不同SNPs阈值下机器学习与多基因风险评分的阿尔茨海默症遗传风险预测研究

目的 基于全基因组汇总数据的不同单核苷酸多态性(single nucleotide polymorphisms,SNPs)阈值,探索机器学习(machine learning,ML)与多基因风险评分(polygenic risk score,PRS)在阿尔茨海默症(Alzheimer’s disease,AD)遗传风险统计建模上的预测效果,为全基因组高维数据下的AD遗传风险预测提供更为快速有效的统计建模策略。方法 将SNPs按照不同阈值(1×10^-8、1×10^-7、1×10^-6、1×10^-5、1×10^-4、1×10^-3)进行划分,并基于PRS、least absolute shrinkage and selection operator(LASSO)、elastic net(EN)、ridge、random forest(RF)、extreme gradient bosting(XGBoost)模型对AD遗传风险预测进行统计建模。采用十折交叉验证,以AUC...     

Testing for Polygenic Effects in Genome‐Wide Association Studies

To confirm associations with a large number of single nucleotide polymorphisms (SNPs), each with only a small effect size, as hypothesized in the polygenic theory for schizophrenia, the International Schizophrenia Consortium (2009, Nature 460:748–752) proposed a polygenic risk score (PRS) test and demonstrated its effectiveness when applied to psychiatric disorders. The basic idea of the PRS test is to use a half of the sample to select and up‐weight those more likely to be associated SNPs, and then use the other half of the sample to test for aggregated effects of the selected SNPs. Intrigued by the novelty and increasing use of the PRS test, we aimed to evaluate and improve its performance for GWAS data. First, by an analysis of the PRS test, we point out its connection with the Sum test [Chapman and Whittaker, 2008 , Genet Epidemiol 32:560–566; Pan, 2009 , Genet Epidemiol 33:497–507]; given the known advantages and disadvantages of the Sum test, this connection motivated the development of several other polygenic tests, some of which may be more powerful than the PRS test under certain situations. Second, more importantly, to overcome the low statistical efficiency of the data‐splitting strategy as adopted in the PRS test, we reformulate and thus modify the PRS test, obtaining several adaptive tests, which are closely related to the adaptive sum of powered score (SPU) test studied in the context of rare variant analysis [Pan et al., 2014, Genetics 197:1081–1095]. We use both simulated data and a real GWAS dataset of alcohol dependence to show dramatically improved power of the new tests over the PRS test; due to its superior performance and simplicity, we recommend the whole sample‐based adaptive SPU test for polygenic testing. We hope to raise the awareness of the limitations of the PRS test and potential power gain of the adaptive SPU test.     

Multiethnic polygenic risk scores improve risk prediction in diverse populations

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (N_eff = 40k) and Latino training data in small sample size (N_eff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R² = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (N_eff = 40k) and South Asian (N_eff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non‐European target populations.     

Unidentified genetic variants influence pancreatic cancer risk: an analysis of polygenic susceptibility in the PanScan study

Genome-wide association (GWA) studies have identified several pancreatic cancer (PanCa) susceptibility loci. Methods for assessment of polygenic susceptibility can be employed to detect the collective effect of additional association signals for PanCa. Using data on 492,651 autosomal single nucleotide polymorphisms (SNPs) from the PanScan GWA study (2,857 cases, 2,967 controls), we employed polygenic risk score (PRS) cross-validation (CV) methods to (a) confirm the existence of unidentified association signals, (b) assess the predictive value of PRSs, and (c) assess evidence for polygenic effects in specific genomic locations (genic vs. intergenic). After excluding SNPs in known PanCa susceptibility regions, we constructed PRS models using a training GWA dataset and then tested the model in an independent testing dataset using fourfold CV. We also employed a "power-replication" approach, where power to detect SNP associations was calculated using a training dataset, and power was tested for association with "replication status" in a testing dataset. PRS scores constructed using ≥ 10% of genome-wide SNPs showed significant association with PanCa (P< 0.05) across the majority of CV analyses. Associations were stronger for PRSs restricted to genic SNPs compared to intergenic PRSs. The power-replications approach produced weaker associations that were not significant when restricting to SNPs with low pairwise linkage disequilibrium, whereas PRS results were robust to such restrictions. Although the PRS approach will not dramatically improve PanCa prediction, it provides strong evidence for unidentified association signals for PanCa. Our results suggest that focusing association studies on genic regions and conducting larger GWA studies can reveal additional PanCa susceptibility loci.     

