基于常见遗传变异和传统风险因素的中国南方汉族人群结直肠癌风险预测模型研究

目的 基于结直肠癌全基因组关联研究(GWAS)发现的易感位点,联合传统风险因素建立中国南方汉族人群结直肠癌风险预测模型。方法 对1 066例结直肠癌患者和3 880例健康对照的21个GWAS候选位点进行基因分型,分析其与结直肠癌易感性之间的关联。通过遗传风险评分(GRS)和加权遗传风险评分(wGRS)计算显著候选位点的联合效应。以不同方式组合遗传风险评分和传统风险因素,构建结直肠癌风险预测模型,并绘制受试者工作特征曲线评价模型优劣性。结果 7个候选位点与结直肠癌易感性显著相关。随着风险评分的升高,人群患结直肠癌的风险也随之升高(GRS:P=0.002 6,wGRS:P<0.000 1),相比于四分位分组中最低一组,GRS和wGRS最高的一组OR值分别为1.33(95%CI:1.12~1.58,P=0.001 0)和1.76(95%CI:1.45~2.14,P<0.000 1)。联合传统风险因素和wGRS的模型为最优模型,其曲线下面积为0.593(95%CI:0.573~0.613)。结论 结直肠癌易感位点间存在显著的联合作用。相比于传统风险因素模型,传统风险因素结合加权遗传风险评分模型能更好预测结直肠癌的患病风险。     

基于全基因组关联研究的中国人群肺癌风险预测模型

目的 联合使用遗传因素和吸烟信息构建中国汉族人群的肺癌风险预测模型。方法 基于中国汉族人群全基因组关联研究(GWAS)数据,根据样本地区来源将样本分为训练集(南京与上海:1 473 名病例vs. 1 962 名对照)和测试集(北京与武汉:858 名病例vs. 1 115 名对照)。系统整理已报道肺癌易感位点,在训练集中用逐步后退法筛选具有独立效应的位点,并通过加权法估算个体遗传得分用于建模。在训练集中分别构建基于吸烟信息、遗传得分和联合使用吸烟与遗传信息的3 种风险预测模型(吸烟模型、遗传效应模型和联合模型),并根据受试者工作特征(ROC)曲线、曲线下面积(AUC)、净分类指数(NRI)和整体鉴别指数(IDI)评价模型对肺癌风险预测的效能。对于构建的模型,进一步在测试集中进行验证。结果 在训练集中,联合模型、吸烟模型和遗传效应模型AUC分别为0.69(0.67~0.71)、0.65(0.63~0.66)和0.60(0.59~0.62)。在训练集和测试集中联合模型的风险预测效能高于吸烟模型或遗传模型,差异有统计学意义(P<0.001)。重分类结果显示,联合模型与吸烟模型相比,在训练集中NRI 增加4.57%(2.23%~6.91%),IDI 增加3.11%(2.52%~3.69%)。在测试集中,NRI和IDI 分别增加2.77%和3.16%。结论 遗传得分可以显著提高肺癌传统风险模型的预测效能。联合使用遗传因素和吸烟信息构建的中国汉族人群肺癌风险预测模型可用于筛选中国汉族人群中肺癌发病的高危人群。     

多基因遗传风险评分用于精准预防的研究进展

近年来，全基因组关联研究鉴定了大量复杂性疾病的遗传易感位点。多基因遗传风险评分通过整合多个易感位点的效应，已被证明可用于量化多种复杂性疾病的遗传风险，对于人群风险分层以及进一步实现精准医学目标存在潜在应用价值。本文介绍了多基因遗传风险评分构建及其评价方法，并就其在精准预防应用中的最新研究进展做概述。     

