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.     

Genetic Variants Influencing Joint Damage in Mexican Americans and European Americans With Rheumatoid Arthritis

Joint destruction in rheumatoid arthritis (RA) is heritable, but knowledge on specific genetic determinants of joint damage in RA is limited. We have used the Immunochip array to examine whether genetic variants influence variation in joint damage in a cohort of Mexican Americans (MA) and European Americans (EA) with RA. We studied 720 MA and 424 EA patients with RA. Joint damage was quantified using a radiograph of both hands and wrists, scored using Sharp's technique. We conducted association analyses with the transformed Sharp score and the Immunochip single nucleotide polymorphism (SNP) data using PLINK. In MAs, 15 SNPs from chromosomes 1, 5, 9, 17 and 22 associated with joint damage yielded strong p‐values (p < 1 × 10^?4). The strongest association with joint damage was observed with rs7216796, an intronic SNP located in the MAP3K14 gene, on chromosome 17 (β ± SE = ?0.25 ± 0.05, p = 6.23 × 10^?6). In EAs, 28 SNPs from chromosomes 1, 4, 6, 9, and 21 showed associations with joint damage (p‐value < 1 × 10^?4). The best association was observed on chromosome 9 with rs59902911 (β ± SE = 0.86 ± 0.17, p = 1.01 × 10^?6), a synonymous SNP within the CARD9 gene. We also observed suggestive evidence for some loci influencing joint damage in MAs and EAs. We identified two novel independent loci (MAP3K14 and CARD9) strongly associated with joint damage in MAs and EAs and a few shared loci showing suggestive evidence for association.     

Genome‐Wide Association Study of a Heart Failure Related Metabolomic Profile Among African Americans in the Atherosclerosis Risk in Communities (ARIC) Study

Both the prevalence and incidence of heart failure (HF) are increasing, especially among African Americans, but no large‐scale, genome‐wide association study (GWAS) of HF‐related metabolites has been reported. We sought to identify novel genetic variants that are associated with metabolites previously reported to relate to HF incidence. GWASs of three metabolites identified previously as risk factors for incident HF (pyroglutamine, dihydroxy docosatrienoic acid, and X‐11787, being either hydroxy‐leucine or hydroxy‐isoleucine) were performed in 1,260 African Americans free of HF at the baseline examination of the Atherosclerosis Risk in Communities (ARIC) study. A significant association on chromosome 5q33 (rs10463316, MAF = 0.358, P‐value = 1.92 × 10^?10) was identified for pyroglutamine. One region on chromosome 2p13 contained a nonsynonymous substitution in N‐acetyltransferase 8 (NAT8) was associated with X‐11787 (rs13538, MAF = 0.481, P‐value = 1.71 × 10^?23). The smallest P‐value for dihydroxy docosatrienoic acid was rs4006531 on chromosome 8q24 (MAF = 0.400, P‐value = 6.98 × 10^?7). None of the above SNPs were individually associated with incident HF, but a genetic risk score (GRS) created by summing the most significant risk alleles from each metabolite detected 11% greater risk of HF per allele. In summary, we identified three loci associated with previously reported HF‐related metabolites. Further use of metabolomics technology will facilitate replication of these findings in independent samples.     

A genetic stochastic process model for genome‐wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post‐GWAS (where GWAS is genome‐wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene‐environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow‐up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10^?7). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10^?7). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.     

A Genome‐Wide Association Study for Serum Bilirubin Levels and Gene‐Environment Interaction in a Chinese Population

Bilirubin is an effective antioxidant and is influenced by both genetic and environmental factors. Recent genome‐wide association studies (GWAS) have identified multiple loci affecting serum total bilirubin levels. However, most of the studies were conducted in European populations and little attention has been devoted either to genetic variants associated with direct and indirect bilirubin levels or to the gene‐environment interactions on bilirubin levels. In this study, a two‐stage GWAS was performed to identify genetic variants associated with all types of bilirubin levels in 10,282 Han Chinese individuals. Gene‐environment interactions were further examined. Briefly, two previously reported loci, UGT1A1 on 2q37 (rs6742078 and rs4148323, combined P = 1.44 × 10^?89 and P = 5.05 × 10^?69, respectively) and SLCO1B3 on 12p12 (rs2417940, combined P = 6.93 × 10^?19) were successfully replicated. The two loci explained 9.2% and 0.9% of the total variations of total bilirubin levels, respectively. Ethnic genetic differences were observed between Chinese and European populations. More importantly, a significant interaction was found between rs2417940 in SLCO1B3 gene and smoking on total bilirubin levels (P = 1.99 × 10^?3). Single nucleotide polymorphism (SNP) rs2417940 had stronger effects on total bilirubin levels in nonsmokers than in smokers, suggesting that the effects of SLCO1B3 genotype on bilirubin levels were partly dependent on smoking status. Consistent associations and interactions were observed for serum direct and indirect bilirubin levels.     

