Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study

Diagnostic Test Accuracy of Genetic Tests in Diagnosing Psoriasis: A Systematic Review

The pathogenesis of psoriasis involves the interaction of several environmental and genetic factors. Predicting the disease risk cannot depend on individual genetic alleles. Consequently, some studies have evaluated the use of genetic risk scores that combine several psoriasis susceptibility loci to increase the accuracy of predicting/diagnosing the disease. This meta-analysis summarizes the evidence regarding using genetic risk scores (GRS) in the diagnosis or prediction of psoriasis. A search of MEDLINE/PubMed, the Latin American Caribbean Health Sciences Literature (LILACS) database, Cochrane Library, Scopus, Web of Science, and ProQuest was conducted in July 2022. The primary objective was to record the area under the curve (AUC) for GRS of psoriasis. Secondary objectives included characteristics of studies and patients. The risk of bias (ROB) was assessed using the PROBAST tool. Five studies fulfilled the eligibility criteria of this review. None of the studies described the clinical criteria (reference standard) that were employed to diagnose psoriasis. The AUCs of the 11 GRS models ranged from 0.6029-0.8583 (median: 0.75). Marked heterogeneity was detected (Cochran Q: 1250.051, p < 0.001, and I2 index: 99.2%). So, pooling of the results of the included studies was not performed. The ROB was high for all studies and clinical application was not described. Genetic risk scores are promising tools for the prediction of psoriasis with fair to good accuracy. However, further research is required to identify the most accurate combination of loci and to validate the scores in variable ethnicities.

Interactive effects of the low-carbohydrate diet score and genetic risk score on Hypo-HDL-cholesterolemia among Korean adults: A cross-sectional analysis from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study

This cross-sectional study investigated the interaction between the genetic risk score (GRS) and abnormal high-density lipoprotein (HDL) cholesterol lipid levels, which are modified by low-carbohydrate diets (LCDs) and their effects on the prevalence of hypo-HDL-cholesterolemia (hypo-HDL-C) in Korean adults. Baseline data were obtained from the Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES), conducted from 2001 to 2002, that targeted 8,314 Korean adults aged 40-69 years, including old men (47.6%) and women (52.4%), and whole genomic single nucleotide polymorphism (SNP) genotyping was performed. We identified 18 SNPs significantly associated with hypo-HDL-C in the proximity of several genes, including LPL, APOA5, LIPC, and CETP, and calculated the GRS. The low-carbohydrate diet score (LCDS) was calculated on the basis of energy intake information from food frequency questionnaires. Furthermore, we performed multivariable-adjusted logistic modeling to examine the odds ratio (OR) for hypo-HDL-C across tertiles of LCDS and GRS, adjusted for several covariates. Among participants in the highest GRS tertile, those in the highest tertile of the LCDS had a significantly lower risk of hypo-HDL-C (OR: 0.759, 95% CI (confidence interval): 0.625-0.923) than those in the lowest tertile of the LCDS. In the joint effect model, the group with the lowest GRS and highest LCDS was found to have the lowest risk of hypo-HDL-C prevalence. This study suggests that individuals with a high genetic risk for low HDL concentrations may have a beneficial effect on a lower intake of carbohydrates.

Genetic factors increase the identification efficiency of predictive models for dyslipidaemia: a prospective cohort study

Background

Few studies have developed risk models for dyslipidaemia, especially for rural populations. Furthermore, the performance of genetic factors in predicting dyslipidaemia has not been explored. The purpose of this study is to develop and evaluate prediction models with and without genetic factors for dyslipidaemia in rural populations.

Methods

A total of 3596 individuals from the Henan Rural Cohort Study were included in this study. According to the ratio of 7:3, all individuals were divided into a training set and a testing set. The conventional models and conventional+GRS (genetic risk score) models were developed with Cox regression, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) classifiers in the training set. The area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination index (IDI) were used to assess the discrimination ability of the models, and the calibration curve was used to show calibration ability in the testing set.