基于环境与遗传风险的2型糖尿病发病风险预测模型的比较

目的尝试采用logistic回归、神经网络、支持向量机3种方法,构建适合中国人群特点的2型糖尿病(T2DM)发病风险评估模型,为T2DM高危人群筛查、健康管理和个性化预防提供科学依据。方法在现况调查资料的基础上,在北京市房山区选取745例T2DM患者及1 964例对照。采用统一的调查问卷收集全部研究对象的一般人口学资料、行为生活方式、既往病史等信息;采用标准化的方法对研究对象进行统一体检及生化检查;实验室采用飞行时间质谱方法对既往全基因组关联研究(GWAS)报道的15个T2DM易感基因的27个SNP位点进行基因型检测,并采用非加权遗传风险评分法综合评估研究对象的遗传风险。分别采用logistic回归模型、神经网络及支持向量机方法构建包含基因和环境危险因素的T2DM发病风险评估模型。结果 (1)logistic回归模型:单独纳入传统危险因素或遗传风险评分时,模型的曲线下面积(AUC)分别为0.647(95%CI:0.623~0.670)和0.579(95%CI:0.554~0.604);同时纳入传统危险因素和遗传风险评分时,模型的AUC为0.670(95%CI:0.647~0.693)。(2)神经网络模型:单独纳入传统危险因素或遗传风险评分时,模型的AUC分别为0.690(95%CI:0.668~0.713)和0.579(95%CI:0.554~0.604);同时纳入传统危险因素和遗传风险评分时,模型的AUC为0.728(95%CI:0.706~0.749)。(3)支持向量机模型:单独纳入传统危险因素或遗传风险评分时,模型的AUC分别为0.733(95%CI:0.663~0.803)和0.562(95%CI:0.484~0.640);同时纳入传统危险因素和遗传风险评分时,模型的AUC为0.765(95%CI:0.700~0.831)。结论与只纳入传统危险因素相比,同时纳入传统危险因素和遗传风险评分时,T2DM发病风险评估模型的预测效果更好。与logistic回归模型相比,采用神经网络或支持向量机构建的T2DM发病风险评估模型的预测效果更好。     

多基因遗传风险评分与胃癌发病年龄的关联分析

目的 探讨多基因遗传风险评分（PRS）与胃癌发病年龄及早发风险之间的关系。方法 基于胃癌全基因组关联研究,以胃癌病例为研究对象,利用112个与胃癌发生风险有关的单核苷酸多态性位点构建PRS,采用方差分析和Pearson相关性检验分析PRS水平与胃癌发病年龄的关系。将发病年龄<50岁的病例定义为早发胃癌病例,以低遗传风险（PRS≤20%）为参照组,采用Cox比例风险模型以早发诊断年龄为时间变量分析中遗传风险（PRS：20%~80%）和高遗传风险（PRS>80%）与胃癌早发风险的关联。结果 共纳入8 629例胃癌病例,其中,男性6 284例（72.82%）,女性2 345例（27.18%）,发病年龄为（60.61±10.80）岁。PRS水平与胃癌发病年龄呈显著负相关（r=-0.05,P<0.001）,PRS越高胃癌发病年龄越小,低、中、高遗传风险组胃癌发病年龄分别为（61.68±10.33）岁、（60.53±10.79）岁、（59.80±11.20）岁。PRS与胃癌早发风险呈剂量反应关系（中遗传风险：HR=1.19,95%CI：1.03~1.39,P=0.022;高遗传风险：HR=1.44,95%CI：1.20~1.71,P<0.001）。结论 高PRS不仅增加胃癌发病风险,同时也是胃癌早发的危险因素,PRS可作为遗传检测指标评估胃癌发病和早发风险。     