A genome‐wide linkage and association analysis of imputed insertions and deletions with cardiometabolic phenotypes in Mexican Americans: The Insulin Resistance Atherosclerosis Family Study

Insertions and deletions (INDELs) represent a significant fraction of interindividual variation in the human genome yet their contribution to phenotypes is poorly understood. To confirm the quality of imputed INDELs and investigate their roles in mediating cardiometabolic phenotypes, genome‐wide association and linkage analyses were performed for 15 phenotypes with 1,273,952 imputed INDELs in 1,024 Mexican‐origin Americans. Imputation quality was validated using whole exome sequencing with an average kappa of 0.93 in common INDELs (minor allele frequencies [MAFs] ≥ 5%). Association analysis revealed one genome‐wide significant association signal for the cholesterylester transfer protein gene (CETP ) with high‐density lipoprotein levels (rs36229491, P = 3.06 × 10^?12); linkage analysis identified two peaks with logarithm of the odds (LOD) > 5 (rs60560566, LOD = 5.36 with insulin sensitivity (S _I) and rs5825825, LOD = 5.11 with adiponectin levels). Suggestive overlapping signals between linkage and association were observed: rs59849892 in the WSC domain containing 2 gene (WSCD2 ) was associated and nominally linked with S _I (P = 1.17 × 10^?7, LOD = 1.99). This gene has been implicated in glucose metabolism in human islet cell expression studies. In addition, rs201606363 was linked and nominally associated with low‐density lipoprotein (P = 4.73 × 10^?4, LOD = 3.67), apolipoprotein B (P = 1.39 × 10^?3, LOD = 4.64), and total cholesterol (P = 1.35 × 10^?2, LOD = 3.80) levels. rs201606363 is an intronic variant of the UBE2F‐SCLY (where UBE2F is ubiquitin‐conjugating enzyme E2F and SCLY is selenocysteine lyase) fusion gene that may regulate cholesterol through selenium metabolism. In conclusion, these results confirm the feasibility of imputing INDELs from array‐based single nucleotide polymorphism (SNP) genotypes. Analysis of these variants using association and linkage replicated previously identified SNP signals and identified multiple novel INDEL signals. These results support the inclusion of INDELs into genetic studies to more fully interrogate the spectrum of genetic variation.     

Efficient Generalized Least Squares Method for Mixed Population and Family‐based Samples in Genome‐wide Association Studies

Genome‐wide association studies (GWAS) that draw samples from multiple studies with a mixture of relationship structures are becoming more common. Analytical methods exist for using mixed‐sample data, but few methods have been proposed for the analysis of genotype‐by‐environment (G×E) interactions. Using GWAS data from a study of sarcoidosis susceptibility genes in related and unrelated African Americans, we explored the current analytic options for genotype association testing in studies using both unrelated and family‐based designs. We propose a novel method—generalized least squares (GLX)—to estimate both SNP and G×E interaction effects for categorical environmental covariates and compared this method to generalized estimating equations (GEE), logistic regression, the Cochran–Armitage trend test, and the W_QLS and M_QLS methods. We used simulation to demonstrate that the GLX method reduces type I error under a variety of pedigree structures. We also demonstrate its superior power to detect SNP effects while offering computational advantages and comparable power to detect G×E interactions versus GEE. Using this method, we found two novel SNPs that demonstrate a significant genome‐wide interaction with insecticide exposure—rs10499003 and rs7745248, located in the intronic and 3' UTR regions of the FUT9 gene on chromosome 6q16.1.     