Results

Compared to the lowest quartile of GRS, the hazard ratio (HR) (95% confidence interval (CI)) of individuals in the highest quartile of GRS was 1.23(1.07, 1.41) in the total population. Age, family history of diabetes, physical activity, body mass index (BMI), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were used to develop the conventional models, and the AUCs of the Cox, ANN, RF, and GBM classifiers were 0.702(0.673, 0.729), 0.736(0.708, 0.762), 0.787 (0.762, 0.811), and 0.816(0.792, 0.839), respectively. After adding GRS, the AUCs increased by 0.005, 0.018, 0.023, and 0.015 with the Cox, ANN, RF, and GBM classifiers, respectively. The corresponding NRI and IDI were 25.6, 7.8, 14.1, and 18.1% and 2.3, 1.0, 2.5, and 1.8%, respectively.

Conclusion

Genetic factors could improve the predictive ability of the dyslipidaemia risk model, suggesting that genetic information could be provided as a potential predictor to screen for clinical dyslipidaemia.

Trial registration

The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register. (Trial registration: ChiCTR-OOC-15006699. Registered 6 July 2015 - Retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-021-01439-3.

Polygenic Markers in Patients Diagnosed of Autosomal Dominant Hypercholesterolemia in Catalonia: Distribution of Weighted LDL-c-Raising SNP Scores and Refinement of Variant Selection

Familial hypercholesterolemia (FH) is associated with mutations in the low-density lipoprotein (LDL) receptor (LDLR), apolipoprotein B (APOB), and proprotein convertase subtilisin/kexin 9 (PCSK9) genes. A pathological variant has not been identified in 30-70% of clinically diagnosed FH patients, and a burden of LDL cholesterol (LDL-c)-raising alleles has been hypothesized as a potential cause of hypercholesterolemia in these patients. Our aim was to study the distribution of weighted LDL-c-raising single-nucleotide polymorphism (SNP) scores (weighted gene scores or wGS) in a population recruited in a clinical setting in Catalonia. The study included 670 consecutive patients with a clinical diagnosis of FH and a prior genetic study involving 250 mutation-positive (FH/M+) and 420 mutation-negative (FH/M-) patients. Three wGSs based on LDL-c-raising variants were calculated to evaluate their distribution among FH patients and compared with 503 European samples from the 1000 Genomes Project. The FH/M- patients had significantly higher wGSs than the FH/M+ and control populations, with sensitivities ranging from 42% to 47%. A wGS based only on the SNPs significantly associated with FH (wGS8) showed a higher area under the receiver operating characteristic curve, and higher diagnostic specificity and sensitivity, with 46.4% of the subjects in the top quartile. wGS8 would allow for the assignment of a genetic cause to 66.4% of the patients if those with polygenic FH are added to the 37.3% of patients with monogenic FH. Our data indicate that a score based on 8 SNPs and the75th percentile cutoff point may identify patients with polygenic FH in Catalonia, although with limited diagnostic sensitivity and specificity.

Genetics of Hypertriglyceridemia

Hypertriglyceridemia, a commonly encountered phenotype in cardiovascular and metabolic clinics, is surprisingly complex. A range of genetic variants, from single-nucleotide variants to large-scale copy number variants, can lead to either the severe or mild-to-moderate forms of the disease. At the genetic level, severely elevated triglyceride levels resulting from familial chylomicronemia syndrome (FCS) are caused by homozygous or biallelic loss-of-function variants in LPL, APOC2, APOA5, LMF1, and GPIHBP1 genes. In contrast, susceptibility to multifactorial chylomicronemia (MCM), which has an estimated prevalence of ~1 in 600 and is at least 50-100-times more common than FCS, results from two different types of genetic variants: (1) rare heterozygous variants (minor allele frequency <1%) with variable penetrance in the five causal genes for FCS; and (2) common variants (minor allele frequency >5%) whose individually small phenotypic effects are quantified using a polygenic score. There is indirect evidence of similar complex genetic predisposition in other clinical phenotypes that have a component of hypertriglyceridemia, such as combined hyperlipidemia and dysbetalipoproteinemia. Future considerations include: (1) evaluation of whether the specific type of genetic predisposition to hypertriglyceridemia affects medical decisions or long-term outcomes; and (2) searching for other genetic contributors, including the role of genome-wide polygenic scores, novel genes, non-linear gene-gene or gene-environment interactions, and non-genomic mechanisms including epigenetics and mitochondrial DNA.