基于遗传和环境危险因素构建的风险评分在结直肠肿瘤分级筛查中的应用评价

目的评价利用遗传与环境危险因素构建的结直肠肿瘤风险评分的人群高危分层效果及其在分级筛查中的应用效果。方法基于一项全国多中心结直肠癌筛查随机对照研究, 纳入2 160例接受结肠镜检查的研究对象。采用MassARRAY技术检测20个结直肠癌相关的遗传易感位点并构建遗传风险评分。个体环境风险评分采用亚太结直肠筛查评分系统计算。使用logistic回归分别评估遗传风险单独和遗传联合环境风险与结直肠肿瘤之间的关联。构建基于遗传和/或环境风险评分的分级筛查方案(即高风险者接受单次结肠镜筛查, 低风险者接受每年一次的定量粪便免疫化学检测, 其结果阳性者接受结肠镜检查), 并与全部接受结肠镜方案进行效果比较。结果与低遗传风险评分组相比, 高遗传风险评分组发生结直肠肿瘤的风险增加了26%(OR=1.26, 95%CI:1.03～1.54, P=0.026)。遗传和环境风险评分均最高的研究对象患进展期肿瘤(包括结直肠癌、进展期腺瘤和进展期锯齿状息肉)的风险是两评分均最低研究对象的3.03倍(95%CI:1.87～4.90, P<0.001)。多轮筛查效果评价方面, 当进行到第3轮筛查时, 基于遗传联...     

p53靶基因结合区遗传变异与中国人群乳腺癌遗传易感性的关联研究

目的 探讨p53靶基因结合区遗传变异与中国人群乳腺癌遗传易感性的关系。方法 通过公共数据库下载乳腺癌细胞系ChIP-seq数据和各种生物信息学方法,筛选可能影响p53靶基因结合区的遗传变异。采用大样本病例-对照研究揭示p53靶基因结合区遗传变异与乳腺癌遗传易感性的关系。结果 利用生物信息学分析方法共筛选出p53靶基因结合区的3个多态位点,并在1 274例乳腺癌病例和1 255名健康女性对照进行关联研究。病例组和对照组年龄差异无统计学意义（P=0.318）,绝经状况、吸烟和饮酒在病例组和对照组的分布差异无统计学意义,其对应的P值分别为0.539、0.258和0.131。VMP1-rs1295925各基因型在病例组和对照组的分布差异有统计学意义。校正年龄、绝经状况以及吸烟饮酒等后,携带rs1295925-CT和TT基因型的个体与携带rs1295925-CC基因型的个体相比,其患乳腺癌的风险分别增加了32%（OR=1.32,95% CI：1.07～1.62）和41%（OR=1.41,95% CI：1.13～1.78）。在等位基因模型、显性模型以及相加模型中也均发现了该遗传变异显著增加乳腺癌发病风险。结论 位于VMP1基因上的rs1295925可能是乳腺癌的遗传易感位点,后续需要生物学功能实验对结果进行验证。     

一种新的调控型遗传变异与中国人群肺癌发病风险的关系：两阶段病例-对照研究

目的 调控型数量性状位点（regQTL）理论可以帮助研究者从三维角度评估单核苷酸多态性（SNPs）对重要生物信号的调控作用。本研究拟探讨regQTL-SNPs对肺癌易感性的影响。方法 基于regQTL理论，利用已知的肺癌regQTL-SNPs数据库，筛选出全基因组关联研究（GWAS）报道的肺癌易感区域中发挥regQTL功能的SNPs。并通过两阶段病例-对照研究（初筛阶段：2 331例肺癌病例和3 077例健康对照；验证阶段：626例肺癌病例和667例健康对照），进一步明确上述regQTL-SNPs与肺癌易感性的关联。结果 在肺癌GWAS已报道的易感区域中，共筛选出8个regQTL-SNPs。人群易感性分析的初筛阶段，研究结果显示3个regQTL-SNPs与肺癌的发病风险存在统计学关联（P<0.05），验证阶段结果显示，位于ADRA1A基因上的rs6998591突变等位基因T可以显著增加肺癌的发病风险（相加模型：OR=1.33，95%CI：1.01~1.74，P=0.040），而位于ACTA2基因上的rs11202916突变等位基因G可以明显降低肺癌的发病风险（隐性模型：OR=0.71，95%CI：0.52~0.96，P=0.026）。分层分析结果显示，rs6998591的突变等位基因T显著增加肺鳞癌的发病风险（相加模型：OR=1.53，95%CI：1.01~2.32，P=0.043），而rs11202916的突变等位基因G显著降低肺腺癌的发病风险（相加模型：OR=0.83，95%CI：0.69~0.98，P=0.031）。基因环境交互作用分析显示携带rs6998591突变等位基因T且吸烟的个体与不携带rs6998591突变等位基因T且不吸烟的个体相比，肺癌的发病风险增加235%（OR=3.35，95%CI：2.10~5.34，P<0.001）。结论 肺癌GWAS已报道的易感区域中存在2个发挥regQTL功能的SNPs，并且可以显著影响肺癌的易感性。     

