Adiposity significantly modifies genetic risk for dyslipidemia

Recent genome-wide association studies have identified multiple loci robustly associated with plasma lipids, which also contribute to extreme lipid phenotypes. However, these common genetic variants explain <12% of variation in lipid traits. Adiposity is also an important determinant of plasma lipoproteins, particularly plasma TGs and HDL cholesterol (HDLc) concentrations. Thus, interactions between genes and clinical phenotypes may contribute to this unexplained heritability. We have applied a weighted genetic risk score (GRS) for both plasma TGs and HDLc in two large cohorts at the extremes of BMI. Both BMI and GRS were strongly associated with these lipid traits. A significant interaction between obese/lean status and GRS was noted for each of TG (P_Interaction = 2.87 × 10⁻⁴) and HDLc (P_Interaction = 1.05 × 10⁻³). These interactions were largely driven by SNPs tagging APOA5, glucokinase receptor (GCKR), and LPL for TG, and cholesteryl ester transfer protein (CETP), GalNAc-transferase (GALNT2), endothelial lipase (LIPG), and phospholipid transfer protein (PLTP) for HDLc. In contrast, the GRS_{LDL cholesterol} × adiposity interaction was not significant. Sexual dimorphism was evident for the GRS_HDL on HDLc in obese (P_Interaction = 0.016) but not lean subjects. SNP by BMI interactions may provide biological insight into specific genetic associations and missing heritability.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.     

Identifying genetic risk factors for osteoporosis

Over the past decades epidemiological research of so-called "complex" diseases, i.e., common age-related disorders such as cancer, cardiovascular disease, diabetes, and osteoporosis, has identified anthropometric, behavioural, and serum parameters as risk factors. Recently, genetic polymorphisms have gained considerable interest, propelled by the Human Genome Project and its sequela that have identified most genes and uncovered a plethora of polymorphic variants, some of which embody the genetic risk factors. In all fields of complex disease genetics (including osteoporosis) progress in identifying these genetic factors has been hampered by often controversial results. Because of the small effect size for each individual risk polymorphism, this is mostly due to low statistical power and limitations of analytical methods. Genome-wide scanning approaches can be used to find the responsible genes. It is by now clear that linkage analysis is not suitable for this, but genome-wide association analysis has much better possibilities, as is illustrated by successful identification of risk alleles for several complex diseases. Candidate gene association analysis followed by replication and prospective multi-centred meta-analysis, is currently the best way forward to identify genetic markers for complex traits, such as osteoporosis. To accomplish this, we need large (global) collaborative studies using standardized methodology and definitions, to quantify by meta-analysis the subtle effects of the responsible gene variants.     

Host genetic risk factors for community-acquired pneumonia

This study was conducted to establish the contribution of genetic host factors in the susceptibility to community acquired pneumonia (CAP) in the Russian population. Patients with CAP (n = 334), volunteers without a previous history of CAP, constantly exposed to infectious agents, control A group (n = 141) and a second control group B consisted of healthy persons (n = 314) were included in the study. All subjects were genotyped for 13 polymorphic variants in the genes of xenobiotics detoxification CYP1A1 (rs2606345, rs4646903, and rs1048943), GSTM1 (Ins/del), GSTT1 (Ins/del), ABCB1 rs1045642); immune and inflammation response IL-6 (rs1800795), TNF-a (rs1800629), MBL2 (rs7096206), CCR5 (rs333), NOS3 (rs1799983), angiotensin-converting enzyme ACE (rs4340), and occlusive vascular disease/hyperhomocysteinemia MTHFR (rs1801133). Seven polymorphic variants in genes CYP1A1, GSTM1, ABCB1, NOS3, IL6, CCR5 and ACE were associated with CAP. For two genes CYP1A1 and GSTM1 associations remained significant after correction for multiple comparisons. Multiple analysis by the number of all risk genotypes showed a highly significant association with CAP (P = 2.4 × 10^− 7, OR = 3.03, 95% CI 1.98–4.64) with the threshold for three risk genotypes. Using the ROC-analysis, the AUC value for multi-locus model was estimated as 68.38.     

