32例三阴性乳腺癌患者易感基因胚系突变分析

目的:应用全外显子测序技术初步探讨三阴性乳腺癌(TNBC)患者易感基因突变情况。方法:收集本院就诊的32例TNBC患者,均经临床手术病理确诊。采集患者外周血提取基因组DNA进行全外显子组测序,通过生物信息学分析筛选与乳腺肿瘤相关的易感基因变异。结果:32例TNBC患者中14例检测到BRCA1/2罕见变异,明确致病性或可疑致病变异6例,突变携带频率为18.8%。其中BRCA1:c.5468-1_5474del和c.4749_4750del是较常见的突变;BRCA2:c.6027A>C为新的变异;BRCA2:c.3794G>T、c.7901T>A,BRCA1:c.4616T>C首次在中国人群中发现。除了BRCA1/2变异外,还检测到83个乳腺肿瘤易感基因变异,每个患者携带2.6个变异。2个以上患者携带的乳腺癌易感基因包括ALK、APC、CDH1、PTCH2、RB1CC1、RAD51D、RAD54L、TSC1等。结论:BRCA1/2是TNBC患者最重要的易感基因,其他与DNA损伤修复相关的基因突变可能与TNBC患者的表型有一定的相关性。     

Detection of BRCA1 and BRCA2 Mutations in a Selected Hawai‘i Population

Objective

To examine BRCA1 and BRCA2 gene sequence testing results, specifically variants of uncertain clinical significance in the BRCA1 and/or BRCA2 sequences of an ethnically diverse population within a particular time constraint.

Methods

A retrospective chart analysis of BRCA1 and BRCA2 gene sequence testing cases was reviewed at Kapi‘olani Medical Center for Women and Children from October 1996 to November 2007. Information was extracted and categorized regarding each patient''s age, age of cancer onset, types of cancer in family history, ethnicity/ancestry, type of test used for analysis, and specific characteristics of each variant.

Results

Of the 273 patients who received BRCA1/BRCA2 gene sequence testing, 45 patients demonstrated variants of uncertain clinical significance. A total of 48 variants of uncertain clinical significance were reported and 9 of the variants had previously never been observed before. Of the 45 patients, 33.3% were Caucasian, 40% were Asian, and 26.67% were of mixed ethnicity.

Conclusions

Within the local population at Kapi‘olani Medical Center for Women and Children, a significantly higher proportion of patients exhibited variants compared to the national average. A high percentage of variants existed among the ethnically diverse as well as the Caucasian population. Gene sequence testing is a valuable asset for physicians treating patients who are at risk for inherited cancer: however, the direction of treatment remains clinically questionable for patients with variants of unknown significance.     

Making Medical Decisions in Dependence of Genetic Background: Estimation of the Utility of DNA Testing in Clinical,Pharmaco‐Epidemiological or Genetic Studies

An index measuring the utility of testing a DNA marker before deciding between two alternative treatments is proposed which can be estimated from pharmaco‐epidemiological case‐control or cohort studies. In the case‐control design, external estimates of the prevalence of the disease and of the frequency of the genetic risk variant are required for estimating the utility index. Formulas for point and interval estimates are derived. Empirical coverage probabilities of the confidence intervals were estimated under different scenarios of disease prevalence, prevalence of drug use, and population frequency of the genetic variant. To illustrate our method, we re‐analyse pharmaco‐epidemiological case‐control data on oral contraceptive intake and venous thrombosis in carriers and non‐carriers of the factor V Leiden mutation. We also re‐analyse cross‐sectional data from the Framingham study on a gene‐diet interaction between an APOA2 polymorphism and high saturated fat intake on obesity. We conclude that the utility index may be helpful to evaluate and appraise the potential clinical and public health relevance of gene‐environment interaction effects detected in genomic and candidate gene association studies and may be a valuable decision support for designing prospective studies on the clinical utility.     

