lipids, and associated in some cases with systemic dyslipidemia. Although previous studies of the genetics of SCCD have localized the defective gene to a 1.58 Mbp interval on chromosome 1p, exhaustive sequencing of positional candidate genes has thus far failed to reveal causal mutations. We have ascertained a large multigenerational family in Nova Scotia affected with SCCD in which we have confirmed linkage to the same general area of chromosome 1. Intensive fine mapping in our family revealed a 1.3 Mbp candidate interval overlapping that previously reported. Sequencing of genes in our interval led to the identification of five putative causal mutations in gene UBIAD1, in our family as well as in four other small families of various geographic origins. UBIAD1 encodes a potential prenyltransferase, and is reported to interact physically with apolipoprotein E. UBIAD1 may play a direct role in intracellular cholesterol biochemistry, or may prenylate other proteins regulating cholesterol transport and storage.
Evidence of clinical utility is a key issue in translating pharmacogenomics into clinical practice. Appropriately designed randomized controlled trials generally provide the most robust evidence of the clinical utility, but often only data from a pharmacogenomic association study are available. This paper details a method for reframing the results of pharmacogenomic association studies in terms of the comparative treatment effect for a pharmacogenomic subgroup to provide greater insight into the likely clinical utility of a pharmacogenomic marker, its’ likely cost effectiveness, and the value of undertaking the further (often expensive) research required for translation into clinical practice. The method is based on the law of total probability, which relates marginal and conditional probability. It takes as inputs: the prevalence of the pharmacogenomic marker in the patient group of interest, prognostic effect of the pharmacogenomic marker based on observational association studies, and the unstratified comparative treatment effect based on one or more conventional randomized controlled trials. The critical assumption is that of exchangeability across the included studies. The method is demonstrated using a case study of cytochrome P450 (CYP) 2C19 genotype and the anti-platelet agent clopidogrel. Indirect subgroup analysis provided insight into relationship between the clinical utility of genotyping CYP2C19 and the risk ratio of cardiovascular outcomes between CYP2C19 genotypes for individuals using clopidogrel. In this case study the indirect and direct estimates of the treatment effect for the cytochrome P450 2C19 subgroups were similar. In general, however, indirect estimates are likely to have substantially greater risk of bias than an equivalent direct estimate.
For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.
Circulating lipids levels, as well as several familial lipid metabolism disorders, are strongly associated with initiation and progression of atherosclerosis and incidence of myocardial infarction (MI).
We hypothesized that genetic variants associated with circulating lipid levels would also be associated with MI incidence, and have tested this in three independent samples.
Setting and Subjects
Using age- and sex-adjusted additive genetic models, we analyzed 554 single nucleotide polymorphisms (SNPs) in 41 candidate gene regions proposed to be involved in lipid-related pathways potentially predisposing to incidence of MI in 2,602 participants of the Swedish Twin Register (STR; 57% women). All associations with nominal P<0.01 were further investigated in the Uppsala Longitudinal Study of Adult Men (ULSAM; N = 1,142).
In the present study, we report associations of lipid-related SNPs with incident MI in two community-based longitudinal studies with in silico replication in a meta-analysis of genome-wide association studies. Overall, there were 9 SNPs in STR with nominal P-value <0.01 that were successfully genotyped in ULSAM. rs4149313 located in ABCA1 was associated with MI incidence in both longitudinal study samples with nominal significance (hazard ratio, 1.36 and 1.40; P-value, 0.004 and 0.015 in STR and ULSAM, respectively). In silico replication supported the association of rs4149313 with coronary artery disease in an independent meta-analysis including 173,975 individuals of European descent from the CARDIoGRAMplusC4D consortium (odds ratio, 1.03; P-value, 0.048).
rs4149313 is one of the few amino acid changing variants in ABCA1 known to associate with reduced cholesterol efflux. Our results are suggestive of a weak association between this variant and the development of atherosclerosis and MI.
Left-sided congenital heart disease (CHD) encompasses a spectrum of malformations that range from bicuspid aortic valve to hypoplastic left heart syndrome. It contributes significantly to infant mortality and has serious implications in adult cardiology. Although left-sided CHD is known to be highly heritable, the underlying genetic determinants are largely unidentified. In this study, we sought to determine the impact of structural genomic variation on left-sided CHD and compared multiplex families (464 individuals with 174 affecteds (37.5%) in 59 multiplex families and 8 trios) to 1,582 well-phenotyped controls. 73 unique inherited or de novo CNVs in 54 individuals were identified in the left-sided CHD cohort. After stringent filtering, our gene inventory reveals 25 new candidates for LS-CHD pathogenesis, such as SMC1A, MFAP4, and CTHRC1, and overlaps with several known syndromic loci. Conservative estimation examining the overlap of the prioritized gene content with CNVs present only in affected individuals in our cohort implies a strong effect for unique CNVs in at least 10% of left-sided CHD cases. Enrichment testing of gene content in all identified CNVs showed a significant association with angiogenesis. In this first family-based CNV study of left-sided CHD, we found that both co-segregating and de novo events associate with disease in a complex fashion at structural genomic level. Often viewed as an anatomically circumscript disease, a subset of left-sided CHD may in fact reflect more general genetic perturbations of angiogenesis and/or vascular biology.