Assessing potential shared genetic aetiology between body mass index and sleep duration in 142,209 individuals

Observational studies find an association between increased body mass index (BMI) and short self-reported sleep duration in adults. However, the underlying biological mechanisms that underpin these associations are unclear. Recent findings from the UK Biobank suggest a weak genetic correlation between BMI and self-reported sleep duration. However, the potential shared genetic aetiology between these traits has not been examined using a comprehensive approach. To investigate this, we created a polygenic risk score (PRS) of BMI and examined its association with self-reported sleep duration in a combination of individual participant data and summary-level data, with a total sample size of 142,209 individuals. Although we observed a nonsignificant genetic correlation between BMI and sleep duration, using LD score regression (r_g = −0.067 [SE = 0.039], P = 0.092) we found that a PRS of BMI is associated with a decrease in sleep duration (unstandardized coefficient = −1.75 min [SE = 0.67], P = 6.13 × 10⁻⁷), but explained only 0.02% of the variance in sleep duration. Our findings suggest that BMI and self-reported sleep duration possess a small amount of shared genetic aetiology and other mechanisms must underpin these associations.     

Genome-wide association study of circulating folate one-carbon metabolites

Experimental, observational, and clinical trials support a critical role of folate one-carbon metabolism (FOCM) in colorectal cancer (CRC) development. In this report, we focus on understanding the relationship between common genetic variants and metabolites of FOCM. We conducted a genome-wide association study of FOCM biomarkers among 1,788 unaffected (without CRC) individuals of European ancestry from the Colon Cancer Family Registry. Twelve metabolites, including 5-methyltetrahydrofolate, vitamin B₂ (flavin mononucleotide and riboflavin), vitamin B₆ (4-pyridoxic acid, pyridoxal, and pyridoxamine), total homocysteine, methionine, S-adenosylmethionine, S-adenosylhomocysteine, cystathionine, and creatinine were measured from plasma using liquid chromatography-mass spectrometry (LC-MS) or LC-MS/MS. For each individual biomarker, we estimated genotype array-specific associations followed by a fixed-effect meta-analysis. We identified the variant rs35976024 (at 2p11.2 and intronic of ATOH8) associated with total homocysteine (p = 4.9 × 10⁻⁸). We found a group of six highly correlated variants on chromosome 15q14 associated with cystathionine (all p < 5 × 10⁻⁸), with the most significant variant rs28391580 (p = 2.8 × 10⁻⁸). Two variants (rs139435405 and rs149119426) on chromosome 14q13 showed significant (p < 5 × 10⁻⁸) associations with S-adenosylhomocysteine. These three biomarkers with significant associations are closely involved in homocysteine metabolism. Furthermore, when assessing the principal components (PCs) derived from seven individual biomarkers, we identified the variant rs12665366 (at 6p25.3 and intronic of EXOC2) associated with the first PC (p = 2.3 × 10⁻⁸). Our data suggest that common genetic variants may play an important role in FOCM, particularly in homocysteine metabolism.     

A principal component approach to improve association testing with polygenic risk scores

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS–PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS–PCA approach with a permutation test and an a priori selected p-value threshold. The PRS–PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.     

Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake

25-Hydroxyvitamin D (25(OH)D) concentration is a complex trait with genetic and environmental predictors that may determine how much vitamin D exposure is required to reach optimal concentration. Interactions between continuous measures of a polygenic score (PGS) and vitamin D intake (PGS*intake) or available ultraviolet (UV) radiation (PGS*UV) were evaluated in individuals of African (n = 1,099) or European (n = 8,569) ancestries. Interaction terms and joint effects (main and interaction terms) were tested using one-degree of freedom (1-DF) and 2-DF models, respectively. Models controlled for age, sex, body mass index, cohort, and dietary intake/available UV. In addition, in participants achieving Institute of Medicine (IOM) vitamin D intake recommendations, 25(OH)D was evaluated by level PGS. The 2-DF PGS*intake, 1-DF PGS*UV, and 2-DF PGS*UV results were statistically significant in participants of European ancestry (p = 3.3 × 10⁻¹⁸, p = 2.1 × 10⁻², and p = 2.4 × 10⁻¹⁹, respectively), but not in those of African ancestry. In European-ancestry participants reaching IOM vitamin D intake guidelines, the percent of participants achieving adequate 25(OH)D ( > 20 ng/ml) increased as genetic risk decreased (72% vs. 89% in highest vs. lowest risk; p = .018). Available UV radiation and vitamin D intake interact with genetics to influence 25(OH)D. Individuals with higher genetic risk may require more vitamin D exposure to maintain optimal 25(OH)D concentrations.     