CRP Gene polymorphism contributes genetic susceptibility to dyslipidemia in Han Chinese population

C-reactive protein (CRP), an inflammatory marker that statistically predicts future cardiovascular risk, has been reported to be associated with plasma lipid level changes. Whether CRP genetic variants affect lipid metabolism is of importance to investigate. A community-based study population including 2,731 adult subjects aged 18–62 years was used to evaluate the association of CRP gene with dyslipidemia and five tagging SNPs (tagSNPs) were genotyped. Multiple logistic regression was applied to further evaluate relationships between the SNPs and lipid metabolism abnormality and general linear model was applied to compare plasma lipid levels between genotypes. Association analyses indicated that recessive model of SNPs rs876537 and rs4285692 had significant association with elevated HDL after adjustment for covariates. Odds ratio (OR) of rs876537 were 0.60 for HDL > 1.54 versus 1.04–1.54 mmol/L (P = 0.011), as well as, ORs were 0.617 for HDL > 1.83 versus ≤1.35 mmol/L (P = 0.002) and 0.724 for HDL = 1.59–1.83 versus ≤1.35 mmol/L (P = 0.028) respectively. OR of rs4285692 was 0.634 for HDL > 1.83 versus ≤1.35 mmol/L (P = 0.027). Further stratification analysis found significant associations of rs10737175 with elevated HDL (>1.54 vs. 1.04–1.54 mmol/L, OR 0.629 and P = 0.027) and elevated TG (≥1.70 vs. <1.70 mmol/L, ORs of additive and dominant models were 0.628, 0.545 and P values were 0.006, 0.003 respectively) in female. rs4285692 was significantly associated with elevated LDL (≥3.37 vs. <3.37 mmol/L), ORs equaled to 1.532, 2.281 for additive model and recessive model and P values were 0.028, 0.024 respectively in male. Furthermore, quantitative trait analysis indicated the variation T to C of rs876537 significantly affect decreased plasma HDL level (P = 0.014). Our findings suggest that CRP genetic polymorphisms independently had positive association with the risk of HDL, LDL and TG elevating and further replication in other large population and biological function research would be warranted.     

Effects of genetic variants on lipid parameters and dyslipidemia in a Chinese population

A number of recent genome-wide association (GWA) studies have identified several novel genetic determinants of plasma lipid and lipoprotein concentrations in European populations. However, it is still unclear whether these loci identified in Caucasian GWA studies also exert the same effect on lipid and lipoprotein concentrations in a Chinese population. We genotyped 10 single-nucleotide polymorphisms (SNPs) in nine loci in a Chinese Han population sample (n = 4,192) and assessed the associations of these SNPs with metabolic traits, using linear regression adjusted for age, gender, diabetes status, and body mass index. Three variants (rs12654264, P ～ 1.7 × 10(-6); rs3764261, P ～ 7.1 × 10(-7); and rs4420638, P ～ 1.1 × 10(-3)) showed strong evidence for association with total cholesterol; four variants (rs780094, P ～ 1.8 × 10(-11); rs17145738, P ～ 5.0 × 10(-7); rs326, P ～ 2.3 × 10(-6); and rs439401, P ～ 2.2 × 10(-5)) showed strong evidence for association with triglycerides, four variants (rs17145738, P ～ 1.9 × 10(-4); rs326, P ～ 9.7 × 10(-4); rs1800588, P ～ 1.5 × 10(-7); and rs3764261, P ～ 4.3 × 10(-14)) showed strong evidence for association with HDL-cholesterol (HDL-C), two variants (rs12654264, P ～ 2.3 × 10(-5); and rs4420638, P ～ 3.6 × 10(-4)) showed strong evidence for association with LDL-C, and four variants (rs326, P ～ 2.8 × 10(-3); rs1800588, P ～ 6.1 × 10(-4); rs3764261, P ～ 2.0 × 10(-3); and rs4420638, P ～ 9.4 × 10(-5)) showed strong evidence for association with total cholesterol-HDL-C-related ratio. These SNPs generated strong combined effects on lipid traits and dyslipidemia. Our findings indicate that the variants that associated with metabolic traits in Europeans may also play a role in a Chinese Han population.     

Genetic variants influencing lipid levels and risk of dyslipidemia in Chinese population

