Human Heredity最新文献_第9页

Unbalanced Sample Size Introduces Spurious Correlations to Genome-Wide Heterozygosity Analyses. 不平衡的样本量给全基因组杂合性分析带来了虚假的相关性。

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-06-15 DOI: 10.1159/000507576

Li Liu, Richard J Caselli

{"title":"Unbalanced Sample Size Introduces Spurious Correlations to Genome-Wide Heterozygosity Analyses.","authors":"Li Liu, Richard J Caselli","doi":"10.1159/000507576","DOIUrl":"https://doi.org/10.1159/000507576","url":null,"abstract":"Excess of heterozygosity (H) is a widely used measure of genetic diversity of a population. As high-throughput sequencing and genotyping data become readily available, it has been applied to investigating the associations of genome-wide genetic diversity with human diseases and traits. However, these studies often report contradictory results. In this paper, we present a meta-analysis of five whole-exome studies to examine the association of H scores with Alzheimer's disease. We show that the mean H score of a group is not associated with the disease status, but ot is associated with the sample size. Across all five studies, the group with more samples has a significantly lower H score than the group with fewer samples. To remove potential confounders in empirical data sets, we perform computer simulations to create artificial genomes controlled for the number of polymorphic loci, the sample size, and the allele frequency. Analyses of these simulated data confirm the negative correlation between the sample size and the H score. Furthermore, we find that genomes with a large number of rare variants also have inflated H scores. These biases altogether can lead to spurious associations between genetic diversity and the phenotype of interest. Based on these findings, we advocate that studies shall balance the sample sizes when using genome-wide H scores to assess genetic diversities of different populations, which helps improve the reproducibility of future research.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 4-5","pages":"197-202"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000507576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38051891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Frequency of BRCA1 and BRCA2 Mutations in Individuals with Breast and Ovarian Cancer in a Chinese Hakka Population Using Next-Generation Sequencing. 利用下一代测序技术研究中国客家人乳腺癌和卵巢癌患者BRCA1和BRCA2突变频率

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-02-26 DOI: 10.1159/000505268

Heming Wu, Qiuming Wang, Xuemin Guo, Qinghua Liu, Qunji Zhang, Qingyan Huang, Zhikang Yu

{"title":"Frequency of BRCA1 and BRCA2 Mutations in Individuals with Breast and Ovarian Cancer in a Chinese Hakka Population Using Next-Generation Sequencing.","authors":"Heming Wu, Qiuming Wang, Xuemin Guo, Qinghua Liu, Qunji Zhang, Qingyan Huang, Zhikang Yu","doi":"10.1159/000505268","DOIUrl":"https://doi.org/10.1159/000505268","url":null,"abstract":"Background: It is necessary to investigate the frequency of BRCA1 and BRCA2 mutations in Hakka populations due to the variations in breast cancer epidemiology and genetics.Methods: 359 breast cancer patients and 66 ovarian cancer patients were included in this retrospective clinical study. Mutations of BRCA1 and BRCA2 were detected in blood samples by semiconductor sequencing.Results: The sensitivity of tumor markers including CEA, CA15-3, CA12-5, and CA199 for screening breast cancer was 16.44, 15.11, 8.44, and 7.56%, the combination of these 4 tumor markers reached the highest sensitivity index (31.11%). For ovarian cancer, the tumor markers were CA12-5 (54.05%), HE-4 (54.05%), CA72-4 (51.35%), and CEA (2.70%) in order of decreasing sensitivity. Moreover, the combination of these 4 tumor markers has the best sensitivity (75.68%) for screening ovarian cancer. In breast cancer patients, we found 5 (1.39%) patients with mutations in BRCA1, 13 (3.62%) mutations in BRCA2, and the total carrier rate is 5.01% (18/359). For ovarian cancer patients, the corresponding results were 3 (4.54%) mutations, 2 (3.03%) mutations, and 7.58% (5/66), respectively. The proportion of BRCA mutations was 5.41% (23/425) in breast and ovarian cancer patients of a Hakka population. The pathogenic, likely pathogenic, and benign mutations, and mutations of uncertain significance in this study mainly occurred in exon 14 of the BRCA1 gene, and exon 10 and exon 11 of the BRCA2 gene.Conclusions: Understanding the spectrum and frequency of BRCA1 and BRCA2 mutations in a Hakka population will assist in the prevention and control of hereditary breast and ovarian cancers in this population.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 4-5","pages":"160-169"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000505268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37679711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Meta-Analysis of SNP-Environment Interaction with Heterogeneity. SNP-环境相互作用与异质性的 Meta 分析。

IF 1.1 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2019-12-19 DOI: 10.1159/000504170