Evaluation of polygenic risk scores for predicting breast and prostate cancer risk

Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores.     

Analysis of single-locus tests to detect gene/disease associations

A goal of association analysis is to determine whether variation in a particular candidate region or gene is associated with liability to complex disease. To evaluate such candidates, ubiquitous Single Nucleotide Polymorphisms (SNPs) are useful. It is critical, however, to select a set of SNPs that are in substantial linkage disequilibrium (LD) with all other polymorphisms in the region. Whether there is an ideal statistical framework to test such a set of ‘tag SNPs’ for association is unknown. Compared to tests for association based on frequencies of haplotypes, recent evidence suggests tests for association based on linear combinations of the tag SNPs (Hotelling T² test) are more powerful. Following this logical progression, we wondered if single‐locus tests would prove generally more powerful than the regression‐based tests? We answer this question by investigating four inferential procedures: the maximum of a series of test statistics corrected for multiple testing by the Bonferroni procedure, T_B, or by permutation of case‐control status, T_P; a procedure that tests the maximum of a smoothed curve fitted to the series of of test statistics, T_S; and the Hotelling T² procedure, which we call T_R. These procedures are evaluated by simulating data like that from human populations, including realistic levels of LD and realistic effects of alleles conferring liability to disease. We find that power depends on the correlation structure of SNPs within a gene, the density of tag SNPs, and the placement of the liability allele. The clearest pattern emerges between power and the number of SNPs selected. When a large fraction of the SNPs within a gene are tested, and multiple SNPs are highly correlated with the liability allele, T_S has better power. Using a SNP selection scheme that optimizes power but also requires a substantial number of SNPs to be genotyped (roughly 10–20 SNPs per gene), power of T_P is generally superior to that for the other procedures, including T_R. Finally, when a SNP selection procedure that targets a minimal number of SNPs per gene is applied, the average performances of T_P and T_R are indistinguishable. Genet. Epidemiol. © 2005 Wiley‐Liss, Inc.     

Genetic predisposition to obesity widens and skews the BMI distribution

Genetic studies of BMI have largely focused on how average BMI changes with SNPs or polygenic risk scores (PRS). We examine the effects of a BMI PRS on changes in BMI percentiles, range, and skewness using quantile regression and US nationally representative data from the Health and Retirement Survey. We find that the BMI PRS is associated with meaningfully larger weight increases at higher than lower BMI percentiles; the 90th BMI percentile increases by 4.7 units with a one SD increase in the PRS compared to 1.5 units at the 10th percentile (both effects individually significant and significantly different from each other p ≤ 0.0001). Our results suggest that PRS effects at average BMI mask substantial heterogeneity for individuals ranking at different BMI percentiles and that genetic effects are associated with greater spread and right skewness of the BMI distribution.     

Genetic Prediction of Quantitative Lipid Traits: Comparing Shrinkage Models to Gene Scores

Accurate genetic prediction of quantitative traits related to complex disease risk would have potential clinical impact, so investigation of statistical methodology to improve predictive performance is important. We compare a simple approach of polygenic scores using top ranking single nucleotide polymorphisms (SNPs) to a set of shrinkage models, namely Ridge Regression, Lasso and Hyper‐Lasso. These penalised regression methods analyse all genotyped SNPs simultaneously, potentially including much larger sets of SNPs in the models, not only those with the smallest P values. We compare the accuracy of these models for predicting low‐density lipoprotein (LDL) and high‐density lipoprotein (HDL) cholesterol, two lipid traits of clinical relevance, in the Whitehall II and British Women's Health and Heart Study cohorts, using SNPs from the HumanCVD BeadChip. For gene scores, the most accurate predictions arise from multivariate weighted scores and include only a small number of SNPs, identified as top hits by the HumanCVD BeadChip. Furthermore, there was little benefit from including external results from published sets of SNPs. We found that shrinkage approaches rarely improved significantly on gene score results. Genetic predictive performance is trait specific, depending on the heritability and genetic architecture of the trait, and is limited by the training data sample size. Our results for lipid traits suggest no current benefit of more complex methods over existing gene score methods. Instead, the most important choice for the prediction model is the number of SNPs and selection of the most predictive SNPs to include. However further comparisons, in larger samples and for other phenotypes, would still be of interest.     

Genetic associations with childhood brain growth,defined in two longitudinal cohorts

Genome‐wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross‐sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome‐wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention‐deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population‐based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta‐analysis identified a genome‐wide significant intergenic SNP (rs12386571, P = 9.09 × 10^?9), near AKR1B10. This gene is part of the aldo‐keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study.     

