Impact of Preadjusting a Quantitative Phenotype Prior to Sib‐Pair Linkage Analysis when Gene×Environment Interaction Exists

The investigation of potential gene×environment (G×E) interactions is an important facet in the study of complex diseases. When G×E interaction exists, linkage analyses of the interacting gene must treat the environmental factor appropriately. Specifically, the common approach of regressing out an environmental factor prior to linkage analysis may be inappropriate if that factor has an interaction with the gene. This is explored here in the Genetic Analysis Workshop 12 simulated data set using the G×E interaction between major gene four (MG4) and environmental factor two (E2). The analysis shows that preadjusting the quantitative trait three (Q3) phenotype for the main effects of several environmental variables, including one (E2) that interacts with MG4, affects the results of a Haseman‐Elston linkage analysis. In particular, the agreement in detecting linkage between preadjusting versus not preadjusting was only 78% and 66% using alpha levels of 0.05 and 0.10, respectively. For both approaches, incorporating an interaction term in the regression models enabled linkage to be detected where the evidence was either minimal or not present in an identical‐by‐descent main effects‐only model. Furthermore, preadjustment for E2 did not appear to account for the major discrepancies between the approaches. © 2001 Wiley‐Liss, Inc.     

Linkage Analysis of Quantitative Traits in Randomly Ascertained Pedigrees: Comparison of Penetrance‐Based and Variance Component Analysis

Penetrance‐based linkage analysis and variance component linkage analysis are two methods that are widely used to localize genes influencing quantitative traits. Using computer programs PAP and SOLAR as representative software implementations, we have conducted an empirical comparison of both methods' power to map quantitative trait loci in extended, randomly ascertained pedigrees, using simulated data. Two‐point linkage analyses were conducted on several quantitative traits of different genetic and environmental etiology using both programs, and the lod scores were compared. The two methods appear to have similar power when the underlying quantitative trait locus is diallelic, with one or the other method being slightly more powerful depending on the characteristics of the quantitative trait and the quantitative trait locus. In the case of a multiallelic quantitative trait locus, however, the variance component approach has much greater power. These findings suggest that one should give careful thought to the likely allelic architecture of the quantitative trait to be analyzed when choosing between these two analytical approaches. It may be the case in general that linkage methods which explicitly or implicitly rely on the assumption of a diallelic trait locus fare poorly when this assumption is incorrect. © 2001 Wiley‐Liss, Inc.     

Description of Three Data Sets: Collaborative Study on the Genetics of Asthma (CSGA), the German Affected‐Sib‐Pair Study,and the Hutterites of South Dakota

Three different data sets with clinical data and markers from genome‐wide screens were submitted for analysis at Genetic Analysis Workshop 12. In each study, participants were carefully characterized for asthma and related phenotypes. Testing for bronchial hyper‐responsiveness using methacholine and standardized protocols was performed. Total serum IgE levels were measured using standardized techniques. In addition, similar questionnaire data on symptoms and relevant environmental exposures were obtained. Relevant clinical data and genotypes for the polymorphic markers used for each genome‐wide screen were submitted. The data set from the United States Collaborative Study on the Genetics of Asthma represents a heterogeneous population consisting of both Caucasian and African American families ascertained through two siblings with clinical asthma from multiple centers. Likewise, the families from the German Asthma Genetics Group were also ascertained through two siblings with asthma at multiple centers. In a contrast to these data sets, Dr. Carole Ober and her collaborators submitted data from the inbred Hutterite population in South Dakota. © 2001 Wiley‐Liss, Inc.     

Exposure Enriched Case‐Control (EECC) Design for the Assessment of Gene–Environment Interaction

Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case‐control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case‐control study, the exposure enriched case‐control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, depending on the skewness of the exposure distribution. Of course, a traditional logistic regression model is no longer valid and results in biased parameter estimation. We show that addition of a simple covariate to the regression model removes this bias and yields reliable estimates of main and interaction effects of interest. We also discuss optimal design, showing that judicious oversampling of high/low exposed individuals can boost study power considerably. We illustrate our results using data from a study involving arsenic exposure and detoxification genes in Bangladesh.     

Meta‐Analysis of Genome‐Wide Association Studies with Correlated Individuals: Application to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Investigators often meta‐analyze multiple genome‐wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta‐analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta‐analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community. We propose a novel mixed‐effect model for meta‐analysis, “MetaCor,” which accounts for correlation between stratum‐specific effect estimates. Simulations show that MetaCor controls inflation better than alternatives such as ignoring the correlation between the strata or analyzing all strata together in a “pooled” GWAS, especially with different minor allele frequencies (MAFs) between strata. We illustrate the benefits of MetaCor on two GWASs in the HCHS/SOL. Analysis of dental caries (tooth decay) stratified by ancestry group detected a genome‐wide significant SNP (rs7791001, P‐value = , compared to in pooled), with different MAFs between strata. Stratified analysis of body mass index (BMI) by ancestry group and sex reduced overall inflation from (pooled) to (MetaCor). Furthermore, even after removing close relatives to obtain nearly uncorrelated strata, a naïve stratified analysis resulted in compared to for MetaCor.     

Indication of linkage of serum IgE levels to the interleukin‐4 gene and exclusion of the contribution of the (–590 C to T) interleukin‐4 promoter polymorphism to IgE variation

Previous segregation analysis of a sample of 234 randomly selected Australian families showed evidence for a recessive major gene controlling serum immunoglobulin E (IgE) levels independently of the specific response to allergens (SRA). Since linkage has been recently reported between serum IgE levels and the 5q candidate region spanning the interleukin‐4 (IL‐4) gene, we investigated whether the recessive major gene detected by segregation analysis was linked to the IL‐4 region and whether polymorphisms within the IL‐4 gene were associated with IgE levels. Both sib‐pair method and combined segregation and linkage analysis using the regressive models were applied to our data. Whereas there was no evidence of linkage of total IgE levels to the IL‐4 region, an indication of linkage (P values ranging between 0.01 and 0.03) was found between IgE levels adjusted for SRA and two IL‐4 polymorphisms: one dinucleotide repeat in intron 2 of the IL‐4 gene and a single nucleotide (–590 C to T) polymorphism in the IL‐4 promoter. However, the putative IL‐4 linked gene did not appear to be in linkage disequilibrium with either of these two polymorphisms. A contribution of the IL‐4 promoter polymorphism, presumed to be a potential functional variant influencing IgE variation, was also excluded. Genet. Epidemiol. 16:84–94, 1999. © 1999 Wiley‐Liss, Inc.     

浙江省首例人感染H7N9禽流感病例的实验室检测与分析

目的 通过对杭州市萧山区1例疑似人感染禽流感病人咽拭子和鼻腔冲洗液样本进行检测和分析,加深对A型流感病毒抗原变异特性的认识,进一步提高实验室相应的检测能力.方法 以实时荧光定量PCR(real-time PCR)检测流感病毒核酸,对未能分型的HA、NA和M基因通过逆转录PCR(RT-PCR)扩增而后克隆测定;同时接种狗肾细胞(MDCK)进行病毒培养.结果 样本A型流感病毒核酸检测为阳性,但未能分型;HA、NA、M基因测序结果表明与禽源H7、N9、M基因相似性最高;同时A型流感病毒培养也呈阳性.结论 经过浙江省CDC与中国CDC证实,该患者感染H7N9禽流感病毒,为浙江省首个病例.