Common genetic variants have associations with human cortical brain regions and risk of schizophrenia

Schizophrenia is a highly heritable mental disorder and is reported to be associated with measurements in cortical regions of the human brain. In this study, we considered genome-wide association studies to uncover genetic effects on cortical regions and prodromal symptoms of schizophrenia. Specifically, area, thickness, and volume of 66 cortical regions derived from magnetic resonance imaging scans of 1,445 children and adolescents from the Philadelphia Neurodevelopmental Cohort were studied. Two common variants were identified as being associated with two prefrontal cortical regions (one significant variant rs11601331 on chromosome 11p11 for right rostral middle frontal gyral area, p = 1.97 × 10 ⁻⁸; one suggestive variant rs2345981 on chromosome 6q11 for left frontal pole gyral volume, p = 2.07 × 10 ⁻⁷), where the significance of rs11601331 was independently replicated on the Pediatric Imaging, Neurocognition, and Genetics study of size 1,239 (p = 9.19 × 10 ⁻³). Moreover, genetic effects on schizophrenia were investigated based on a sample of 8,719 subjects. The two identified variants rs11601331 and rs2345981 showed significant association with the longest prodromal symptoms duration (p = 0.048 and p = 0.027, respectively).     

Admixture Mapping Identifies a Quantitative Trait Locus Associated with FEV1/FVC in the COPDGene Study

African Americans are admixed with genetic contributions from European and African ancestral populations. Admixture mapping leverages this information to map genes influencing differential disease risk across populations. We performed admixture and association mapping in 3,300 African American current or former smokers from the COPDGene Study. We analyzed estimated local ancestry and SNP genotype information to identify regions associated with FEV₁/FVC, the ratio of forced expiratory volume in one second to forced vital capacity, measured by spirometry performed after bronchodilator administration. Global African ancestry inversely associated with FEV₁/FVC (P = 0.035). Genome‐wide admixture analysis, controlling for age, gender, body mass index, current smoking status, pack‐years smoked, and four principal components summarizing the genetic background of African Americans in the COPDGene Study, identified a region on chromosome 12q14.1 associated with FEV₁/FVC (P = 2.1 × 10^?6) when regressed on local ancestry. Allelic association in this region of chromosome 12 identified an intronic variant in FAM19A2 (rs348644) as associated with FEV₁/FVC (P = 1.76 × 10^?6). By combining admixture and association mapping, a marker on chromosome 12q14.1 was identified as being associated with reduced FEV₁/FVC ratio among African Americans in the COPDGene Study.     

Informed Genome‐Wide Association Analysis With Family History As a Secondary Phenotype Identifies Novel Loci of Lung Cancer

Lung cancer is the leading cause of cancer death worldwide. Although several genetic variants associated with lung cancer have been identified in the past, stringent selection criteria of genome‐wide association studies (GWAS) can lead to missed variants. The objective of this study was to uncover missed variants by using the known association between lung cancer and first‐degree family history of lung cancer to enrich the variant prioritization for lung cancer susceptibility regions. In this two‐stage GWAS study, we first selected a list of variants associated with both lung cancer and family history of lung cancer in four GWAS (3,953 cases, 4,730 controls), then replicated our findings for 30 variants in a meta‐analysis of four additional studies (7,510 cases, 7,476 controls). The top ranked genetic variant rs12415204 in chr10q23.33 encoding FFAR4 in the Discovery set was validated in the Replication set with an overall OR of 1.09 (95% CI = 1.04, 1.14, P = 1.63 × 10^?4). When combining the two stages of the study, the strongest association was found in rs1158970 at Ch4p15.2 encoding KCNIP4 with an OR of 0.89 (95% CI = 0.85, 0.94, P = 9.64 × 10^?6). We performed a stratified analysis of rs12415204 and rs1158970 across all eight studies by age, gender, smoking status, and histology, and found consistent results across strata. Four of the 30 replicated variants act as expression quantitative trait loci (eQTL) sites in 1,111 nontumor lung tissues and meet the genome‐wide 10% FDR threshold.     

Genome‐Wide Family‐Based Linkage Analysis of Exome Chip Variants and Cardiometabolic Risk

Linkage analysis of complex traits has had limited success in identifying trait‐influencing loci. Recently, coding variants have been implicated as the basis for some biomedical associations. We tested whether coding variants are the basis for linkage peaks of complex traits in 42 African‐American (n = 596) and 90 Hispanic (n = 1,414) families in the Insulin Resistance Atherosclerosis Family Study (IRASFS) using Illumina HumanExome Beadchips. A total of 92,157 variants in African Americans (34%) and 81,559 (31%) in Hispanics were polymorphic and tested using two‐point linkage and association analyses with 37 cardiometabolic phenotypes. In African Americans 77 LOD scores greater than 3 were observed. The highest LOD score was 4.91 with the APOE SNP rs7412 (MAF = 0.13) with plasma apolipoprotein B (ApoB). This SNP was associated with ApoB (P‐value = 4 × 10^?19) and accounted for 16.2% of the variance in African Americans. In Hispanic families, 104 LOD scores were greater than 3. The strongest evidence of linkage (LOD = 4.29) was with rs5882 (MAF = 0.46) in CETP with HDL. CETP variants were strongly associated with HDL (0.00049 < P‐value <4.6 × 10^?12), accounting for up to 4.5% of the variance. These loci have previously been shown to have effects on the biomedical traits evaluated here. Thus, evidence of strong linkage in this genome wide survey of primarily coding variants was uncommon. Loci with strong evidence of linkage was characterized by large contributions to the variance, and, in these cases, are common variants. Less compelling evidence of linkage and association was observed with additional loci that may require larger family sets to confirm.     