Single nucleotide polymorphisms associated with susceptibility for development of colorectal cancer: Case-control study in a Basque population

Given the significant population diversity in genetic variation, we aimed to investigate whether single nucleotide polymorphisms (SNPs) previously identified in studies of colorectal cancer (CRC) susceptibility were also relevant to the population of the Basque Country (North of Spain). We genotyped 230 CRC cases and 230 healthy controls for 48 previously reported CRC-susceptibility SNPs. Only the rs6687758 in DUPS10 exhibited a statistically significant association with CRC risk based on the crude analysis. The rs6687758 AG genotype conferred about 2.13-fold increased risk for CRC compared to the AA genotype. Moreover, we found significant associations in cases between smoking status, physical activity, and the rs6687758 SNP. The results of a Genetic Risk Score (GRS) showed that the risk alleles were more frequent in cases than controls and the score was associated with CRC in crude analysis. In conclusion, we have confirmed a CRC susceptibility locus and the existence of associations between modifiable factors and the rs6687758 SNP; moreover, the GRS was associated with CRC. However, further experimental validations are needed to establish the role of this SNP, the function of the gene identified, as well as the contribution of the interaction between environmental factors and this locusto the risk of CRC.

The evolution of genetic-based risk scores for lipids and cardiovascular disease

PURPOSE OF REVIEW

With improved next-generation sequencing technology, open-access genetic databases and increased awareness of complex trait genetics, we are entering a new era of risk assessment in which genetic-based risk scores (GRSs) will play a clinical role. We review the concepts underlying polygenic models of disease susceptibility and challenges in clinical implementation.

RECENT FINDINGS

Polygenic risk scores are currently used in genetic research on dyslipidemias and cardiovascular disease (CVD). Although the underlying principles for constructing polygenic scores for lipids are established, the lack of consensus on which score to use is indicated by the large number - about 50 - that have been published. Recently, large-scale polygenic scores for CVD appear to afford superior risk prediction compared to small-scale scores. Despite the potential benefits of GRSs, certain biases towards ethnicity and sex need to be worked through.

SUMMARY

We are on the verge of clinical application of GRSs to provide incremental information on dyslipidemia and CVD risk above and beyond traditional clinical variables. Additional work is required to develop a consensus of how such scores will be constructed and measured in a validated manner, as well as clinical indications for their use.

Polygenic influences on dyslipidemias

PURPOSE OF REVIEW

Rare large-effect genetic variants underlie monogenic dyslipidemias, whereas common small-effect genetic variants - single nucleotide polymorphisms (SNPs) - have modest influences on lipid traits. Over the past decade, these small-effect SNPs have been shown to cumulatively exert consistent effects on lipid phenotypes under a polygenic framework, which is the focus of this review.

RECENT FINDINGS

Several groups have reported polygenic risk scores assembled from lipid-associated SNPs, and have applied them to their respective phenotypes. For lipid traits in the normal population distribution, polygenic effects quantified by a score that integrates several common polymorphisms account for about 20-30% of genetic variation. Among individuals at the extremes of the distribution, that is, those with clinical dyslipidemia, the polygenic component includes both rare variants with large effects and common polymorphisms: depending on the trait, 20-50% of susceptibility can be accounted for by this assortment of genetic variants.

SUMMARY

Accounting for polygenic effects increases the numbers of dyslipidemic individuals who can be explained genetically, but a substantial proportion of susceptibility remains unexplained. Whether documenting the polygenic basis of dyslipidemia will affect outcomes in clinical trials or prospective observational studies remains to be determined.

Genetic risk score based on the lifetime prevalence of femoral fracture in 924 consecutive autopsies of Japanese males

A genetic risk score (GRS) was developed for predicting fracture risk based on lifetime prevalence of femoral fractures in 924 consecutive autopsies of Japanese males. A total of 922 non-synonymous single nucleotide polymorphisms (SNPs) located in 62 osteoporosis susceptibility genes were genotyped and evaluated for their association with the prevalence of femoral fracture in autopsy cases. GRS values were calculated as the sum of risk allele counts (unweighted GRS) or the sum of weighted scores estimated from logistic regression coefficients (weighted GRS). Five SNPs (α-ʟ-iduronidase rs3755955, C7orf58 rs190543052, homeobox C4 rs75256744, G patch domain-containing gene 1 rs2287679, and Werner syndrome rs2230009) showed a significant association (P < 0.05) with the prevalence of femoral fracture in 924 male subjects. Both the unweighted and weighted GRS adequately predicted fracture prevalence; areas under receiver-operating characteristic curves were 0.750 [95 % confidence interval (CI) 0.660-0.840] and 0.770 (95 % CI 0.681-0.859), respectively. Multiple logistic regression analysis revealed that the odds ratio (OR) for the association between fracture prevalence and unweighted GRS ≥3 (n = 124) was 8.39 (95 % CI 4.22-16.69, P < 0.001) relative to a score <3 (n = 797). Likewise, the OR for a weighted GRS of 6-15 (n = 135) was 7.73 (95 % CI 3.89-15.36, P < 0.001) relative to scores of 0-5 (n = 786). The GRS based on risk allele profiles of the five SNPs could help identify at-risk individuals and enable implementation of preventive measures for femoral fracture.