Recently, several human genetic and genomewide association studies (GWAS) have discovered many genetic loci that are associated with the concentration of the blood lipids. To confirm the reported loci in Chinese population, we conducted a cross-section study to analyse the association of 25 reported SNPs, genotyped by the ABI SNaPshot method, with the blood levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) in 1900 individuals by multivariate analysis. Logistic regression was applied to assess the association of the genetic loci with the risk of different types of dyslipidemia. Our study has convincingly identified that 12 of 25 studied SNPs were strongly associated with one or more blood lipid parameters (TC, LDL, HDL and TG). Among the 12 associated SNPs, 10 significantly influence the risk of one or more types of dyslipidemia. We firstly found four SNPs (rs12654264 in HMGCR; rs2479409 in PCSK9; rs16996148 in CILP2, PBX4; rs4420638 in APOE-C1-C4-C2) robustly and independently associate with four types of dyslipidemia (MHL, mixed hyperlipidemia; IHTC, isolated hypercholesterolemia; ILH, isolated low HDL-C; IHTG, isolated hypertriglyceridemia). Our results suggest that genetic susceptibility is different on the same candidate locus for the different populations. Meanwhile, most of the reported genetic variants strongly influence one or more plasma lipid levels and the risk of dyslipidemia in Chinese population.     

Novel genetic risk variants for pediatric celiac disease

Background

Celiac disease is a complex chronic immune-mediated disorder of the small intestine. Today, the pathobiology of the disease is unclear, perplexing differential diagnosis, patient stratification, and decision-making in the clinic.

Methods

Herein, we adopted a next-generation sequencing approach in a celiac disease trio of Greek descent to identify all genomic variants with the potential of celiac disease predisposition.

Results

Analysis revealed six genomic variants of prime interest: SLC9A4 c.1919G>A, KIAA1109 c.2933T>C and c.4268_4269delCCinsTA, HoxB6 c.668C>A, HoxD12 c.418G>A, and NCK2 c.745_746delAAinsG, from which NCK2 c.745_746delAAinsG is novel. Data validation in pediatric celiac disease patients of Greek (n?=?109) and Serbian (n?=?73) descent and their healthy counterparts (n?=?111 and n?=?32, respectively) indicated that HoxD12 c.418G>A is more prevalent in celiac disease patients in the Serbian population (P?<?0.01), while NCK2 c.745_746delAAinsG is less prevalent in celiac disease patients rather than healthy individuals of Greek descent (P?=?0.03). SLC9A4 c.1919G>A and KIAA1109 c.2933T>C and c.4268_4269delCCinsTA were more abundant in patients; nevertheless, they failed to show statistical significance.

Conclusions

The next-generation sequencing-based family genomics approach described herein may serve as a paradigm towards the identification of novel functional variants with the aim of understanding complex disease pathobiology.

     

Prediction of genetic growth curves in pigs

Genetic growth curves of boars in a test station were predicted on daily weight records collected by automated weighing scales. The data contained 121 865 observations from 1477 Norwegian Landrace boars and 108 589 observations from 1300 Norwegian Duroc boars. Random regression models using Legendre polynomials up to second order for weight at different ages were compared for best predicting ability and Bayesian information criterion (BIC) for both breeds. The model with second-order polynomials had best predictive ability and BIC. The heritability for weight, based on this model, was found to vary along the growth trajectory between 0.32-0.35 for Duroc and 0.17-0.25 for Landrace. By varying test length possibility to use shorter test time and pre-selection was tested. Test length was varied and compared with average termination at 100 kg, termination of the test at 90 kg gives, e.g. 2% reduction in accuracy of estimated breeding values (EBV) for both breeds and termination at 80 kg gives 5% reduction in accuracy of EBVs for Landrace and 3% for Duroc. A shorter test period can decrease test costs per boar, but also gives possibilities to increase selection intensity as there will be room for testing more boars.     

Performance of an adipokine pathway-based multilocus genetic risk score for prostate cancer risk prediction

Few biomarkers are available to predict prostate cancer risk. Single nucleotide polymorphisms (SNPs) tend to have weak individual effects but, in combination, they have stronger predictive value. Adipokine pathways have been implicated in the pathogenesis. We used a candidate pathway approach to investigate 29 functional SNPs in key genes from relevant adipokine pathways in a sample of 1006 men eligible for prostate biopsy. We used stepwise multivariate logistic regression and bootstrapping to develop a multilocus genetic risk score by weighting each risk SNP empirically based on its association with disease. Seven common functional polymorphisms were associated with overall and high-grade prostate cancer (Gleason≥7), whereas three variants were associated with high metastatic-risk prostate cancer (PSA≥20 ng/mL and/or Gleason≥8). The addition of genetic variants to age and PSA improved the predictive accuracy for overall and high-grade prostate cancer, using either the area under the receiver-operating characteristics curves (P<0.02), the net reclassification improvement (P<0.001) and integrated discrimination improvement (P<0.001) measures. These results suggest that functional polymorphisms in adipokine pathways may act individually and cumulatively to affect risk and severity of prostate cancer, supporting the influence of adipokine pathways in the pathogenesis of prostate cancer. Use of such adipokine multilocus genetic risk score can enhance the predictive value of PSA and age in estimating absolute risk, which supports further evaluation of its clinical significance.     