Genome‐wide interaction with the insulin secretion locus MTNR1B reveals CMIP as a novel type 2 diabetes susceptibility gene in African Americans

Although type 2 diabetes (T2D) results from metabolic defects in insulin secretion and insulin sensitivity, most of the genetic risk loci identified to date relates to insulin secretion. We reported that T2D loci influencing insulin sensitivity may be identified through interactions with insulin secretion loci, thereby leading to T2D. Here, we hypothesize that joint testing of variant main effects and interaction effects with an insulin secretion locus increases power to identify genetic interactions leading to T2D. We tested this hypothesis with an intronic MTNR1B SNP, rs10830963, which is associated with acute insulin response to glucose, a dynamic measure of insulin secretion. rs10830963 was tested for interaction and joint (main + interaction) effects with genome‐wide data in African Americans (2,452 cases and 3,772 controls) from five cohorts. Genome‐wide genotype data (Affymetrix Human Genome 6.0 array) was imputed to a 1000 Genomes Project reference panel. T2D risk was modeled using logistic regression with rs10830963 dosage, age, sex, and principal component as predictors. Joint effects were captured using the Kraft two degrees of freedom test. Genome‐wide significant (P < 5 × 10^?8) interaction with MTNR1B and joint effects were detected for CMIP intronic SNP rs17197883 (P_interaction = 1.43 × 10^?8; P_joint = 4.70 × 10^?8). CMIP variants have been nominally associated with T2D, fasting glucose, and adiponectin in individuals of East Asian ancestry, with high‐density lipoprotein, and with waist‐to‐hip ratio adjusted for body mass index in Europeans. These data support the hypothesis that additional genetic factors contributing to T2D risk, including insulin sensitivity loci, can be identified through interactions with insulin secretion loci.     

中国人群WNT代谢通路与非综合征型唇腭裂的亲源效应分析

目的 非综合征型唇裂合并或不合并腭裂（NSCL/P）是一类常见的出生缺陷,遗传致病因素一直是其病因学研究的热点。本研究拟基于家系设计在WNT代谢通路基因中探索亲源效应对NSCL/P发病风险的影响。方法 本研究人群为“唇腭裂的基因组学国际合作组研究”项目在中国地区募集的806个NSCL/P核心家系。利用对数线性模型探索WNT基因及其单体型的亲源效应与疾病的关联,采用Wald检验探索亲源效应与环境因素的交互作用。经过Bonferroni多重检验校正后,统计学检验的显著性阈值设为P<3.47×10^-4。结果 质量控制后共纳入7个基因上144个单核苷酸多态性位点进行分析。结果显示,NSCL/P家系中有8个位点具有潜在的亲源效应（P<0.05）,但经Bonferroni多重检验校正后,均未达到统计学显著性水平（P>3.47×10^-4）。NSCL/P家系中位于WNT9A rs4074668-rs12725747单体型（T-A）具有亲源效应,且经Bonferroni校正后差异仍有统计学意义（P=2.74×10^-4）。但该单体型的亲源效应与环境因素（被动吸烟、复合维生素补充）的交互作用并未达到统计学显著水平。结论 WNT代谢通路基因可能通过亲源效应影响NSCL/P的发生风险。位于WNT9A基因rs4074668-rs12725747单体型（T-A）亲源效应与NSCL/P发病风险存在显著关联。未来仍需其他独立样本验证以进一步确认WNT代谢通路在NSCL/P发生中的作用。     

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.     

Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction

Polygenic prediction using genome‐wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10‐fold cross‐validation using the PRS approach, the R² for HC increased by 66% (0.0456–0.0755; P < 10⁻¹⁶), the R² for TA increased by 123% (0.0154 to 0.0344; P < 10⁻¹⁶), and the liability‐scale R² for BCC increased by 68% (0.0138–0.0232; P < 10⁻¹⁶) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.     

What SNP genotyping errors are most costly for genetic association studies?