Evaluation of Psoriasis Genetic Risk Based on Five Susceptibility Markers in a Population from Northern Poland

Psoriasis genetic background depends on polygenic and multifactorial mode of inheritance. As in other complex disorders, the estimation of the disease risk based on individual genetic variants is impossible. For this reason, recent investigations have been focused on combinations of known psoriasis susceptibility markers in order to improve the disease risk evaluation. Our aim was to compare psoriasis genetic risk score (GRS) for five susceptibility loci involved in the immunological response (HLA-C, ERAP1, ZAP70) and in the skin barrier function (LCE3, CSTA) between patients with chronic plaque psoriasis (n = 148) and the control group (n = 146). A significantly higher number of predisposing alleles was observed in patients with psoriasis in comparison to healthy individuals (6.1 vs. 5.2, respectively; P = 8.8×10⁻⁷). The statistical significance was even more profound when GRS weighted by logarithm odds ratios was evaluated (P = 9.9×10⁻¹⁴). Our results demonstrate the developed panel of five susceptibility loci to be more efficient in predicting psoriasis risk in the Polish population and to possess higher sensitivity and specificity for the disease than any of the markers analyzed separately, including the most informative HLA-C*06 allele.

A multiple-phenotype imputation method for genetic studies

Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

A genome-wide association study of multiple longitudinal traits with related subjects

Pleiotropy is a phenomenon that a single gene inflicts multiple correlated phenotypic effects, often characterized as traits, involving multiple biological systems. We propose a two-stage method to identify pleiotropic effects on multiple longitudinal traits from a family-based data set. The first stage analyzes each longitudinal trait via a three-level mixed-effects model. Random effects at the subject-level and at the family-level measure the subject-specific genetic effects and between-subjects intraclass correlations within families, respectively. The second stage performs a simultaneous association test between a single nucleotide polymorphism and all subject-specific effects for multiple longitudinal traits. This is performed using a quasi-likelihood scoring method in which the correlation structure among related subjects is adjusted. Two simulation studies for the proposed method are undertaken to assess both the type I error control and the power. Furthermore, we demonstrate the utility of the two-stage method in identifying pleiotropic genes or loci by analyzing the Genetic Analysis Workshop 16 Problem 2 cohort data drawn from the Framingham Heart Study and illustrate an example of the kind of complexity in data that can be handled by the proposed approach. We establish that our two-stage method can identify pleiotropic effects whilst accommodating varying data types in the model.

The Combined Effect of Common Genetic Risk Variants on Circulating Lipoproteins Is Evident in Childhood: A Longitudinal Analysis of the Cardiovascular Risk in Young Finns Study

Low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are modifiable risk factors for cardiovascular disease. Several genetic loci for predisposition to abnormal LDL-C, HDL-C and TG have been identified. However, it remains unclear whether these loci are consistently associated with serum lipid levels at each age or with unique developmental trajectories. Therefore, we assessed the association between genome wide association studies (GWAS) derived polygenic genetic risk scores and LDL-C, HDL-C, and triglyceride trajectories from childhood to adulthood using data available from the 27-year European ‘Cardiovascular Risk in Young Finns’ Study. For 2,442 participants, three weighted genetic risk scores (wGRSs) for HDL-C (38 SNPs), LDL-C (14 SNPs) and triglycerides (24 SNPs) were computed and tested for association with serum lipoprotein levels measured up to 8 times between 1980 and 2011. The categorical analyses revealed no clear divergence of blood lipid trajectories over time between wGRSs categories, with participants in the lower wGRS quartiles tending to have average lipoprotein concentrations 30 to 45% lower than those in the upper-quartile wGRS beginning at age 3 years and continuing through to age 49 years (where the upper-quartile wGRS have 4–7 more risk alleles than the lower wGRS group). Continuous analyses, however, revealed a significant but moderate time-dependent genetic interaction for HDL-C levels, with the association between HDL-C and the continuous HDL-C risk score weakening slightly with age. Conversely, in males, the association between the continuous TG genetic risk score and triglycerides levels tended to be lower in childhood and become more pronounced after the age of 25 years. Although the influence of genetic factors on age-specific lipoprotein values and developmental trajectories is complex, our data show that wGRSs are highly predictive of HDL-C, LDL-C, and triglyceride levels at all ages.