Information requirements and regulatory approaches for heritable genetic risk assessment and risk communication

With the evolution of genetic toxicology as a scientific discipline and the formation of the Environmental Mutagen Society (EMS), much thought was given to the study of chemicals in the human environment for their mutagenic effects. The Society's goal was to promote scientific investigation and dissemination of information related to genetic toxicology. Subsequently, the concern for chemically induced genetic damage in human germ cells and its potential impact on genetic diseases was detailed in the Committee 17 Report (1975). With new information on the involvement of genetic alterations in disease and on the ramifications of possible effects of exposures to environmental mutagens, it is becoming increasingly necessary to again focus our attention on the assessment of heritable genetic effects. Clearly, strategies for communication of genetic hazard/risk assessments to exposed individuals and to those charged with regulating environmental agents need to be developed.     

Gender differences in genetic risk profiles for cardiovascular disease

Background

Cardiovascular disease (CVD) incidence, complications and burden differ markedly between women and men. Although there is variation in the distribution of lifestyle factors between the genders, they do not fully explain the differences in CVD incidence and suggest the existence of gender-specific genetic risk factors. We aimed to estimate whether the genetic risk profiles of coronary heart disease (CHD), ischemic stroke and the composite end-point of CVD differ between the genders.

Methodology/Principal Findings

We studied in two Finnish population cohorts, using the case-cohort design the association between common variation in 46 candidate genes and CHD, ischemic stroke, CVD, and CVD-related quantitative risk factors. We analyzed men and women jointly and also conducted genotype-gender interaction analysis. Several allelic variants conferred disease risk for men and women jointly, including rs1801020 in coagulation factor XII (HR = 1.31 (1.08–1.60) for CVD, uncorrected p = 0.006 multiplicative model). Variant rs11673407 in the fucosyltransferase 3 gene was strongly associated with waist/hip ratio (uncorrected p = 0.00005) in joint analysis. In interaction analysis we found statistical evidence of variant-gender interaction conferring risk of CHD and CVD: rs3742264 in the carboxypeptidase B2 gene, p(interaction) = 0.009 for CHD, and rs2774279 in the upstream stimulatory factor 1 gene, p(interaction) = 0.007 for CHD and CVD, showed strong association in women but not in men, while rs2069840 in interleukin 6 gene, p(interaction) = 0.004 for CVD, showed strong association in men but not in women (uncorrected p-values). Also, two variants in the selenoprotein S gene conferred risk for ischemic stroke in women, p(interaction) = 0.003 and 0.007. Importantly, we identified a larger number of gender-specific effects for women than for men.

Conclusions/Significance

A false discovery rate analysis suggests that we may expect half of the reported findings for combined gender analysis to be true positives, while at least third of the reported genotype-gender interaction results are true positives. The asymmetry in positive findings between the genders could imply that genetic risk loci for CVD are more readily detectable in women, while for men they are more confounded by environmental/lifestyle risk factors. The possible differences in genetic risk profiles between the genders should be addressed in more detail in genetic studies of CVD, and more focus on female CVD risk is also warranted in genome-wide association studies.     

Attitude toward genetic testing for cancer risk in Istanbul

To identify attitudes toward genetic testing, and the effects of this information on decisions regarding issues such as pregnancy, abortion, and prophylactic surgery, several subsets of the Turkish population were surveyed in hospital settings. Individuals (n = 179) chosen arbitrarily from four different subsets of a Turkish population were asked to participate in a confidential 23-question survey. Survey participants were familiar with the concept of cancer being a familial disease (85.5%), and 84.7% of them expressed interest in genetic testing to determine cancer risk, 83.9% would have their fetuses tested for such cancer risk, 65.1% would terminate their pregnancies, 92.2% would have their children tested if they were determined to have an increased cancer risk, 71.9% would agree to undergo prophylactic oophorectomy or orchiectomy and 67.6% would have mastectomy/prostatectomy should there be an increased cancer risk to these organs. It appears that at least the sampled segment of a Turkish population is willing to undergo genetic testing to determine if they are at increased risk for cancer. The feasibility and acceptance of genetic testing and the influence of education and genetic counseling in the Turkish people should further be evaluated with a larger stratified sample of the population.     