母亲、子代血管紧张素原基因单核苷酸多态性与先兆子痫/子痫的关联研究

目的 探讨母亲、子代血管紧张素原（AGT）基因单核苷酸多态性（SNP）与先兆子痫/子痫的关联。方法 2008年1月至2015年10月，采用病例-父母/对照-母亲混合研究设计，调查347组病例和700组对照的基本人口学特征，并检测AGT相关SNP的基因型，运用对数线性模型及非条件logistic回归的方法分析母亲、子代AGT各SNP与先兆子痫/子痫的关联。结果 当子代rs3789679基因型为GA和AA时，其母亲发生先兆子痫/子痫的风险降低（OR=0.73，95% CI：0.55~0.96；OR=0.62，95% CI：0.39~0.98）；当子代rs2493132为TT基因型时，其母亲发生先兆子痫/子痫的风险增加（OR=1.60，95% CI：1.08~2.37），此结果经多重检验校正后无统计学意义。遗传模型分析显示，子代rs3789679在显性模型下（GA+AA/GG）降低了其母亲发生先兆子痫/子痫的风险（OR=0.73，95% CI：0.55~0.96）；子代rs2493132在隐性模型（TT/CT+CC）下增加了其母亲发生先兆子痫/子痫的风险（OR=1.66，95% CI：1.13~2.44）；母亲rs5051在显性模型下（TC+CC/TT）增加了自身发生先兆子痫/子痫的风险（OR=1.33，95% CI：1.01~1.76）。结论 在显性遗传模型下，子代rs3789679 GA和AA基因型降低了其母亲发生先兆子痫/子痫的风险，母亲rs5051 TC和CC基因型增加了自身发生先兆子痫/子痫的风险。在隐性遗传模型下，子代rs2493132 TT基因型增加了其母亲发生疾病的风险。     

吸烟和健康饮食评分在文化程度与肺癌发病风险关联中的中介效应研究

目的 探讨吸烟和健康饮食评分在文化程度与肺癌发病风险关联中的中介效应。方法 基于英国生物银行（UKB）前瞻性队列研究数据,剔除文化程度信息缺失及基线患有癌症的个体后,最终纳入446 772名研究对象。采用Cox比例风险回归模型估计文化程度与肺癌发病风险的关联以及吸烟和健康饮食评分与肺癌发病的关联,采用中介效应模型分析吸烟和健康饮食评分在文化程度和肺癌风险关联中的中介效应。结果 在7.13年的中位随访时间内,新发肺癌1 994例。受教育年数每增加一个标准差（5年）,肺癌风险降低12%（HR=0.88,95%CI：0.84~0.92）。根据UKB自我报告的最高学历转换为国际教育代码分类标准（ISCED）中对应的1~5级,级数越大代表文化程度越高。与ISCED-1者相比,ISCED-2、ISCED-3、ISCED-4和ISCED-5人群的肺癌发病HR值（95%CI）依次为0.83（0.72~0.94）、0.67（0.53~0.85）、0.76（0.65~0.89）和0.72（0.64~0.80）。文化程度与吸烟呈负相关（β=-0.079,95%CI：-0.081~-0.077）,与健康饮食评分呈正相关（β=0.042,95%CI：0.039~0.045）。中介效应分析显示,吸烟与健康饮食评分在文化程度与肺癌的关联中存在中介效应,中介比例分别为38.952%（95%CI：31.802%~51.659%）和1.784%（95%CI：0.405%~3.713%）。结论 吸烟和健康饮食评分可能介导文化程度对肺癌发病的影响,这表明提高文化程度可能通过改变吸烟和饮食等生活方式降低肺癌发病风险。     

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.     

Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study

Recent studies have evaluated whether incorporating nontraditional risk factors improves coronary heart disease (CHD) prediction models. This 1986-2001 US study aggregated the contribution of multiple single nucleotide polymorphisms into a genetic risk score (GRS) and assessed whether the GRS plus traditional risk factors predict CHD better than traditional risk factors alone. The Atherosclerosis Risk in Communities (ARIC) cohort was followed for a median of 13 years for CHD events (n = 1,452). Individuals were genotyped for 116 single nucleotide polymorphisms associated with CHD in multiple case-control studies. Single nucleotide polymorphisms nominally predicting incident CHD in the ARIC study were included in the GRS. The GRS was significantly associated with incident CHD in Blacks (hazard rate ratio = 1.20, 95% confidence interval: 1.11, 1.29) and Whites (hazard rate ratio = 1.10, 95% confidence interval: 1.06, 1.14) as well as in each tertile defined by the traditional cardiovascular risk score (p < or = 0.02). When receiver operating characteristic curves based on traditional risk factors were recalculated after the GRS was added, the increase in the area under the receiver operating characteristic curve was statistically significant for Blacks and suggestive of improved CHD prediction for Whites. This study demonstrates the concept of aggregating information from multiple single nucleotide polymorphisms into a risk score and indicates that it can improve prediction of incident CHD in the ARIC study.     

Saturated Fat Intake Modulates the Association between an Obesity Genetic Risk Score and Body Mass Index in Two US Populations

Combining multiple genetic variants related to obesity into a genetic risk score (GRS) might improve identification of individuals at risk of developing obesity. Moreover, characterizing gene–diet interactions is a research challenge to establish dietary recommendations to individuals with higher predisposition to obesity. Our objective was to analyze the association between an obesity GRS and body mass index (BMI) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) population, focusing on gene–diet interactions with total fat and saturated fatty acid (SFA) intake, and to replicate findings in the Multi-Ethnic Study of Atherosclerosis (MESA) population. Cross-sectional analyses included 783 white US participants from GOLDN and 2,035 from MESA. Dietary intakes were estimated with validated food frequency questionnaires. Height and weight were measured. A weighted GRS was calculated on the basis of 63 obesity-associated variants. Multiple linear regression models adjusted by potential confounders were used to examine gene–diet interactions between dietary intake (total fat and SFA) and the obesity GRS in determining BMI. Significant interactions were found between total fat intake and the obesity GRS using these variables as continuous for BMI (P for interaction=0.010, 0.046, and 0.002 in GOLDN, MESA, and meta-analysis, respectively). These association terms were stronger when assessing interactions between SFA intake and GRS for BMI (P for interaction=0.005, 0.018, and <0.001 in GOLDN, MESA, and meta-analysis, respectively). SFA intake interacts with an obesity GRS in modulating BMI in two US populations. Although determining the causal direction requires further investigation, these findings suggest that potential dietary recommendations to reduce BMI effectively in populations with high obesity GRS would be to reduce total fat intake mainly by limiting SFAs.     