Evaluation of genetic risk scores for prediction of dichotomous outcomes

Substantial uncertainty exists as to whether combining multiple disease-associated single nucleotide polymorphisms (SNPs) into a genotype risk score (GRS) can improve the ability to predict the risk of disease in a clinically relevant way. We calculated the ability of a simple count GRS to predict the risk of a dichotomous outcome under both multiplicative and additive models of combined effects. We then compared the results of these simulations with the observed results of published GRS measured within multiple epidemiologic cohorts. If the combined effect of each disease-associated SNP included in a GRS is multiplicative on the risk scale, then a count GRS score should be useful for risk prediction with as few as 10-20 SNPs. Adding additional SNPs to the GRS under this model dramatically improves risk prediction. By contrast, if the combined effect of each SNP included in a GRS is linearly additive on the risk scale, a simple count GRS is unlikely to provide clinically useful risk prediction. Adding additional SNPs to the GRS under this model does not improve risk prediction. The combined effect of SNPs included in several published GRS measured in several well-phenotyped epidemiologic cohort studies appears to be more consistent with a linearly additive effect. A simple count GRS is unlikely to be clinically useful for predicting the risk of a dichotomous outcome. Alternative methods for constructing GRS that attempt to identify and include SNPs that demonstrate multiplicative gene-gene or gene-environment interactive effects are needed.

A 2-step strategy for detecting pleiotropic effects on multiple longitudinal traits

Genetic pleiotropy refers to the situation in which a single gene influences multiple traits and so it is considered as a major factor that underlies genetic correlation among traits. To identify pleiotropy, an important focus in genome-wide association studies (GWAS) is on finding genetic variants that are simultaneously associated with multiple traits. On the other hand, longitudinal designs are often employed in many complex disease studies, such that, traits are measured repeatedly over time within the same subject. Performing genetic association analysis simultaneously on multiple longitudinal traits for detecting pleiotropic effects is interesting but challenging. In this paper, we propose a 2-step method for simultaneously testing the genetic association with multiple longitudinal traits. In the first step, a mixed effects model is used to analyze each longitudinal trait. We focus on estimation of the random effect that accounts for the subject-specific genetic contribution to the trait; fixed effects of other confounding covariates are also estimated. This first step enables separation of the genetic effect from other confounding effects for each subject and for each longitudinal trait. Then in the second step, we perform a simultaneous association test on multiple estimated random effects arising from multiple longitudinal traits. The proposed method can efficiently detect pleiotropic effects on multiple longitudinal traits and can flexibly handle traits of different data types such as quantitative, binary, or count data. We apply this method to analyze the 16th Genetic Analysis Workshop (GAW16) Framingham Heart Study (FHS) data. A simulation study is also conducted to validate this 2-step method and evaluate its performance.

Polygenic risk for hypertriglyceridemia is attenuated in Japanese men with high fitness levels