Use of in vivo genetic toxicity data for risk assessment

Mutagenicity studies have been used to identify specific agents as potential carconogens or other human health hazards; however, they have been used minimally for risk assessment or in determining permissible levels of human exposure. The poor predictive value of in vitro mutagenesis tests for carcinogenic activity and a lack of mechanistic understanding of the roles of mutagens in the induction of specific cancers have made these tests unattractive for the purpose of risk assessment. However, the limited resources available for carcinogen testing and large number of chemicals which need to be evaluated necessitate the incorporation of more efficient methods into the evaluation process. In vivo genetic toxicity testing can be recommended for this purpose because in vivo assays incorporate the metabolic activation pathways that are relevant to humans. We propose the use of a multiple end-point in vivo comprehensive testing protocol (CTP) using rodents. Studies using sub-acute exposure to low levels of test agents by routes consistent with human exposure can be a useful adjunct to methods currently used to provide data for risk assessment. Evaluations can include metabolic and pharmacokinetic endpoints, in addition to genetic toxicity studies, in order to provide a comprehensive examination of the mechanism of toxicity of the agent. A parallelogram approach can be used to estimate effects in non-accessible human tissues by using data from accessible human tissues and analogous tissues in animals. A categorical risk assessment procedure can be used which would consider, in order of priority, genetic damage in man, genetic damage in animals that is highly relevant to disease outcome (mutation, chromosome damage), and data from animals that is of less certain relevance to disease. Action levels of environmental exposure would be determined based on the lowest observed effect levels or the highest observed no effect levels, using sub-acute low level exposure studies in rodents. As an example, the known genotoxic effects of benzene exposure at low levels in man and animals are discussed. The lowest observed genotoxic effects were observed at about 1–10 parts per million for man and 0.04–0.1 parts per million in subacute animal studies. If genetic toxicity is to achieve a prominent role in evaluating carcinogens and characterizing germ-cell mutagens, minimal testing requirements must be established to ascertain the risk associated with environmental mutagen exposure. The use of the in vivo approach described here should provide the information needed to meet this goal. In addition, it should allow truly epigenetic or non-genotoxic carcinogens to be distinguished from the genotoxic carcinogens that are not detected by in vitro methods.     

Prediction model for knee osteoarthritis based on genetic and clinical information

Introduction  

Osteoarthritis (OA) is the most common bone and joint disease influenced by genetic and environmental factors. Recent association studies have uncovered the genetic factors behind OA, its susceptibility genes, which would enable us to predict disease occurrence based on genotype information. However, most previous studies have evaluated the effects of only a single susceptibility gene, and hence prediction based on such information is not as reliable. Here, we constructed OA-prediction models based on genotype information from a case-control association study and tested their predictability.     

The impact of age on genetic risk for common diseases

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.     

Combined genetic polymorphism and risk for development of lung cancer

Susceptibility to lung cancer has been shown to be modulated by host specific factors. Inheritance of different polymorphic cytochrome P450s (CYPs) and the glutathione S-transferases (GSTs) which affect metabolism of environmental toxicants may play a key role in individual susceptibility. Although individual polymorphic genes have been reported to be associated with development of lung cancer, little is known about the combined effects of several genes in carcinogenesis. From our study of 54 lung cancer patients and 50 matched controls, we observed that a combination of several versions of ‘unfavorable' metabolizing genes (CYP2D6, CYP2E1, GSTM1 and GSTT1) is strongly associated with lung cancer. The relative risk for the different combinations of these genotypes ranged between 1.3 and 14, with higher risk involving the activating genes. The duration and intensity of heavy smoking (expressed in pack-years) are the most important determinant for the development of lung cancer. For example, the estimated risk for development of lung cancer associated with smoking >30 pack-years is represented by an odds ratio=6.65; 95% CL=2.3–19.9 irrespective of an individual's genotype, whereas for smoking between >30 and <50 pack-years, odds ratio=4.5; 95% CL=1.37–15; and for smoking >50 pack-years, odds ratio=30; 95% CL=5.7–114. On the other hand, smoking of less than 30 pack-years is associated with an increased risk in the presence of the polymorphic genes (odds ratio=2.5; 95% CL=0.32–54). The results of our study indicate that the inheritance of multiple ‘unfavorable' genotypes, especially activating genes, is a crucial predisposing factor for the development of lung cancer from cigarette smoking. In addition, the genes may cause moderate smokers who would normally outlive the deleterious effects of smoking to develop lung cancer. The information can therefore be used to target individuals for prevention of health problems.