Contemporary Considerations for Constructing a Genetic Risk Score: An Empirical Approach

Genetic risk scores are an increasingly popular tool for summarizing the cumulative risk of a set of Single Nucleotide Polymorphisms (SNPs) with disease. Typically only the set of the SNPs that have reached genome‐wide significance compose these scores. However recent work suggests that including additional SNPs may aid risk assessment. In this paper, we used the Atherosclerosis Risk in Communities (ARIC) Study cohort to illustrate how one can choose the optimal set of SNPs for a genetic risk score (GRS). In addition to P‐value threshold, we also examined linkage disequilibrium, imputation quality, and imputation type. We provide a variety of evaluation metrics. Results suggest that P‐value threshold had the greatest impact on GRS quality for the outcome of coronary heart disease, with an optimal threshold around 0.001. However, GRSs are relatively robust to both linkage disequilibrium and imputation quality. We also show that the optimal GRS partially depends on the evaluation metric and consequently the way one intends to use the GRS. Overall the implications highlight both the robustness of GRS and a means to empirically choose the best set of GRSs.     

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.     

Validating the doubly weighted genetic risk score for the prediction of type 2 diabetes in the Lifelines and Estonian Biobank cohorts

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high-risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for “Winner's curse” bias in the effect size estimates. The traditional (single-weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines (n = 12,018) and EstBB (n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10–1.43] and HR = 1.35 [1.28–1.42]) compared to the swGRS (HR = 1.21 [1.07–1.38] and HR = 1.25 [1.19–1.32]) in Lifelines and EstBB, respectively. Comparing the 5-year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034–0.243; p = .009 Lifelines), and 0.257 (0.194–0.319; p < 2 × 10⁻¹⁶ EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance.     

Multifactorial disease risk calculator: Risk prediction for multifactorial disease pedigrees

Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user‐friendly interface, extending a reported methodology based on a liability‐threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects. It allows the application of such models to disease pedigrees. Pedigree‐related outputs are (i) individual disease risk for pedigree members, (ii) n year risk for unaffected pedigree members, and (iii) the disease pedigree's joint liability distribution. Risk prediction for each pedigree member is based on using the constructed disease model to appropriately weigh evidence on disease risk available from personal attributes and family history. Evidence is used to construct the disease pedigree's joint liability distribution. From this, lifetime and n year risk can be predicted. Example disease models and pedigrees are provided at the website and are used in accompanying tutorials to illustrate the features available. The website is built on an R package which provides the functionality for pedigree validation, disease model construction, and risk prediction. Website: http://grass.cgs.hku.hk:3838/mdrc/current     

Thyroid function,sex hormones and sexual function: a Mendelian randomization study

Hypothyroidism and hyperthyroidism are observationally associated with sex hormone concentrations and sexual dysfunction, but causality is unclear. We investigated whether TSH, fT4, hypo- and hyperthyroidism are causally associated with sex hormones and sexual function. We used publicly available summary statistics from genome-wide association studies on TSH and fT4 and hypo- and hyperthyroidism from the ThyroidOmics Consortium (N?≤?54,288). Outcomes from UK Biobank (women?≤?194,174/men?≤?167,020) and ReproGen (women?≤?252,514) were sex hormones (sex hormone binding globulin [SHBG], testosterone, estradiol, free androgen index [FAI]) and sexual function (ovulatory function in women: duration of menstrual period, age at menarche and menopause, reproductive lifespan, and erectile dysfunction in men). We performed two-sample Mendelian randomization (MR) analyses on summary level, and unweighted genetic risk score (GRS) analysis on individual level data. One SD increase in TSH was associated with a 1.332 nmol/L lower (95% CI: ??0.717,??1.946; p?=?2?×?10^–5) SHBG and a 0.103 nmol/l lower (??0.051,V0.154; p?=?9?×?10^–5) testosterone in two-sample MR, supported by the GRS approach. Genetic predisposition to hypothyroidism was associated with decreased and genetic predisposition to hyperthyroidism with increased SHBG and testosterone in both approaches. The GRS for fT4 was associated with increased testosterone and estradiol in women only. The GRS for TSH and hypothyroidism were associated with increased and the GRS for hyperthyroidism with decreased FAI in men only. While genetically predicted thyroid function was associated with sex hormones, we found no association with sexual function.