High cardiorespiratory fitness (CRF) is associated with a reduced risk for dyslipidemia; however, blood lipid levels are also affected by individual genetic variations. We performed a cross-sectional study to determine whether CRF modifies polygenic risk for dyslipidemia. Serum levels of triglycerides (TG), LDL cholesterol (LDL-C), and HDL cholesterol (HDL-C) were measured in 170 Japanese men (age 20–79 yr). CRF was assessed by measuring maximal oxygen uptake (V̇o2max), and subjects were divided into low-fitness and high-fitness groups according to the reference V̇o2max value for health promotion in Japan. We analyzed 19 single nucleotide polymorphisms (SNPs) associated with TG, LDL-C, or HDL-C levels. Based on these SNPs, we calculated three genetic risk scores (GRSs: TG-GRS, LDL-GRS, and HDL-GRS), and subjects were divided into low, middle, and high groups according to the tertile for each GRS. Serum TG levels of low-fitness individuals were higher in the high and middle TG-GRS groups than in the low TG-GRS group ( P < 0.01 and P < 0.05, respectively), whereas no differences were detected in the TG levels of high-fitness individuals among the TG-GRS groups. In contrast, the high LDL-GRS group had higher LDL-C levels than did the low LDL-GRS group, and HDL-C levels were lower in the high HDL-GRS group than in the low HDL-GRS group regardless of the fitness level ( P < 0.05). These results suggest that high CRF attenuates polygenic risk for hypertriglyceridemia; however, high CRF may not modify the polygenic risk associated with high LDL-C and low HDL-C levels in Japanese men.

A confidence set inference method for identifying SNPs that regulate quantitative phenotypes

AIMS

We introduce a family-based confidence set inference (CSI) method that can be used in preliminary genome-wide association studies to obtain confidence sets of SNPs that contribute a specific percentage to the additive genetic variance of quantitative traits.

METHODS

Developed in the framework of generalized linear mixed models, the method utilizes data from outbred families of arbitrary size and structure. Through our own simulation study and analysis of the Genetics Analysis Workshop 16 simulated data, we study the properties of our method and compare its performance to that of the family association method described by Chen and Abecasis [Am J Hum Genet 2007;81:913-926]. We also analyze the Framingham Heart Study data to identify SNPs regulating high-density lipoprotein levels.

RESULTS

The simulation studies demonstrated that CSI yields confidence sets with correct coverage and that it can outperform the method introduced by Chen and Abecasis [Am J Hum Genet 2007;81:913-926]. Furthermore, we identified five SNPs that potentially regulate high-density lipoprotein levels: rs9989419, rs11586238, rs1754415, rs9355648, and rs9356560.

CONCLUSION

The CSI method provides confidence sets of SNPs that contribute to the genetic variance of quantitative traits and is a competitive alternative to currently used family association methods. The approach is particularly useful in genome-wide association studies as it significantly reduces the number of SNPs investigated in follow-up studies.

Genetic susceptibility, birth weight and obesity risk in young Chinese

OBJECTIVE

Birth weight reflects prenatal metabolic adaption and has been related to later-life obesity risk. This study aimed to evaluate whether birth weight modifies the effect of genetic susceptibility on obesity risk in young Chinese.

METHODS

We recruited 540 young (14-30 years) and obese patients (body mass index, BMI30 kg m(-2)), and 500 age- and sex-matched normal-weight healthy individuals (BMI<23 kg m(-2)). We genotyped 23 BMI-associated genetic variants identified from recent genome-wide association studies (GWAS) in Caucasians with European ancestry with minor allele frequency>0.05 in HapMap Han Chinese in Beijing, China.

RESULTS

Six loci, including SEC16B, GNPDA2, BDNF, FTO, MC4R and TMEM160, were significantly associated with obesity risk, with odds ratio from 1.314 to 1.701. The 23 risk loci accounted for 6.38% of the genetic variance in obesity. We created two genetic risk scores (GRSs) by summing the risk alleles of all 23 (GRS1) and 6 obesity-associated (GRS2) genetic variants. Prediction of obesity was significantly improved (P<0.001) when the GRS1 and GRS2 were added to a model with age and gender, with improvement of discrimination for obesity by 0.8% and 2.7%, respectively. In addition, we found that the two GRSs interacted with birth weight in relation to obesity (Pinteraction<0.001). The genetic effect appeared to be more pronounced in individuals with normal range of birth weight (25-75%) than those with either low (<25%) or high (>75%) birth weight.

CONCLUSION

We confirmed the associations of the single-nucleotide polymorphism tagging six loci reported in recent GWAS with obesity in young Chinese. Our data also suggest birth weight may significantly modify genetic susceptibility to obesity risk.