A Multigene Test Could Cost-Effectively Help Extend Life Expectancy for Women at Risk of Hereditary Breast Cancer

Background

The National Comprehensive Cancer Network recommends that women who carry gene variants that confer substantial risk for breast cancer consider risk-reduction strategies, that is, enhanced surveillance (breast magnetic resonance imaging and mammography) or prophylactic surgery. Pathogenic variants can be detected in women with a family history of breast or ovarian cancer syndromes by multigene panel testing.

Evidence for multiple genes determining sodium transport

Sodium transport comprises a set of interacting systems. Consequently, a defective sodium transport gene affects multiple sodium transport systems, and a sodium transport variable measured on a sample of individuals reflects genetic variation from a number of different genes, complicating the task of identifying the effect of a single gene. To test for genes which affect sodium transport, we first applied principal components analysis to 14 variables related to sodium transport, thereby defining uncorrelated sources of variation in the variables. The sample consisted of 1,218 members of 68 pedigrees ascertained through probands with early-onset stroke, hypertension, or coronary heart disease. Segregation analysis of the 14 principal components scores provided evidence for 8 genetic variants which alter sodium transport. One of the 8 variants is recessive, has homozygous genotype frequency estimated as 8.8% of the population, and increases sodium-lithium countertransport, the passive sodium leak, body mass index, and triglyceride; the genetic variant may coincide with an insulin resistance gene. A second of the 8 variants is also recessive, has homozygous genotype frequency estimated as 7.4% of the population, and increases intraerythrocytic sodium and the passive sodium leak while decreasing sodium pump number; the genetic variant may reduce pump number. Two of the 8 variants substantially increase sodium-lithium countertransport; frequency estimates for heterozygotes for the dominant variant and homozygotes for the recessive variant equal 1.8% and 3.1 %, respectively. Another of the 8 variants is recessive, has homozygous genotype frequency estimated as 1.9%, and increases body mass index. Each of the 3 remaining variants is rare and expressed in less than 1 % of the sample. © 1994 Wiley-Liss, Inc.     

Rare‐variant association tests in longitudinal studies,with an application to the Multi‐Ethnic Study of Atherosclerosis (MESA)

Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene‐based association tests have been proposed to aggregate the genetic variants within a gene, pathway, or specific genomic region as opposed to a one‐at‐a‐time single variant analysis. In addition, in longitudinal studies, statistical power to detect disease susceptibility rare variants can be improved through jointly testing repeatedly measured outcomes, which better describes the temporal development of the trait of interest. However, usual sandwich/model‐based inference for sequencing studies with longitudinal outcomes and rare variants can produce deflated/inflated type I error rate without further corrections. In this paper, we develop a group of tests for rare‐variant association based on outcomes with repeated measures. We propose new perturbation methods such that the type I error rate of the new tests is not only robust to misspecification of within‐subject correlation, but also significantly improved for variants with extreme rarity in a study with small or moderate sample size. Through extensive simulation studies, we illustrate that substantially higher power can be achieved by utilizing longitudinal outcomes and our proposed finite sample adjustment. We illustrate our methods using data from the Multi‐Ethnic Study of Atherosclerosis for exploring association of repeated measures of blood pressure with rare and common variants based on exome sequencing data on 6,361 individuals.     

A Fast and Noise‐Resilient Approach to Detect Rare‐Variant Associations With Deep Sequencing Data for Complex Disorders

Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare‐variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare‐variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease‐associated P‐values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach, dubbed as Sigma‐P method, is extremely robust to the inclusion of a high proportion of noncausal variants and is also powerful when both detrimental and protective variants are present within a genetic region. The performance of the Sigma‐P method was tested using simulated data based on realistic population demographic and disease models and its power was compared to several previously published methods. The results demonstrate that this method generally outperforms other rare‐variant association methods over a wide range of models. Additionally, sequence data on the ANGPTL family of genes from the Dallas Heart Study were tested for associations with nine metabolic traits and both known and novel putative associations were uncovered using the Sigma‐P method.     

