Effect Of Multiple Single Nucleotide Polymorphisms Research Articles

A Missense Variant in Granulysin is Associated with the Efficacy of Pegylated-Interferon-Alpha Therapy in Chinese Patients with HBeAg-Positive Chronic Hepatitis B.

PurposeGranulysin (GNLY) is a cytotoxic granule that has been reported to have various antimicrobial activities. We evaluated the association between a missense variant in GNLY (rs11127) and treatment efficacy of pegylated interferon-alpha (PegIFNα) or nucleos(t)ide analogs (NUCs) in patients with chronic hepatitis B (CHB).Patients and MethodsWe included a total of 1823 patients with hepatitis B e antigen (HBeAg)-positive CHB (954 patients treated with PegIFNα and 869 patients treated with NUCs) in four Phase IV multicenter randomized controlled trials. The association of the GNLY rs11127 genotype with the combined response (CR), defined as HBeAg seroconversion and hepatitis B virus (HBV) DNA level <2000 IU/mL was evaluated. A polygenic score (PGS) was constructed to evaluate the cumulative effect of multiple single-nucleotide polymorphisms (SNPs), including rs11127 and several other SNPs, STAT4 rs7574865, CFB rs12614, and CD55 rs28371597, which were reported to be associated with CR.ResultsGNLY rs11127 was significantly associated with CR in patients treated with PegIFNα. The CR rate in patients with the rs11127 CC genotype was higher than that with the CT or TT genotype (40.98% vs 30.34% or 27.09%, P = 0.003). Furthermore, a PGS integrating GNLY rs11127 and three other SNPs was significantly associated with CR in PegIFNα-treated patients (P < 0.001). However, no significant correlation was found between GNLY rs11127 and CR in NUCs-treated patients.ConclusionGNLY rs11127 is an independent biomarker for predicting the response to PegIFNα therapy in HBeAg-positive CHB patients. Furthermore, the PGS, including GNLY rs11127, provides new insights for individualized treatment in clinical practice.

Abstract 2419: A polygenic risk score predicts breast cancer risk in Latinas

Abstract Background: Genome-wide association studies (GWAS) have identified over 180 single nucleotide polymorphisms (SNPs) associated with breast cancer risk. Polygenic risk scores (PRS) represent the combined effects of multiple SNPs and have been consistently shown to improve the performance of existing risk models. Most SNPs were discovered in women of European ancestry. Thus, there are limited data on how PRS perform in Latinas, whose ancestry is a mixture of European and Indigenous American. We therefore sought to validate the performance of a 184-SNP PRS in Latinas. Methods: We conducted a pooled analysis of U.S. Latina and Mexican participants from 6 existing studies. Our analysis included 3,441 women with breast cancer and 7,773 women without breast cancer. We constructed a PRS containing 184 SNPs associated with breast cancer in prior GWAS, p &amp;lt; 5 x 10-8. We used multivariable logistic regression to evaluate the associations between the PRS and breast cancer, adjusting for genetic ancestry and study of origin. To assess discrimination, we calculated the area under the receiver operating characteristic curve (AUROC). To test the performance of the PRS by genetic ancestry, we stratified our analysis by quartiles of Indigenous American genetic ancestry. Results: The PRS was associated with breast cancer, with an odds ratio (OR) per standard deviation increment of 1.51 (95% CI 1.44-1.59). The PRS showed intermediate discrimination for cases vs. controls, with an AUROC of 0.62 (95% CI 0.60-0.63). When we stratified the analysis by genetic ancestry, the PRS performed worse in women with higher Indigenous American/lower European ancestry. Specifically, the discrimination of the PRS was least in the top quartile of Indigenous American ancestry and greatest in the bottom quartile, with AUROC of 0.60 (95% CI 0.57-0.62) vs. 0.65 (95% CI 0.62-0.67), respectively (p = 0.01). The corresponding ORs per standard deviation of the PRS were 1.40 (95% CI 1.29-1.53) for the top quartile and 1.70 (95% CI 1.52-1.89) for the bottom quartile of Indigenous American ancestry (p = 0.006). Conclusions: A 184-SNP PRS predicts breast cancer in Latinas, with the AUROC of 0.62 in our study being comparable to previous estimates in Europeans. However, the performance of the PRS varied according to genetic ancestry. While our results suggest the PRS may be used as-is in Latinas with higher European ancestry, future efforts should focus on refining the PRS in Latinas of higher Indigenous American ancestry through discovery and replication of novel predictive SNPs in this subgroup. Citation Format: Yiwey Shieh, Laura Fejerman, Sarah D. Sawyer, Donglei Hu, Scott Huntsman, Esther M. John, Lawrence H. Kushi, Gabriela Torres-Mejia, Jeffrey N. Weitzel, Christopher A. Haiman, Elad Ziv, Susan L. Neuhausen. A polygenic risk score predicts breast cancer risk in Latinas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2419.

Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging.

Genomewide association studies (GWAS) sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple single-nucleotide polymorphisms (SNPs) simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights. It estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing an SNP prioritization that best identifies underlying true signals, we show the following: our method easily outperforms a single-marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.

Results of a "GWAS plus:" general cognitive ability is substantially heritable and massively polygenic.

We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus three other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens. We conducted the GWAS across ∼2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric bootstrapping, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA. The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score. Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect. We place our study in the context of the literature–both contemporary and historical–and provide accessible explication of our statistical methods.

Gene analysis for longitudinal family data using random-effects models.

We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10-12).

Supervised categorical principal component analysis for genome-wide association analyses

In order to have a better understanding of unexplained heritability for complex diseases in conventional Genome-Wide Association Studies (GWAS), aggregated association analyses based on predefined functional regions, such as genes and pathways, become popular recently as they enable evaluating joint effect of multiple Single-Nucleotide Polymorphisms (SNPs), which helps increase the detection power, especially when investigating genetic variants with weak individual effects. In this paper, we focus on aggregated analysis methods based on the idea of Principal Component Analysis (PCA). The past approaches using PCA mostly make some inherent genotype data and/or risk effect model assumptions, which may hinder the accurate detection of potential disease SNPs that influence disease phenotypes. In this paper, we derive a general Supervised Categorical Principal Component Analysis (SCPCA), which explicitly models categorical SNP data without imposing any risk effect model assumption. We have evaluated the efficacy of SCPCA with the comparison to a traditional Supervised PCA (SPCA) and a previously developed Supervised Logistic Principal Component Analysis (SLPCA) based on both the simulated genotype data by HAPGEN2 and the genotype data of Crohn's Disease (CD) from Wellcome Trust Case Control Consortium (WTCCC). Our preliminary results have demonstrated the superiority of SCPCA over both SPCA and SLPCA due to its modeling explicitly designed for categorical SNP data as well as its flexibility on the risk effect model assumption.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-S1-S10) contains supplementary material, which is available to authorized users.

Kernel machine SNP-set testing under multiple candidate kernels.

Joint testing for the cumulative effect of multiple single-nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large-scale genetic association studies. The kernel machine (KM)-testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori because this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest P-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power vs. using the best candidate kernel.

Predicting Adaptive Phenotypes From Multilocus Genotypes in Sitka Spruce (Picea sitchensis) Using Random Forest

Climate is the primary driver of the distribution of tree species worldwide, and the potential for adaptive evolution will be an important factor determining the response of forests to anthropogenic climate change. Although association mapping has the potential to improve our understanding of the genomic underpinnings of climatically relevant traits, the utility of adaptive polymorphisms uncovered by such studies would be greatly enhanced by the development of integrated models that account for the phenotypic effects of multiple single-nucleotide polymorphisms (SNPs) and their interactions simultaneously. We previously reported the results of association mapping in the widespread conifer Sitka spruce (Picea sitchensis). In the current study we used the recursive partitioning algorithm ‘Random Forest’ to identify optimized combinations of SNPs to predict adaptive phenotypes. After adjusting for population structure, we were able to explain 37% and 30% of the phenotypic variation, respectively, in two locally adaptive traits—autumn budset timing and cold hardiness. For each trait, the leading five SNPs captured much of the phenotypic variation. To determine the role of epistasis in shaping these phenotypes, we also used a novel approach to quantify the strength and direction of pairwise interactions between SNPs and found such interactions to be common. Our results demonstrate the power of Random Forest to identify subsets of markers that are most important to climatic adaptation, and suggest that interactions among these loci may be widespread.

Pathway analysis for family data using nested random-effects models.