A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact

Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population‐based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where . Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, , and the residual polygenic component due to the postulated but as yet unknown genetic variants, . The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population‐based family data and established risk variants exist. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. 35: 549‐556, 2011     

Evaluating shared genetic influences on nonsyndromic cleft lip/palate and oropharyngeal neoplasms

It has been hypothesised that nonsyndromic cleft lip/palate (nsCL/P) and cancer may share aetiological risk factors. Population studies have found inconsistent evidence for increased incidence of cancer in nsCL/P cases, but several genes (e.g., CDH1, AXIN2) have been implicated in the aetiologies of both phenotypes. We aimed to evaluate shared genetic aetiology between nsCL/P and oral cavity/oropharyngeal cancers (OC/OPC), which affect similar anatomical regions. Using a primary sample of 5,048 OC/OPC cases and 5,450 controls of European ancestry and a replication sample of 750 cases and 336,319 controls from UK Biobank, we estimate genetic overlap using nsCL/P polygenic risk scores (PRS) with Mendelian randomization analyses performed to evaluate potential causal mechanisms. In the primary sample, we found strong evidence for an association between a nsCL/P PRS and increased odds of OC/OPC (per standard deviation increase in score, odds ratio [OR]: 1.09; 95% confidence interval [CI]: 1.04, 1.13; p = .000053). Although confidence intervals overlapped with the primary estimate, we did not find confirmatory evidence of an association between the PRS and OC/OPC in UK Biobank (OR 1.02; 95% CI: 0.95, 1.10; p = .55). Mendelian randomization analyses provided evidence that major nsCL/P risk variants are unlikely to influence OC/OPC. Our findings suggest possible shared genetic influences on nsCL/P and OC/OPC.     

Studying the Genetics of Complex Disease With Ancestry‐Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations

Genome‐wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry‐specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P‐value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all‐inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry‐specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry‐specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry‐specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine.     

African and non‐African admixture components in African Americans and an African Caribbean population

Admixture is a potential source of confounding in genetic association studies, so it becomes important to detect and estimate admixture in a sample of unrelated individuals. Populations of African descent in the US and the Caribbean share similar historical backgrounds but the distributions of African admixture may differ. We selected 416 ancestry informative markers (AIMs) to estimate and compare admixture proportions using STRUCTURE in 906 unrelated African Americans (AAs) and 294 Barbadians (ACs) from a study of asthma. This analysis showed AAs on average were 72.5% African, 19.6% European and 8% Asian, while ACs were 77.4% African, 15.9% European, and 6.7% Asian which were significantly different. A principal components analysis based on these AIMs yielded one primary eigenvector that explained 54.04% of the variation and captured a gradient from West African to European admixture. This principal component was highly correlated with African vs. European ancestry as estimated by STRUCTURE (r²=0.992, r²=0.912, respectively). To investigate other African contributions to African American and Barbadian admixture, we performed PCA on ∼14,000 (14k) genome‐wide SNPs in AAs, ACs, Yorubans, Luhya and Maasai African groups, and estimated genetic distances (F_ST). We found AAs and ACs were closest genetically (F_ST=0.008), and both were closer to the Yorubans than the other East African populations. In our sample of individuals of African descent, ∼400 well‐defined AIMs were just as good for detecting substructure as ∼14,000 random SNPs drawn from a genome‐wide panel of markers. Genet. Epidemiol. 34:561–568, 2010.© 2010 Wiley‐Liss, Inc.     