美国医学遗传学会对基因芯片拷贝数变异结果解读指南

全基因组芯片已被推荐作为分析智力障碍、孤独症、多发性出生缺陷病因的首要筛查手段,通过芯片检查发现了人类基因组中大量的拷贝数变异（copy number variation,CNV）,这些变异中既有正常个体的多态性,也有新发的致病性变异。为帮助临床实验室对芯片结果的解读保持一致性,美国医学遗传学会制定了此有关CNV的解读指南。该指南主要应用于产后的分子遗传诊断中。     

A Novel Test for Testing the Optimally Weighted Combination of Rare and Common Variants Based on Data of Parents and Affected Children

With the development of sequencing technologies, the direct testing of rare variant associations has become possible. Many statistical methods for detecting associations between rare variants and complex diseases have recently been developed, most of which are population‐based methods for unrelated individuals. A limitation of population‐based methods is that spurious associations can occur when there is a population structure. For rare variants, this problem can be more serious, because the spectrum of rare variation can be very different in diverse populations, as well as the current nonexistence of methods to control for population stratification in population‐based rare variant associations. A solution to the problem of population stratification is to use family‐based association tests, which use family members to control for population stratification. In this article, we propose a novel test for Testing the Optimally Weighted combination of variants based on data of Parents and Affected Children (TOW‐PAC). TOW‐PAC is a family‐based association test that tests the combined effect of rare and common variants in a genomic region, and is robust to the directions of the effects of causal variants. Simulation studies confirm that, for rare variant associations, family‐based association tests are robust to population stratification although population‐based association tests can be seriously confounded by population stratification. The results of power comparisons show that the power of TOW‐PAC increases with an increase of the number of affected children in each family and TOW‐PAC based on multiple affected children per family is more powerful than TOW based on unrelated individuals.     

Rare variants, common markers: synthetic association and beyond

The phenomenon of synthetic association raises the possibility that common variant genetic markers may be coupled with functional rare variants sufficiently often to allow the rare variants to be tagged by the common ones. Using human exome sequence data from the 1000 Genomes Project, two investigative teams in Group 12 of Genetic Analysis Workshop 17 found that stochastic coupling between rare and common variants does occur, although perhaps not sufficiently often that we can expect common variant signals to reflect synthetic association; other teams considered methods for detecting association using both rare and common variants. Common themes were that synthetic association is more apparent in population strata (ancestral or familial) and that careful selection of the unit of analysis (gene, gene network, or other genomic subset) is likely to be crucial to the discovery of rare variants that contribute to risk of disease.     

The more you test,the more you find: The smallest P‐values become increasingly enriched with real findings as more tests are conducted

The increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow specific scientific hypotheses with statistical analysis, researchers can now test many possible relationships and let statistics generate hypotheses for them. The field of genetic epidemiology is an illustrative case, where testing of candidate genetic variants for association with an outcome has been replaced by agnostic screening of the entire genome. Poor replication rates of candidate gene studies have improved dramatically with the increase in genomic coverage, due to factors such as adoption of better statistical practices and availability of larger sample sizes. Here, we suggest that another important factor behind the improved replicability of genome‐wide scans is an increase in the amount of statistical testing itself. We show that an increase in the number of tested hypotheses increases the proportion of true associations among the variants with the smallest P‐values. We develop statistical theory to quantify how the expected proportion of genuine signals (EPGS) among top hits depends on the number of tests. This enrichment of top hits by real findings holds regardless of whether genome‐wide statistical significance has been reached in a study. Moreover, if we consider only those “failed” studies that produce no statistically significant results, the same enrichment phenomenon takes place: the proportion of true associations among top hits grows with the number of tests. The enrichment occurs even if the true signals are encountered at the logarithmically decreasing rate with the additional testing.     

Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study

A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant's DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype‐phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS‐based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors ( http://zhaocenter.org/software/ ).     