Which genotype misclassification errors are most costly, in terms of increased sample size necessary (SSN) to maintain constant asymptotic power and significance level, when performing case/control studies of genetic association? We answer this question for single‐nucleotide polymorphisms (SNPs), using the 2×3 χ² test of independence. Our strategy is to expand the noncentrality parameter of the asymptotic distribution of the χ² test under a specified alternative hypothesis to approximate SSN, using a linear Taylor series in the error parameters. We consider two scenarios: the first assumes Hardy‐Weinberg equilibrium (HWE) for the true genotypes in both cases and controls, and the second assumes HWE only in controls. The Taylor series approximation has a relative error of less than 1% when each error rate is less than 2%. The most costly error is recording the more common homozygote as the less common homozygote, with indefinitely increasing cost coefficient as minor SNP allele frequencies approach 0 in both scenarios. The cost of misclassifying the more common homozygote to the heterozygote also becomes indefinitely large as the minor SNP allele frequency goes to 0 under both scenarios. For the violation of HWE modeled here, the cost of misclassifying a heterozygote to the less common homozygote becomes large, although bounded. Therefore, the use of SNPs with a small minor allele frequency requires careful attention to the frequency of genotyping errors to ensure that power specifications are met. Furthermore, the design of automated genotyping should minimize those errors whose cost coefficients can become indefinitely large. Genet Epidemiol 26:132–141, 2004. © 2004 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。     

Impact of repeated measures and sample selection on genome‐wide association studies of fasting glucose

Although genome‐wide association studies (GWAS) have been performed in longitudinal studies, most used only a single trait measure. GWAS of fasting glucose have generally included only normoglycemic individuals. We examined the impact of both repeated measures and sample selection on GWAS in Atherosclerosis Risk In Communities (ARIC), a study which obtained four longitudinal measures of fasting glucose and included both individuals with and without prevalent diabetes. The sample included Caucasians and the Affymetrix 6.0 chip was used for genotyping. Sample sizes for GWAS analyses ranged from 8,372 (first study visit) to 5,782 (average fasting glucose). Candidate SNP analyses with SNPs identified through fasting glucose or diabetes GWAS were conducted in 9,133 individuals, including 761 with prevalent diabetes. For a constant sample size, smaller P‐values were obtained for the average measure of fasting glucose compared to values at any single visit, and two additional significant GWAS signals were detected. For four candidate SNPs (rs780094, rs10830963, rs7903146, and rs4607517), the strength of association between genotype and glucose was significantly (P‐interaction<0.05) different in those with and without prevalent diabetes, and for all five fasting glucose candidate SNPs (rs780094, rs10830963, rs560887, rs4607517, and rs13266634) the association with measured fasting glucose was more significant in the smaller sample without prevalent diabetes than in the larger combined sample of those with and without diabetes. This analysis demonstrates the potential utility of averaging trait values in GWAS studies and explores the advantage of using only individuals without prevalent diabetes in GWAS of fasting glucose. Genet. Epidemiol. 34: 665‐673, 2010. © 2010 Wiley‐Liss, Inc.     

Gene–environment interactions related to blood pressure traits in two community-based Korean cohorts

Hypertension is a complex disorder caused by genetic and environmental risk factors. Recently, genome-wide association studies (GWASs) identified more than 100 genetic variants for blood pressure traits and hypertension. However, the interactions between these genetic variants and environmental factors have not been systematically investigated. Therefore, we examined the interaction between genetic and environmental risk factors in blood pressure traits using the genetic risk score (GRS). Two Korean community-based cohorts, Cohort I (KARE; N = 8,840) and Cohort II (CAVAS; N = 9,599), were used for this study, and GRSs were calculated from 42 GWAS single-nucleotide polymorphisms (SNPs) that were validated for their association in these cohorts. We calculated GRSs in both ways by considering the effect sizes of each SNP (weighted GRS) and not considering the effect sizes (unweighted GRS). The unweighted GRS was strongly associated with systolic blood pressure, diastolic blood pressure, and hypertension (p = 9.03 × 10 ^–47, p = 9.41 × 10 ^–48, and p = 3.22 × 10 ^–55 by meta-analysis, respectively) and the weighted GRS showed the similar results. The environmental factors of body mass index, waist circumference, and drinking status were significantly associated with blood pressure traits, and the interaction between these factors and GRSs were examined. However, no interactions were found with either the GRS or the individual SNPs considered for the GRS. Our findings show that it is challenging to find GRS–environment interactions regarding blood pressure traits.     

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.