Recently we proposed a novel two-step approach to test for pathway effects in disease progression. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to certain genes. By using random effects, our approach acknowledges the correlations within and between genes when testing for pathway effects. Gene-gene and gene-environment interactions can be included in the model. The method can be implemented with standard software, and the distribution of the test statistics under the null hypothesis can be approximated by using standard chi-square distributions. Hence no extensive permutations are needed for computations of the p-value. In this paper we adapt and apply the method to family data, and we study its performance for sequence data from Genetic Analysis Workshop 17. For the set of unrelated subjects, the performance of the new test was disappointing. We found a power of 6% for the binary outcome and of 18% for the quantitative trait Q1. For family data the new approach appears to perform well, especially for the quantitative outcome. We found a power of 39% for the binary outcome and a power of 89% for the quantitative trait Q1.

A two‐stage mixed‐effects model approach for gene‐set analyses in candidate gene studies

In genetic association studies, a gene-set analysis can be more powerful than the separate analyses of multiple genetic variants and can offer unique insights into the genetic basis of many common human diseases. The goal of such an analysis is to study the joint effect of multiple single-nucleotide polymorphisms (SNPs) which belong to certain genes, and these genes are assumed to be involved in a common biological function. Currently, few approaches acknowledge the within-genes and between-genes correlations when testing for gene-set effects. Thus, here we propose a two-stage approach, which in the first stage uses a mixed-effects model with a general random-effects structure to capture the correlation between the SNPs and in the second stage tests for gene-set effects by using the empirical Bayes estimates of the random effects of the first stage as covariates in the model for the longitudinal phenotype. The advantage of this approach is its broad applicability because it can be used for any phenotypic outcome and any genetic model and can be implemented with standard statistical software.

Abstract 2738: Insights into survival from ovarian cancer revealed by gene set analysis

Abstract Epithelial ovarian cancer (EOC) is the 6th most commonly diagnosed cancer among women in the world, accounting for 4% of all female cancers. On a worldwide basis, an estimated 204,000 new cases are diagnosed and 125,000 women die of EOC annually, with 5-year survival rate at 45%. Metastasis and its complications are the usual cause of EOC-related deaths. Thus, factors related to tumor aggressiveness, response to therapy, and the underlying health state of the patient often predict survival. Candidate gene and genome-wide association studies (GWAS) for survival from EOC have discovered only a few the genetic variants, accounting for only a small proportion of the heritability. One explanation for the missing heritability is that the common analysis approach _ assessing the effect of each single nucleotide polymorphism (SNP) individually _ is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) overcomes this limitation by assessing the overall evidence of association of a phenotype with all measured variation in a set of genes or pathway. Therefore, we conducted a GSA using data from an EOC GWAS, using the Illumina 610-quad SNP array, to determine novel gene sets (GSs) associated with overall survival (OS) from EOC (N cases = 1057, N deaths = 495). All subjects were non-Hispanic, non-Jewish Caucasians, with sample and SNP call rates &amp;gt;95%. Subjects with ambiguous gender, unresolved identical genotypes, and less than 80% European ancestry were also excluded. SNPs were mapped to genes within 20 kb, with genes then mapped to GSs defined by the following: KEGG, Gene Ontology (biological, cellular, molecular) and PharmGKB. GSA was completed in which the association of each gene with OS was assessed using a principal component gene-level test (adjusting for study site, age and population sub-structure based on the first eigenvector from EigenStrat), followed by the aggregation of the gene-level p-values using the Gamma method with a soft truncation threshold of 0.15. Empirical p-values were determined using permutation methods. Of the 2,573 GSs assessed, 30 GSs had a p-value &amp;lt; 0.01. The top GSs were related to: mismatch repair (p = 3.0E-4), platelet formation (p=0.001), leukocyte adhesive activation (p=0.002), antigen processing and presentation of endogenous antigens (p = 0.002) or peptide antigens (p = 0.002), platinum pathway (p = 0.003), aging and lifespan (p = 0.004, p = 0.007), immune system development (p = 0.004) and somatic diversification (p=0.007). These results illustrate the usefulness of GSA for the development of novel hypotheses that can be followed-up with additional clinical or functional studies. Moreover, application of GSA to survival from EOC allowed the detection of significant GSs (and the corresponding genes/SNPs within these GSs) that impact OS that were not detected by assessing each SNP individually, where the smallest p-value from single SNP analysis of the GWAS for OS was 1.3E-05. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2738. doi:10.1158/1538-7445.AM2011-2738