A combination test for detection of gene‐environment interaction in cohort studies

Identifying gene‐environment (G‐E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G‐E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome‐wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed score statistic based genetic association testing approaches to the G‐E interaction testing problem. We also propose tests for interaction using gene‐based summary measures that pool variants together. Although it has recently been shown that these summary measures can be biased and may lead to inflated type I error, we show that under several realistic scenarios, we can still provide valid tests of interaction. These tests use significantly less degrees of freedom and thus can have much higher power to detect interaction. Additionally, we demonstrate that the iSeq‐aSum‐min test, which combines a gene‐based summary measure test, iSeq‐aSum‐G, and an interaction‐based summary measure test, iSeq‐aSum‐I, provides a powerful alternative to test G‐E interaction. We demonstrate the performance of these approaches using simulation studies and illustrate their performance to study interaction between the SNPs in several candidate genes and family climate environment on alcohol consumption using the Minnesota Center for Twin and Family Research dataset.     

DLX3基因罕见变异与发育性髋关节发育不良

目的:筛查DLX3基因(NM_005220)中发育性髋关节发育不良(DDH)相关的致病变异。方法:本研究对192例DDH患者和188例健康对照组的DLX3基因全部外显子区进行Sanger测序,排除已知高频单核苷酸多态性(SNP)位点(最小等位基因频率MAF≥1%)和对照组中存在的变异位点,结合功能性预测和保守性分析,最终筛选出DDH候选致病变异。结果:经过分析,最终在一个DDH患者中筛选出一个错义杂合变异DLX3c.G736C:p.D246H(rs3744539)可能为其致病突变,此变异在物种进化过程中高度保守且致病的可能性较高。结论:本研究首次对DLX3整个外显子区进行变异筛查,并发现新的DDH候选致病变异p.D246H。     

Communicating genetic test results to the family: a six-step, skills-building strategy

As the availability of tests to identify hereditary predisposition to chronic diseases continues to grow, a need has arisen to prepare individuals receiving genetic test results to share this highly sophisticated and value-laden information with other at-risk family members. Responding to this need, a communication skills-building intervention, based on Buckman's model of "Breaking Bad News," was developed for use in the setting of genetic testing for BRCA1 and BRCA2 mutations. Outcomes will include knowledge, attitudes, and health behavior on the part of both the proband and her first-degree relatives.     

Using Whole Exome Sequencing to Identify Candidate Genes With Rare Variants In Nonsyndromic Cleft Lip and Palate

Studies suggest that nonsyndromic cleft lip and palate (NSCLP) is polygenic with variable penetrance, presenting a challenge in identifying all causal genetic variants. Despite relatively high prevalence of NSCLP among Amerindian populations, no large whole exome sequencing (WES) studies have been completed in this population. Our goal was to identify candidate genes with rare genetic variants for NSCLP in a Honduran population using WES. WES was performed on two to four members of 27 multiplex Honduran families. Genetic variants with a minor allele frequency > 1% in reference databases were removed. Heterozygous variants consistent with dominant disease with incomplete penetrance were ascertained, and variants with predicted functional consequence were prioritized for analysis. Pedigree‐specific P‐values were calculated as the probability of all affected members in the pedigree being carriers, given that at least one is a carrier. Preliminary results identified 3,727 heterozygous rare variants; 1,282 were predicted to be functionally consequential. Twenty‐three genes had variants of interest in ≥3 families, where some genes had different variants in each family, giving a total of 50 variants. Variant validation via Sanger sequencing of the families and unrelated unaffected controls excluded variants that were sequencing errors or common variants not in databases, leaving four genes with candidate variants in ≥3 families. Of these, candidate variants in two genes consistently segregate with NSCLP as a dominant variant with incomplete penetrance: ACSS2 and PHYH. Rare variants found at the same gene in all affected individuals in several families are likely to be directly related to NSCLP.     