基因-基因间交互作用对学龄儿童腹型肥胖的影响

目的 探讨我国学龄儿童6个肥胖相关基因多态性位点（SNPs）及其交互作用与腹型肥胖的关联。方法 以"北京市儿童青少年代谢综合征（BCAMS）研究"中1 196名肥胖儿童和2 306名非肥胖儿童为研究对象。采用盐析法从外周血白细胞中提取DNA。使用ABI Prisms^TM-7900实时荧光定量PCR仪对6个SNPs（FTO rs9939609、MC4R rs17782313、BDNF rs6265、PCSK1 rs6235、SH2B1 rs4788102和CSK rs1378942）进行分型检测。采用BCAMS基线总人群腰围的性别年龄别第90百分位值判定腹型肥胖。运用logistic回归模型分析6个SNPs与腹型肥胖的关联。采用广义多因子降维法（GMDR）模型检测6个SNPs之间的基因-基因交互作用,并使用多因素logistic回归模型验证。结果 在加性遗传模型下,调整性别、年龄、Tanner分期、体力活动和肥胖家族史后,FTO rs9939609-A、MC4R rs17782313-C和BDNF rs6265-G等位基因增加儿童腹型肥胖罹患风险（OR=1.24,95%CI：1.06~1.45,P=0.008;OR=1.26,95%CI：1.11~1.43,P=2.98×10^-4;OR=1.18,95%CI：1.06~1.32,P=0.003）。GMDR模型分析显示,在调整同样的影响因素后,MC4R rs17782313和BDNF rs6265之间交互作用的差异有统计学意义（P=0.001）,交叉验证一致性为10/10,平均检验准确度为0.539,为最优模型;logistic回归分析显示,MC4R rs17782313-C和BDNF rs6265-G可能存在正交互作用。结论 FTO rs9939609-A、MC4R rs17782313-C和BDNF rs6265-G增加儿童腹型肥胖罹患风险;MC4R rs17782313与BDNF rs6265可能存在交互作用,对学龄儿童腹型肥胖的罹患风险存在影响。     

A Genome‐Wide Association Study of Early Spontaneous Preterm Delivery

Preterm birth is the leading cause of infant morbidity and mortality. Despite extensive research, the genetic contributions to spontaneous preterm birth (SPTB) are not well understood. Term controls were matched with cases by race/ethnicity, maternal age, and parity prior to recruitment. Genotyping was performed using Affymetrix SNP Array 6.0 assays. Statistical analyses utilized PLINK to compare allele occurrence rates between case and control groups, and incorporated quality control and multiple‐testing adjustments. We analyzed DNA samples from mother–infant pairs from early SPTB cases (20^0/7–33^6/7 weeks, 959 women and 979 neonates) and term delivery controls (39^0/7–41^6/7 weeks, 960 women and 985 neonates). For validation purposes, we included an independent validation cohort consisting of early SPTB cases (293 mothers and 243 infants) and term controls (200 mothers and 149 infants). Clustering analysis revealed no population stratification. Multiple maternal SNPs were identified with association P‐values between 10 × 10^–5 and 10 × 10^–6. The most significant maternal SNP was rs17053026 on chromosome 3 with an odds ratio (OR) 0.44 with a P‐value of 1.0 × 10^–6. Two neonatal SNPs reached the genome‐wide significance threshold, including rs17527054 on chromosome 6p22 with a P‐value of 2.7 × 10^–12 and rs3777722 on chromosome 6q27 with a P‐value of 1.4 × 10^–10. However, we could not replicate these findings after adjusting for multiple comparisons in a validation cohort. This is the first report of a genome‐wide case‐control study to identify single nucleotide polymorphisms (SNPs) that correlate with SPTB.     

Genetic Markers Associated With Plasma Protein C Level in African Americans: The Atherosclerosis Risk in Communities (ARIC) Study

Protein C is an endogenous anticoagulant protein with anti‐inflammatory properties. Single‐nucleotide polymorphisms (SNPs) affect the levels of circulating protein C in European Americans. We performed a genome‐wide association (GWA) scan of plasma protein C concentration with approximately 2.5 million SNPs in 2,701 African Americans in the Atherosclerosis Risk in Communities Study. Seventy‐nine SNPs from the 20q11 and 2q14 regions reached the genome‐wide significance threshold of 5 × 10^‐8. A missense variant rs867186 in the PROCR gene at 20q11 is known to affect protein C levels in individuals of European descent and showed the strongest signal (P = 9.84 × 10^‐65) in African Americans. The minor allele of this SNP was associated with higher protein C levels (β = 0.49 μg/ml; 10% variance explained). In the 2q14 region, the top SNPs were near or within the PROC gene: rs7580658 (β = 0.15 μg/ml; 2% variance explained, P = 1.7 × 10^‐12) and rs1799808 (β = 0.15 μg/ml; 2% variance explained, P = 2.03 × 10^‐12). These two SNPs were in strong linkage disequilibrium (LD) with another SNP rs1158867 that resides in a biochemically functional site and in weak to strong LD with the top PROC variants previously reported in individuals of European descent. In addition, two variants outside the PROC region were significantly and independently associated with protein C levels: rs4321325 in CYP27C1 and rs13419716 in MYO7B. In summary, this first GWA study for plasma protein C levels in African Americans confirms the associations of SNPs in the PROC and PROCR regions with circulating levels of protein C across ethnic populations and identifies new candidates for protein C regulation.