Sex-specific association of rs16996148 SNP in the NCAN/CILP2/PBX4 and serum lipid levels in the Mulao and Han populations

BACKGROUND

The association of rs16996148 single nucleotide polymorphism (SNP) in NCAN/CILP2/PBX4 and serum lipid levels is inconsistent. Furthermore, little is known about the association of rs16996148 SNP and serum lipid levels in the Chinese population. We therefore aimed to detect the association of rs16996148 SNP and several environmental factors with serum lipid levels in the Guangxi Mulao and Han populations.

METHOD

A total of 712 subjects of Mulao nationality and 736 participants of Han nationality were randomly selected from our stratified randomized cluster samples. Genotyping of the rs16996148 SNP was performed by polymerase chain reaction and restriction fragment length polymorphism combined with gel electrophoresis, and then confirmed by direct sequencing.

RESULTS

The levels of apolipoprotein (Apo) B were higher in Mulao than in Han (P < 0.001). The frequencies of G and T alleles were 87.2% and 12.8% in Mulao, and 89.9% and 10.1% in Han (P <0.05); respectively. The frequencies of GG, GT and TT genotypes were 76.0%, 22.5% and 1.5% in Mulao, and 81.2%, 17.4% and 1.4% in Han (P <0.05); respectively. There were no significant differences in the genotypic and allelic frequencies between males and females in both ethnic groups. The levels of HDL-C, ApoAI, and the ratio of ApoAI to ApoB in Mulao were different between the GG and GT/TT genotypes in males but not in females (P < 0.01 for all), the subjects with GT/TT genotypes had higher serum levels of HDL-C, ApoAI, and the ratio of ApoAI to ApoB than the subjects with GG genotype. The levels of TC, TG, LDL-C, ApoAI, and ApoB in Han were different between the GG and GT/TT genotypes in males but not in females (P < 0.05-0.001), the T allele carriers had higher serum levels of TC, TG, LDL-C, ApoAI, and ApoB than the T allele noncarriers. The levels of HDL-C, ApoAI, and the ratio of ApoAI to ApoB in Mulao were correlated with the genotypes in males (P < 0.05-0.01) but not in females. The levels of TC, TG, HDL-C, LDL-C, ApoAI and ApoB in Han were associated with the genotypes in males (P < 0.05-0.001) but not in females. Serum lipid parameters were also correlated with several enviromental factors in both ethnic groups (P < 0.05-0.001).

CONCLUSIONS

The genotypic and allelic frequencies of rs16996148 SNP and the associations of the SNP and serum lipid levels are different in the Mulao and Han populations. Sex (male)-specific association of rs16996148 SNP in the NCAN/CILP2/PBX4 and serum lipid levels is also observed in the both ethnic groups.

Genetic variants and blood pressure in a population-based cohort: the Cardiovascular Risk in Young Finns study

Clinical relevance of a genetic predisposition to elevated blood pressure was quantified during the transition from childhood to adulthood in a population-based Finnish cohort (N=2357). Blood pressure was measured at baseline in 1980 (age 3-18 years) and in follow-ups in 1983, 1986, 2001, and 2007. Thirteen single nucleotide polymorphisms associated with blood pressure were genotyped, and 3 genetic risk scores associated with systolic and diastolic blood pressures and their combination were derived for all of the participants. Effects of the genetic risk score were 0.47 mm Hg for systolic and 0.53 mm Hg for diastolic blood pressures (both P<0.01). The combination genetic risk score was associated with diastolic blood pressure from age 9 years onward (β=0.68 mm Hg; P=0.015). Replications in 1194 participants of the Bogalusa Heart Study showed essentially similar results. The participants in the highest quintile of the combination genetic risk score had a 1.82-fold risk of hypertension in adulthood (P<0.0001) compared with the lowest quintile, independent of a family history of premature hypertension. These findings show that genetic variants are associated with preclinical blood pressure traits in childhood; individuals with several susceptibility alleles have, on average, a 0.5-mm Hg higher blood pressure, and this trajectory continues from childhood to adulthood.

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.

Exploring multilocus associations of inflammation genes and colorectal cancer risk using hapConstructor

BACKGROUND

In candidate-gene association studies of single nucleotide polymorphisms (SNPs), multilocus analyses are frequently of high dimensionality when considering haplotypes or haplotype pairs (diplotypes) and differing modes of expression. Often, while candidate genes are selected based on their biological involvement in a given pathway, little is known about the functionality of SNPs to guide association studies. Investigators face the challenge of exploring multiple SNP models to elucidate which variants, independently or in combination, might be associated with a disease of interest. A data mining module, hapConstructor (freely-available in Genie software) performs systematic construction and association testing of multilocus genotype data in a Monte Carlo framework. Our objective was to assess its utility to guide statistical analyses of haplotypes within a candidate region (or combined genotypes across candidate genes) beyond that offered by a standard logistic regression approach.