Congenital heart disease (CHD) is the leading malformation among all newborns, and one of the leading causes of morbidity and mortality in Western countries. Left-sided CHD (LS-CHD) encompasses a spectrum ranging from bicuspid aortic valve to aortic stenosis and hypoplastic left heart syndrome with familial clustering. To date, the genetic causes for LS-CHD remain unknown in the majority of patients. To determine the impact of structural genomic variation in multiplex families with LS-CHD, we searched for unique or rare copy number variants present only in affected members of a multiplex family cohort (N total = 464, N affected members = 174 (37.5%)) and absent from 1,582 controls free from LS-CHD. A stringent filter based on in silico prioritization and gene expression analysis during development allowed us to identify genes associated with LS-CHD. Our study revealed 25 new candidate genes for LS-CHD, such as SMC1A, MFAP4, and CTHRC1, and overlap with known syndromic loci. We estimate that unique copy number variants contribute to at least 10% of left-sided CHD cases, with a gene content suggesting broader perturbations of angiogenesis at the base of LS-CHD.
To date, few mutations are described to underlie highly-elevated HDLc levels in families. Here we sequenced the coding regions and adjacent sequence of the LIPG, CETP, and GALNT2 genes in 171 unrelated Dutch Caucasian probands with HDLc≥90th percentile and analyzed segregation of mutations with lipid phenotypes in family members. In these probands, mutations were most frequent in LIPG (12.9%) followed by GALNT2 (2.3%) and CETP (0.6%). A total of 6 of 10 mutations in these three genes were novel (60.0%), and mutations segregated with elevated HDLc in families. Interestingly, the LIPG mutations N396S and R476W, which usually result in elevated HDLc, were unexpectedly found in 6 probands with low HDLc (i.e., ≤10th percentile). However, 5 of these probands also carried mutations in ABCA1, LCAT, or LPL. Finally, no CETP and GALNT2 mutations were found in 136 unrelated probands with low HDLc. Taken together, we show that rare coding and splicing mutations in LIPG, CETP, and GALNT2 are enriched in persons with hyperalphalipoproteinemia and segregate with elevated HDLc in families. Moreover, LIPG mutations do not overcome low HDLc in individuals with ABCA1 and possibly LCAT and LPL mutations, indicating that LIPG affects HDLc levels downstream of these proteins.
Oxidative stress related genes modify the effects of ambient air pollution or tobacco smoking on lung function decline. The impact of interactions might be substantial, but previous studies mostly focused on main effects of single genes.
We studied the interaction of both exposures with a broad set of oxidative-stress related candidate genes and pathways on lung function decline and contrasted interactions between exposures.
For 12679 single nucleotide polymorphisms (SNPs), change in forced expiratory volume in one second (FEV1), FEV1 over forced vital capacity (FEV1/FVC), and mean forced expiratory flow between 25 and 75% of the FVC (FEF25-75) was regressed on interval exposure to particulate matter <10 µm in diameter (PM10) or packyears smoked (a), additive SNP effects (b), and interaction terms between (a) and (b) in 669 adults with GWAS data. Interaction p-values for 152 genes and 14 pathways were calculated by the adaptive rank truncation product (ARTP) method, and compared between exposures. Interaction effect sizes were contrasted for the strongest SNPs of nominally significant genes (pinteraction<0.05). Replication was attempted for SNPs with MAF>10% in 3320 SAPALDIA participants without GWAS.
On the SNP-level, rs2035268 in gene SNCA accelerated FEV1/FVC decline by 3.8% (pinteraction = 2.5×10−6), and rs12190800 in PARK2 attenuated FEV1 decline by 95.1 ml pinteraction = 9.7×10−8) over 11 years, while interacting with PM10. Genes and pathways nominally interacting with PM10 and packyears exposure differed substantially. Gene CRISP2 presented a significant interaction with PM10 (pinteraction = 3.0×10−4) on FEV1/FVC decline. Pathway interactions were weak. Replications for the strongest SNPs in PARK2 and CRISP2 were not successful.