Estimating and Testing Pleiotropy of Single Genetic Variant for Two Quantitative Traits

Along with the accumulated data of genetic variants and biomedical phenotypes in the genome era, statistical identification of pleiotropy is of growing interest for dissecting and understanding genetic correlations between complex traits. We proposed a novel method for estimating and testing pleiotropic effect of a genetic variant on two quantitative traits. Based on a covariance decomposition and estimation, our method quantifies pleiotropy as the portion of between‐trait correlation explained by the same genetic variant. Unlike most multiple‐trait methods that assess potential pleiotropy (i.e., whether a variant contributes to at least one trait), our method formulates a statistic that tests exact pleiotropy (i.e., whether a variant contributes to both of two traits). We developed two approaches (a regression approach and a bootstrapping approach) for such test and investigated their statistical properties, in comparison with other potential pleiotropy test methods. Our simulation shows that the regression approach produces correct P‐values under both the complete null (i.e., a variant has no effect on both two traits) and the incomplete null (i.e., a variant has effect on only one of two traits), but requires large sample sizes to achieve a good power, when the bootstrapping approach has a better power and produces conservative P‐values under the complete null. We demonstrate our method for detecting exact pleiotropy using a real GWAS dataset. Our method provides an easy‐to‐implement tool for measuring, testing, and understanding the pleiotropic effect of a single variant on the correlation architecture of two complex traits.     

Generalized Functional Linear Models for Gene‐Based Case‐Control Association Studies

By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT‐O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT‐O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT‐O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT‐O in the real data analysis. Our methods can be used in either gene‐disease genome‐wide/exome‐wide association studies or candidate gene analyses.     

Simulating Realistic Genomic Data With Rare Variants

Increasing evidence suggests that rare and generally deleterious genetic variants might have a strong impact on disease risks of not only Mendelian disease, but also many common diseases. However, identifying such rare variants remains challenging, and novel statistical methods and bioinformatic software must be developed. Hence, we have to extensively evaluate various methods under reasonable genetic models. Although there are abundant genomic data, they are not most helpful for the evaluation of the methods because the disease mechanism is unknown. Thus, it is imperative that we simulate genomic data that mimic the real data containing rare variants and that enable us to impose a known disease penetrance model. Although resampling simulation methods have shown their advantages in computational efficiency and in preserving important properties such as linkage disequilibrium (LD) and allele frequency, they still have limitations as we demonstrated. We propose an algorithm that combines a regression‐based imputation with resampling to simulate genetic data with both rare and common variants. Logistic regression model was employed to fit the relationship between a rare variant and its nearby common variants in the 1000 Genomes Project data and then applied to the real data to fill in one rare variant at a time using the fitted logistic model based on common variants. Individuals then were simulated using the real data with imputed rare variants. We compared our method with existing simulators and demonstrated that our method performed well in retaining the real sample properties, such as LD and minor allele frequency, qualitatively.     

The search for genenotype/phenotype associations and the phenome scan

All the approaches to the search for genotype/phenotype associations have their share of problems. Comparing the genome scan and candidate gene approaches, the former makes fewer assumptions at the genetic level or about mechanism but has greater statistical difficulties while the latter partially solves the statistical problem but makes more assumptions at both genetic and mechanistic levels. Among current difficulties is a lack of information about the nature of gene variant/phenotype associations: the frequency with which different classes of gene or sequence are involved; the type of genetic variation most commonly involved; the appropriate genetic models to apply to analysis. The overarching problem is that of multiple testing, one solution to which is to integrate genetic information to create a smaller number of compound variables. At the other end of the scale, decisions about the level of complexity at which to pitch the identification of phenotypes also affect the multiple testing problem: whether to pitch them at the level of disease outcomes, or at any of the multiple levels of intermediate phenotypes or traits. The third issue is how best to deal with gene/gene or gene/environment interactions, or whether to ignore them. Only as more genotype/phenotype associations emerge, by whatever means, will the numbers of results allow these questions to be answered. We describe here a new approach to genotype/phenotype association studies, the phenome scan, in which dense phenotypic information in human cohorts is scanned for associations with individual genetic variants. We believe that this approach can generate data that will be useful in answering generic questions about genotype/phenotype associations as well as in discovering novel ones.