Qinqin Jin, Gang Shi

{"title":"Meta-Analysis of SNP-Environment Interaction with Heterogeneity.","authors":"Qinqin Jin, Gang Shi","doi":"10.1159/000504170","DOIUrl":"10.1159/000504170","url":null,"abstract":"Meta-analyses are widely used in genome-wide association studies to combine the results obtained from multiple studies. Classical random-effects methods treat genetic heterogeneity as a random effect and consider it as a portion of the variance associated with a fixed effect of the variant. Recent work suggests performing hypothesis testing with the null hypothesis under which neither fixed nor random effects exist for a variant. This method has been shown to perform better than classical random-effects methods. In this work, we propose a meta-analysis of testing single nucleotide polymorphism (SNP)-environment interaction in the presence of genetic heterogeneity. We introduced the random effects of the SNP and SNP-environment interaction under test into a meta-regression model to account for heterogeneity. A test for the SNP-environment interaction was formulated to test for fixed and random effects of the interaction simultaneously. Similarly, a test for total genetic effects was formulated to test for fixed effects of the SNP and the SNP-environment interaction together with their random effects. We performed simulations to study the null distribution and statistical power of the proposed tests. We show that the new methods have higher power than classical random-effects and fixed-effects meta-regression methods when heterogeneity effects are large.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 3","pages":"117-126"},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37481469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

48th European Mathematical Genetics Meeting (EMGM) 2020. 第48届欧洲数学遗传学会议(EMGM) 2020。

IF 1.1 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-04-08 DOI: 10.1159/000507248

Zoltan Kutalik

引用次数: 0

Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies. 在测序关联研究中测试多个性状的基因-环境交互作用

IF 1.8 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2020-05-16 DOI: 10.1159/000506008

Jianjun Zhang, Qiuying Sha, Han Hao, Shuanglin Zhang, Xiaoyi Raymond Gao, Xuexia Wang

{"title":"Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies.","authors":"Jianjun Zhang, Qiuying Sha, Han Hao, Shuanglin Zhang, Xiaoyi Raymond Gao, Xuexia Wang","doi":"10.1159/000506008","DOIUrl":"10.1159/000506008","url":null,"abstract":"Motivation: The risk of many complex diseases is determined by an interplay of genetic and environmental factors. The examination of gene-environment interactions (G×Es) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease-associated genes. However, the methods for testing G×Es for multiple traits are very limited.Method: We developed novel approaches to test G×Es for multiple traits in sequencing association studies. We first perform a transformation of multiple traits by using either principal component analysis or standardization analysis. Then, we detect the effects of G×Es using novel proposed tests: testing the effect of an optimally weighted combination of G×Es (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ Fisher's combination test to combine the p values.Results: Extensive simulation studies show that the type I error rates of the proposed methods are well controlled. Compared to the interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal G×Es. Application to the COPDGene Study demonstrates that our proposed methods are very effective.Conclusions: Our proposed methods are useful tools in the identification of G×Es for multiple traits. The proposed methods can be used not only to identify G×Es for common variants, but also for rare variants. Therefore, they can be employed in identifying G×Es in both genome-wide association studies and next-generation sequencing data analyses.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 4-5","pages":"170-196"},"PeriodicalIF":1.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351593/pdf/nihms-1558071.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37943558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design. 估计分层两阶段病例对照设计中的加性相互作用效应

IF 1.1 4区生物学

Human Heredity Pub Date : 2019-01-01 Epub Date: 2019-10-21 DOI: 10.1159/000502738

Ai Ni, Jaya M Satagopan

{"title":"Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design.","authors":"Ai Ni, Jaya M Satagopan","doi":"10.1159/000502738","DOIUrl":"10.1159/000502738","url":null,"abstract":"Background and aims: There is considerable interest in epidemiology to estimate an additive interaction effect between two risk factors in case-control studies. An additive interaction is defined as the differential reduction in absolute risk associated with one factor between different levels of the other factor. A stratified two-phase case-control design is commonly used in epidemiology to reduce the cost of assembling covariates. It is crucial to obtain valid estimates of the model parameters by accounting for the underlying stratification scheme to obtain accurate and precise estimates of additive interaction effects. The aim of this paper is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified two-phase case-control design.Methods: Using simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probability weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.Results: Simulation studies show that the multiple imputation method performs well when both the true and analysis models are additive (i.e., does not include multiplicative interaction terms) but does not provide a discernible advantage over the offset method when the analysis models are non-additive (i.e., includes multiplicative interaction terms). The offset method exhibits the best overall properties when the analysis model contains multiplicative interaction effects.Conclusion: When estimating additive interaction between risk factors in stratified two-phase case-control studies, we recommend estimating model parameters using multiple imputation when the analysis model is additive, and we recommend the offset method when the analysis model is non-additive.","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":"84 1","pages":"90-108"},"PeriodicalIF":1.1,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925975/pdf/nihms-1053034.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46172932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Front & Back Matter 正面和背面事项

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-11-01 DOI: 10.1159/000495776

引用次数: 0

Front & Back Matter 正面和背面

IF 1.8 4区生物学

Human Heredity Pub Date : 2018-06-01 DOI: 10.1159/000490860

W. Kiess, C. Bornehag, C. Gennings

引用次数: 0

SAGES 2018, Symposium on Advances in Genomics, Epidemiology and Statistics 2018, Philadelphia, PA, USA, June 1, 2018: Abstracts. SAGES 2018，2018 年基因组学、流行病学和统计学进展研讨会，美国宾夕法尼亚州费城，2018 年 6 月 1 日：摘要。

IF 1.8 4区生物学