METHODS

We applied the hapConstructor method to a multilocus investigation of candidate genes involved in pro-inflammatory cytokine IL6 production, IKBKB, IL6, and NFKB1 (16 SNPs total) hypothesized to operate together to alter colorectal cancer risk. Data come from two U.S. multicenter studies, one of colon cancer (1,556 cases and 1,956 matched controls) and one of rectal cancer (754 cases and 959 matched controls).

RESULTS

hapConstructor enabled us to identify important associations that were further analyzed in logistic regression models to simultaneously adjust for confounders. The most significant finding (nominal P = 0.0004; false discovery rate q = 0.037) was a combined genotype association across IKBKB SNP rs5029748 (1 or 2 variant alleles), IL6 rs1800797 (1 or 2 variant alleles), and NFKB1 rs4648110 (2 variant alleles) which conferred an ~80% decreased risk of colon cancer.

CONCLUSIONS

Strengths of hapConstructor were: systematic identification of multiple loci within and across genes important in CRC risk; false discovery rate assessment; and efficient guidance of subsequent logistic regression analyses.

Joint effects of common genetic variants from multiple genes and pathways on the risk of premature coronary artery disease

OBJECTIVE

The aim of this study is to discover common variants in 6 lipid metabolic genes and construct and validate a genetic risk score (GRS) based on the joint effects of genetic variants in multiple genes from lipid and other pathobiologic pathways.

BACKGROUND

Explaining the genetic basis of coronary artery disease (CAD) is incomplete. Discovery and aggregation of genetic variants from multiple pathways may advance this objective.

METHODS

Premature CAD cases (n = 1,947) and CAD-free controls (n = 1,036) were selected from our angiographic registry. In a discovery phase, single nucleotide polymorphisms (SNPs) at 56 loci from internal discovery and external reports were tested for associations with biomarkers and CAD: 28 promising SNPs were then tested jointly for CAD associations, and a GRS consisting of SNPs contributing independently was constructed and validated in a replication set of familial cases and population-based controls (n = 1,320).

RESULTS

Five variants contributed jointly to CAD prediction in a multigenic GRS model: odds ratio 1.24 (95% CI 1.16-1.33) per risk allele, P = 8.2 x 10(-11), adjusted OR 2.03 (1.53-2.70), fourth versus first quartile. 5-SNP genetic risk score had minor impact on area under the receiver operating characteristic curve (P > .05) but resulted in substantial net reclassification improvement: 0.16 overall, 0.28 in intermediate-risk patients (both P < .0001). GRS(5) predicted familial CAD with similar magnitude in the validation set.

CONCLUSIONS

The Intermountain Healthcare's Coronary Genetics study demonstrates the ability of a multigenic, multipathway GRS to improve discrimination of angiographic CAD. Genetic risk scores promise to increase understanding of the genetic basis of CAD and improve identification of individuals at increased CAD risk.

Analysis of multiple phenotypes

The complex etiology of common diseases like cardiovascular disease, diabetes, hypertension, and rheumatoid arthritis has led investigators to focus on the genetics of correlated phenotypes and risk factors. Joint analysis of multiple disease-related phenotypes may reveal genes of pleiotropic effect and increase analytical power, but at the cost of increased analytical and computational complexity. All three data sets provided for analysis at the Genetic Analysis Workshop 16 offered multiple quantitative measures of phenotypes related to underlying disease processes as well as discrete measures of affection status. Participants in Group 6 addressed the challenges and possibilities of association analysis of these data sets on multiple levels, including phenotype definition and data reduction, multivariate approaches to gene discovery, analysis of causality and data structure, and development of predictive models. These approaches included combinations of continuous and discrete phenotypes, use of repeated measures in longitudinal data, and models that included multiple phenotypic measures and multiple single-nucleotide polymorphism variants. Most research teams regarded the use of multiple related phenotypes as a tool for increasing analytical power, as well as for clarifying the underlying biology of complex diseases.