A simple and fast two-locus quality control test to detect false positives due to batch effects in genome-wide association studies

The impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome-wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two-locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred to thousands. Using simulated data, it was shown that the proposed method was powerful and performed better than other tested existing methods. The power of the proposed approach to detect erroneous genotypes was ～80% for a 3% error rate per SNP. This novel QC approach is easy to implement and computationally efficient, and can lead to a better quality of genotypes for subsequent genotype-phenotype investigations.     

A powerful approach to sub‐phenotype analysis in population‐based genetic association studies

The ultimate goal of genome‐wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub‐phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub‐phenotype analysis is that large disease cohorts are sub‐divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub‐phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub‐phenotypes. Simulations demonstrate the power of the multinomial regression‐based analysis over existing methods when genetic effects differ between sub‐phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome‐wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non‐obese forms of the disease, and provides evidence of a potential novel association that warrants follow‐up in independent replication cohorts. Genet. Epidemiol. 34: 335–343, 2010. © 2009 Wiley‐Liss, Inc.     

Using cases to strengthen inference on the association between single nucleotide polymorphisms and a secondary phenotype in genome‐wide association studies

Case‐control genome‐wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AW or CO approaches, are illustrated by data from a case‐control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome‐wide study, compared to CO analysis. Further experience with genome‐wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions. Genet. Epidemiol. 34:427–433, 2010. Published 2010 Wiley‐Liss, Inc.     

Joint testing of genotype and ancestry association in admixed families

Current genome-wide association studies (GWAS) often involve populations that have experienced recent genetic admixture. Genotype data generated from these studies can be used to test for association directly, as in a non-admixed population. As an alternative, these data can be used to infer chromosomal ancestry, and thus allow for admixture mapping. We quantify the contribution of allele-based and ancestry-based association testing under a family-design, and demonstrate that the two tests can provide non-redundant information. We propose a joint testing procedure, which efficiently integrates the two sources information. The efficiencies of the allele, ancestry and combined tests are compared in the context of a GWAS. We discuss the impact of population history and provide guidelines for future design and analysis of GWAS in admixed populations.     

新疆维吾尔族慢性乙型肝炎患者HBV基因型及亚型分析

目的探讨新疆维吾尔族(维族)慢性乙型肝炎患者中乙型肝炎病毒(HBV)基因型及基因亚型。方法用本实验室建立的HBV基因型及基因亚型特异性引物巢式聚合酶链反应(nPCR)法,对54份新疆维族慢性乙型肝炎患者血清样本进行HBV基因型及亚型分析。随机选取11株经nPCR法鉴定为D基因型的样本,同时进行PreS/S区及PreC/C区测序以鉴定D基因亚型。随机选取2株D型HBV进行基因组全长扩增并克隆测序。从GenBank下载75株各国HBVD1亚型参考序列,根据Kimuratwo-parameter法计算中国新疆地区与其他国家的HBVD1亚型株进化距离。结果新疆维族人群HBV感染以D基因型为主,占66.67%,同时存在B(5.56%)、C(5.56%)基因型及混合感染(22.22%)。新疆存在D1和D3两种亚型,以D1亚型为主。新疆D1亚型与中亚哈萨克斯坦、乌兹别克斯坦、蒙古等国及中东地区土耳其、伊朗、埃及等国的进化距离较近。结论新疆维族慢性乙肝患者中D基因型为优势株,主要为D1亚型,其进化距离与中亚中东等国较为接近。     

Genome sequence conservation of Hendra virus isolates during spillover to horses, Australia

Bat-to-horse transmission of Hendra virus has occurred at least 14 times. Although clinical signs in horses have differed, genome sequencing has demonstrated little variation among the isolates. Our sequencing of 5 isolates from recent Hendra virus outbreaks in horses found no correlation between sequences and time or geographic location of outbreaks.     

Reassortment of Human Rotavirus Gene Segments into G11 Rotavirus Strains

G11 rotaviruses are believed to be of porcine origin. However, a limited number of G11 rotaviruses have been recently isolated from humans in combination with P[25], P[8], P[6], and P[4]. To investigate the evolutionary relationships of these strains, we analyzed the complete genomes of 2 human G11P[25] strains, 2 human G11P[8] strains, and 3 porcine reference strains. Most of the 11 gene segments of these 7 strains belonged to genotype 1 (Wa-like). However, phylogenetic clustering patterns suggested that an unknown G11P[25] strain with a new I12 VP6 genotype was transmitted to the human population, in which it acquired human genotype 1 gene segments through reassortment, resulting in a human G11P[8] rotavirus strain with an entire human Wa-genogroup backbone. This Wa-like backbone is believed to have caused the worldwide spread of human G9 and G12 rotaviruses. G11 human rotavirus strains should be monitored because they may also become major human pathogens.