Prediction of genetic risk for dyslipidemia

The purpose of the present study was to identify genetic variants that confer susceptibility to dyslipidemia. A total of 5213 individuals from two independent populations were examined: Subject panel A comprised 3794 individuals who visited participating hospitals; subject panel B comprised 1419 community-dwelling elderly individuals. The genotypes for 100 polymorphisms of 65 candidate genes were determined. The chi(2) test and multivariable logistic regression analysis revealed that seven polymorphisms of APOA5, APOC3, APOA1, ACAT2, and LPL were significantly associated with hypertriglyceridemia, six polymorphisms of APOA5, LIPC, and CYP3A4 with low HDL-cholesterol, and three polymorphisms of APOE and CCR2 with high LDL-cholesterol in subject panel A. For validation of these associations, the same polymorphisms were examined in subject panel B. Six polymorphisms of APOA5, APOC3, APOA1, and LPL were again significantly associated with hypertriglyceridemia, three polymorphisms of APOA5 with low HDL-cholesterol, and two polymorphisms of APOE with high LDL-cholesterol. Serum triglyceride, HDL-cholesterol, and LDL-cholesterol concentrations differed significantly among genotypes of these corresponding polymorphisms in both subject panels. These results indicate that polymorphisms of APOA5, APOC3, APOA1, and LPL are determinants of hypertriglyceridemia and that those of APOA5 and APOE are determinants of low HDL-cholesterol and high LDL-cholesterol, respectively, in Japanese individuals.     

遗传风险评分在复杂疾病遗传学研究中的应用

心血管疾病、2型糖尿病、原发性高血压、哮喘、肥胖、肿瘤等复杂疾病在全球范围内流行,并成为人类死亡的主要原因。越来越多的人开始关注遗传易感性在复杂疾病发病机制中的作用。至今,与复杂疾病相关的易感基因和基因序列变异仍未完全清楚。人们希望通过遗传关联研究来阐明复杂疾病的遗传基础。近年来,全基因组关联研究和候选基因研究发现了大量与复杂疾病有关的基因序列变异。这些与复杂疾病有因果和(或)关联关系的基因序列变异的发现促进了复杂疾病预测和防治方法的产生和发展。遗传风险评分(Genetic risk score,GRS)作为探索单核苷酸多态(Single nucleotide polymorphisms,SNPs)与复杂疾病临床表型之间关系的新兴方法,综合了若干SNPs的微弱效应,使基因多态对疾病的预测性大幅度提升。该方法在许多复杂疾病遗传学研究中得到成功应用。本文重点介绍了GRS的计算方法和评价标准,简要列举了运用GRS取得的系列成果,并对运用过程中所存在的局限性进行了探讨,最后对遗传风险评分的未来发展方向进行了展望。     

Amyotrophic lateral sclerosis as a complex genetic disease

In complex diseases like ALS, there are multiple genetic and environmental factors all contributing to disease liability. The genetic factors causing susceptibility to developing ALS can be considered a spectrum from single genes with large effect sizes causing classical Mendelian ALS, to genes of smaller effect, producing apparently sporadic disease. We examine the statistical genetic principles that underpin this model and review what is known about ALS as a disease with complex genetics.     

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.

     

A genetic interaction network model of a complex neurological disease

Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike‐wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use combined analysis of pleiotropy and epistasis (CAPE) to detect and interpret genetic interactions in a meta‐population derived from three C3H × B6J strain crosses, each of which is fixed for a different SWD‐causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6J, suggesting common modifiers. By combining information across two phenotypic measures – SWD duration and frequency – CAPE showed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, toward a more comprehensive view of its underlying genetic architecture.     

A genetic risk score combining ten psoriasis risk loci improves disease prediction

Psoriasis is a chronic, immune-mediated skin disease affecting 2–3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS) combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS) and a weighted (wGRS) approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7) versus 12.09 (SD 1.8), p = 4.577×10⁻⁴⁰. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63–14.57), p = 2.010×10⁻⁶⁵. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC). The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10⁻⁸). Additionally, the AUC for HLA-C alone (rs10484554) was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18), highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10⁻⁶) and family history (p = 0.020). Using a liability threshold model, we estimated that the 10 risk loci account for only11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date.     

Parental genotypes in the risk of a complex disease

Our understanding of the genetic etiology of complex disorders is still elusive. According to the common-variant/common-disease hypothesis, frequent functional polymorphisms are the best candidates for disease-susceptibility alleles. Implicitly, we also assume that disease-susceptibility alleles are preferentially transmitted from parents to the affected offspring and that this effect can be captured by the transmission/disequilibrium test (TDT). However, our study of genetic predisposition to childhood acute lymphoblastic leukemia suggests that a focus on the patient's genotype might, in certain instances, be misleading. Our results indicate that, at least at some loci, parental genetics might be of primary importance in predicting the risk of cancer in this pediatric model of a complex disease. Consequently, in addition to TDT, other complementary strategies will need to be simultaneously applied to dissect genetic predisposition to complex disorders.     

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.     

Prioritizing complex disease risk genes by integrating multiple data

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.     

Strategies for the assessment of genetic coronary artery disease risk.

In atherosclerotic diseases, genetic factors have a substantial influence on the age of onset and the frequency and severity of clinical symptoms, as well as response to therapy. In myocardial infarctions occurring at young age, genetics may be the leading causative factor. Despite such a prominent role of genetics in the pathophysiology of atherosclerosis clinical risk assessment and therapeutic decision making are still based on classical risk factors. In this paper we analyse the reasons for the current lack of predictive power of genetics-based algorithms and we speculate why future developments might open the door to a role for genetics in the clinical management of atherosclerosis.     

Non-invasive genetic sampling and individual identification

Individual identification via non-invasive sampling is of prime importance in conservation genetics and in behavioural ecology. This approach allows for genetics studies of wild animals without having to catch them, or even to observe them. The material used as a source of DNA is usually faeces, shed hairs, or shed feathers. It has been recendy shown that this material may lead to genotyping errors, mainly due to allelic dropout. In addition to these technical errors, there are problems with accurately estimating the probability of identity (PI, or the probability of two individuals having identical genotypes) because of the presence of close relatives in natural populations. As a consequence, before initiating an extensive study involving non-invasive sampling, we strongly suggest conducting a pilot study to assess both the technical difficulties and the PI for the genetic markers to be used. This pilot study could be carried out in three steps: (i) estimation of the PI using preliminary genetic data; (ii) simulations taking into account the PI and choosing the technical error rate mat is sufficiently low for assessing the scientific question; (iii) polymerase chain reaction (PCR) experiments to check if it is technically possible to achieve this error rate.     

Finding genes underlying risk of complex disease by linkage disequilibrium mapping

Identification of genes that harbor variation associated with inter-individual differences in risk of complex diseases remains one of the most challenging and important problems in human genetics. For genetic variants that are sufficiently common and have sufficiently large effects, direct tests of association through linkage disequilibrium with anonymous SNPs may prove effective. But the two critical parameters - the frequency of risk-inflating alleles and the magnitudes of their effect on risk - remain largely unknown. In this review we consider the latest information regarding the likely efficacy of the linkage disequilibrium mapping approach.     

Accuracy of predicting the genetic risk of disease using a genome-wide approach

Background

The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy.

Methodology/Principal Findings

We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability.

Conclusions/Significance

This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.