Consistent with a stratified response to increasing oxidative stress, different genes and pathways potentially mediate PM10 and tobacco smoke effects on lung function decline. Ignoring environmental exposures would miss these patterns, but achieving sufficient sample size and comparability across study samples is challenging.
Circulating cell-free DNA (cf-DNA) is a useful indicator of cell death, and it can also be used to predict outcomes in various clinical disorders. Several innate immune mechanisms are known to be involved in eliminating DNA and chromatin-related material as part of the inhibition of potentially harmful autoimmune responses. However, the exact molecular mechanism underlying the clearance of circulating cf-DNA is currently unclear.
To examine the mechanisms controlling serum levels of cf-DNA, we carried out a genome-wide association analysis (GWA) in a cohort of young adults (aged 24–39 years; n = 1841; 1018 women and 823 men) participating in the Cardiovascular Risk in Young Finns Study. Genotyping was performed with a custom-built Illumina Human 670 k BeadChip. The Quant-iTTM high sensitivity DNA assay was used to measure cf-DNA directly from serum.
The results revealed that 110 single nucleotide polymorphisms (SNPs) were associated with serum cf-DNA with genome-wide significance (p<5×10−8). All of these significant SNPs were localised to chromosome 2q37, near the UDP-glucuronosyltransferase 1 (UGT1) family locus, and the most significant SNPs localised within the UGT1 polypeptide A1 (UGT1A1) gene region.
The UGT1A1 enzyme catalyses the detoxification of several drugs and the turnover of many xenobiotic and endogenous compounds by glucuronidating its substrates. These data indicate that UGT1A1-associated processes are also involved in the regulation of serum cf-DNA concentrations.
Elevated serum IL-6 level is a risk factor for coronary heart disease (CHD). The −174G>C and −572G>C polymorphisms in the IL-6 gene have previously been shown to modulate IL-6 levels. But the association between the −174G>C and −572G>C polymorphisms and the risk of CHD is still unclear. A meta-analysis of all eligible studies was carried out to clarify the role of IL-6 gene polymorphisms in CHD.
Methods and Results
PubMed, EMBASE, Vip, CNKI and CBM-disc were searched for eligible articles in English and Chinese that were published before October 2010. 27 studies involving 11580 patients with CHD and 17103 controls were included. A meta-analysis was performed for the included articles using the RevMan 5.0 and Stata 10.0 softwares. Overall, the −174C allele was not significantly associated with CHD risk (ORs = 1.04, 95%CI = 0.98 to 1.10) when compared with the −174G allele in the additive model, and meta-analysis under other genetic models (dominant, recessive, CC versus GG, and GC versus GG) also did not reveal any significant association. On the contrary, the −572C allele was associated with a decreased risk of CHD when compared with the −572G allele (ORs = 0.79, 95%CI = 0.68 to 0.93). Furthermore, analyses under the recessive model (ORs = 0.69, 95% = 0.59 to 0.80) and the allele contrast model (genotype of CC versus GG, ORs = 0.49, 95% = 0.35 to 0.70) yielded similar results. However, statistical significance was not found when the meta-analysis was restricted to studies focusing on European populations, studies with large sample size, and cohort studies by using subgroup analysis.
The −174G>C polymorphism in the IL-6 gene is not significantly associated with increased risks of CHD. However, The −572G>C polymorphism may contribute to CHD development. Future investigations with better study design and large number of subjects are needed.
For decades, research efforts have tried to uncover the underlying genetic basis of human susceptibility to a variety of diseases. Linkage studies have resulted in highly replicated findings and helped identify quantitative trait loci (QTL) for many complex traits; however identification of specific alleles accounting for linkage remains elusive. The purpose of this study was to determine whether with a sufficient number of variants a linkage signal can be fully explained.
We used comprehensive fine-mapping using a dense set of single nucleotide polymorphisms (SNPs) across the entire quantitative trait locus (QTL) on human chromosome 7q36 linked to plasma triglyceride levels. Analyses included measured genotype and combined linkage association analyses.
Screening this linkage region, we found an over representation of nominally significant associations in five genes (MLL3, DPP6, PAXIP1, HTR5A, INSIG1). However, no single genetic variant was sufficient to account for the linkage. On the other hand, multiple variants capturing the variation in these five genes did account for the linkage at this locus. Permutation analyses suggested that this reduction in LOD score was unlikely to have occurred by chance (p = 0.008).
With recent findings, it has become clear that most complex traits are influenced by a large number of genetic variants each contributing only a small percentage to the overall phenotype. We found that with a sufficient number of variants, the linkage can be fully explained. The results from this analysis suggest that perhaps the failure to identify causal variants for linkage peaks may be due to multiple variants under the linkage peak with small individual effect, rather than a single variant of large effect.
The Protein C anticoagulant pathway regulates blood coagulation by preventing the inadequate formation of thrombi. It has two main plasma components: protein C and protein S. Individuals with protein C or protein S deficiency present a dramatically increased incidence of thromboembolic disorders. Here, we present the results of a genome-wide association study (GWAS) for protein C and protein S plasma levels in a set of extended pedigrees from the Genetic Analysis of Idiopathic Thrombophilia (GAIT) Project. A total number of 397 individuals from 21 families were typed for 307,984 SNPs using the Infinium® 317 k Beadchip (Illumina). Protein C and protein S (free, functional and total) plasma levels were determined with biochemical assays for all participants. Association with phenotypes was investigated through variance component analysis. After correcting for multiple testing, two SNPs for protein C plasma levels (rs867186 and rs8119351) and another two for free protein S plasma levels (rs1413885 and rs1570868) remained significant on a genome-wide level, located in and around the PROCR and the DNAJC6 genomic regions respectively. No SNPs were significantly associated with functional or total protein S plasma levels, although rs1413885 from DNAJC6 showed suggestive association with the functional protein S phenotype, possibly indicating that this locus plays an important role in protein S metabolism. Our results provide evidence that PROCR and DNAJC6 might play a role in protein C and free protein S plasma levels in the population studied, warranting further investigation on the role of these loci in the etiology of venous thromboembolism and other thrombotic diseases.
Kawasaki disease results from an abnormal immunological response to one or more infectious triggers. We hypothesised that heritable differences in immune responses in Kawasaki disease-affected children and their families would result in different epidemiological patterns of other immune-related conditions. We investigated whether hospitalisation for infection and asthma/allergy were different in Kawasaki disease-affected children and their relatives.
We used Western Australian population-linked health data from live births (1970–2006) to compare patterns of hospital admissions in Kawasaki disease cases, age- and sex-matched controls, and their relatives. There were 295 Kawasaki disease cases and 598 age- and sex-matched controls, with 1,636 and 3,780 relatives, respectively. Compared to controls, cases were more likely to have been admitted at least once with an infection (cases, 150 admissions (50.8%) vs controls, 210 admissions (35.1%); odds ratio (OR) = 1.9, 95% confidence interval (CI) 1.4–2.6, P = 7.2×10−6), and with asthma/allergy (cases, 49 admissions (16.6%) vs controls, 42 admissions (7.0%); OR = 2.6, 95% CI 1.7–4.2, P = 1.3×10−5). Cases also had more admissions per person with infection (cases, median 2 admissions, 95% CI 1–5, vs controls, median 1 admission, 95% CI 1–4, P = 1.09×10−5). The risk of admission with infection was higher in the first degree relatives of Kawasaki disease cases compared to those of controls, but the differences were not significant.
Differences in the immune phenotype of children who develop Kawasaki disease may influence the severity of other immune-related conditions, with some similar patterns observed in relatives. These data suggest the influence of shared heritable factors in these families.
APOE plays a well established role in lipid metabolism. Animal model evidence suggests APOE may also be associated with adiposity, but this has not been thoroughly investigated in humans. We measured adiposity (BMI, truncal fat mass, waist circumference), physical activity (PA), cardiorespiratory fitness and APOE genotype (E2, E3, E4) in 292 8-year-old children from the Tasmanian Infant Health Survey (TIHS), an Australian population-based prospective birth cohort. Our aims were to examine the association of APOE with child adiposity, and to examine the interplay between this association and other measured factors. We found that APOE was associated with child lipid profiles. APOE was also associated with child adiposity measures. The association was E4 allele-specific, with adiposity lower in the E4-containing group (BMI: Mean difference -0.90 kg/m2; 95% confidence intervals (CI) -1.51, -0.28; p = 0.004). The association of APOE4 with lower BMI differed by fitness status (difference in effect p = 0.002), and was more evident among the less fit (mean difference -1.78 kg/m2; 95% CI -2.74, -0.83; p<0.001). Additionally, associations between BMI and lipids were only apparent in those of lower fitness who did not carry APOE4. Similar overall findings were observed when truncal fat mass and waist circumference were used as alternative adiposity measures. APOE4 and cardiorespitatory fitness could interact to influence child adiposity. In studies addressing the genetic determinants of childhood obesity, the context of child fitness should also be taken into account.