Augmented case-only designs for randomized clinical trials with failure time endpoints.

Under suitable assumptions and by exploiting the independence between inherited genetic susceptibility and treatment assignment, the case-only design yields efficient estimates for subgroup treatment effects and gene-treatment interaction in a Cox model. However it cannot provide estimates of the genetic main effect and baseline hazards, that are necessary to compute the absolute disease risk. For two-arm, placebo-controlled trials with rare failure time endpoints, we consider augmenting the case-only design with random samples of controls from both arms, as in the classical case-cohort sampling scheme, or with a random sample of controls from the active treatment arm only. The latter design is motivated by vaccine trials for cost-effective use of resources and specimens so that host genetics and vaccine-induced immune responses can be studied simultaneously in a bigger set of participants. We show that these designs can identify all parameters in a Cox model and that the efficient case-only estimator can be incorporated in a two-step plug-in procedure. Results in simulations and a data example suggest that incorporating case-only estimators in the classical case-cohort design improves the precision of all estimated parameters; sampling controls only in the active treatment arm attains a similar level of efficiency.

Mendelian randomization studies for a continuous exposure under case-control sampling.

In this article, we assess the impact of case-control sampling on mendelian randomization analyses with a dichotomous disease outcome and a continuous exposure. The 2-stage instrumental variables (2SIV) method uses the prediction of the exposure given genotypes in the logistic regression for the outcome and provides a valid test and an approximation of the causal effect. Under case-control sampling, however, the first stage of the 2SIV procedure becomes a secondary trait association, which requires proper adjustment for the biased sampling. Through theoretical development and simulations, we compare the naïve estimator, the inverse probability weighted estimator, and the maximum likelihood estimator for the first-stage association and, more importantly, the resulting 2SIV estimates of the causal effect. We also include in our comparison the causal odds ratio estimate derived from structural mean models by double-logistic regression. Our results suggest that the naïve estimator is substantially biased under the alternative, yet it remains unbiased under the null hypothesis of no causal effect; the maximum likelihood estimator yields smaller variance and mean squared error than other estimators; and the structural mean models estimator delivers the smallest bias, though generally incurring a larger variance and sometimes having issues in algorithm stability and convergence.

Evaluation of published single nucleotide polymorphisms associated with acute GVHD.

Candidate genetic associations with acute GVHD (aGVHD) were evaluated with the use of genotyped and imputed single-nucleotide polymorphism data from genome-wide scans of 1298 allogeneic hematopoietic cell transplantation (HCT) donors and recipients. Of 40 previously reported candidate SNPs, 6 were successfully genotyped, and 10 were imputed and passed criteria for analysis. Patient and donor genotypes were assessed for association with grades IIb-IV and III-IV aGVHD, stratified by donor type, in univariate and multivariate allelic, recessive and dominant models. Use of imputed genotypes to replicate previous IL10 associations was validated. Similar to previous publications, the IL6 donor genotype for rs1800795 was associated with a 20%-50% increased risk for grade IIb-IV aGVHD after unrelated HCT in the allelic (adjusted P = .011) and recessive (adjusted P = .0013) models. The donor genotype was associated with a 60% increase in risk for grade III-IV aGVHD after related HCT (adjusted P = .028). Other associations were found for IL2, CTLA4, HPSE, and MTHFR but were inconsistent with original publications. These results illustrate the advantages of using imputed single-nucleotide polymorphism data in genetic analyses and demonstrate the importance of validation in genetic association studies.

Effect of MHC and non-MHC donor/recipient genetic disparity on the outcome of allogeneic HCT.

The outcome of allogeneic hematopoietic cell transplantation is influenced by donor/recipient genetic disparity at loci both inside and outside the MHC on chromosome 6p. Although disparity at loci within the MHC is the most important risk factor for the development of severe GVHD, disparity at loci outside the MHC that encode minor histocompatibility (H) antigens can elicit GVHD and GVL activity in donor/recipient pairs who are otherwise genetically identical across the MHC. Minor H antigens are created by sequence and structural variations within the genome. The enormous variation that characterizes the human genome suggests that the total number of minor H loci is probably large and ensures that all donor/recipient pairs, despite selection for identity at the MHC, will be mismatched for many minor H antigens. In addition to mismatch at minor H loci, unrelated donor/recipient pairs exhibit genetic disparity at numerous loci within the MHC, particularly HLA-DP, despite selection for identity at HLA-A, -B, -C, and -DRB1. Disparity at HLA-DP exists in 80% of unrelated pairs and clearly influences the outcome of unrelated hematopoietic cell transplantation; the magnitude of this effect probably exceeds that associated with disparity at any locus outside the MHC.

Predicting multiallelic genes using unphased and flanking single nucleotide polymorphisms.

Recent advances in genotyping technologies have enabled genomewide association studies (GWAS) of many complex traits including autoimmune disease, infectious disease, cancer and heart disease. To facilitate interpretations and establish biological basis, it could be advantageous to identify alleles of functional genes, beyond just single nucleotide polymorphisms (SNPs) within or nearby genes. Leslie et al. ([2008] Am J Hum Genet 82:48­56) have proposed an Identity-by-Decent method (IBD-based) for predicting human leukocyte antigen (HLA) alleles (multiallelic and highly polymorphic) with SNP data, and predictions have achieved a satisfactory accuracy on the order of 97%. Building upon their success, we introduce a complementary method for predicting highly polymorphic alleles using unphased SNP data as the training data set. Due to its generality and flexibility, the new method is readily applicable to large population studies. Applying it to HLA genes in a cohort of 630 healthy individuals as a training set, we constructed predictive models for HLA-A, B, C, DRB1 and DQB1. Then, we performed a validation study with another cohort of 630 healthy individuals, and the predictive models achieved predictive accuracies for HLA alleles defined at intermediate or high resolution ranging as high as (100%, 97%) for HLA-A, (98%, 96%) for B, (98%, 98%) for C, (97%, 96%) for DRB1 and (98%, 95%) for DQB1, respectively. These preliminary results suggest the feasibility of predicting other polymorphic genetic alleles, since HLA loci are almost certainly among most polymorphic genes.

Sequencing genes in silico using single nucleotide polymorphisms.

BACKGROUND: The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs) discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive. RESULTS: To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS), which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles) at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%). This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC) Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes. CONCLUSIONS: Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate genes for more detailed functional and mechanistic studies.

Empirical evaluations of analytical issues arising from predicting HLA alleles using multiple SNPs.

BACKGROUND: Numerous immune-mediated diseases have been associated with the class I and II HLA genes located within the major histocompatibility complex (MHC) consisting of highly polymorphic alleles encoded by the HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1 loci. Genotyping for HLA alleles is complex and relatively expensive. Recent studies have demonstrated the feasibility of predicting HLA alleles, using MHC SNPs inside and outside of HLA that are typically included in SNP arrays and are commonly available in genome-wide association studies (GWAS). We have recently described a novel method that is complementary to the previous methods, for accurately predicting HLA alleles using unphased flanking SNPs genotypes. In this manuscript, we address several practical issues relevant to the application of this methodology. RESULTS: Applying this new methodology to three large independent study cohorts, we have evaluated the performance of the predictive models in ethnically diverse populations. Specifically, we have found that utilizing imputed in addition to genotyped SNPs generally yields comparable if not better performance in prediction accuracies. Our evaluation also supports the idea that predictive models trained on one population are transferable to other populations of the same ethnicity. Further, when the training set includes multi-ethnic populations, the resulting models are reliable and perform well for the same subpopulations across all HLA genes. In contrast, the predictive models built from single ethnic populations have superior performance within the same ethnic population, but are not likely to perform well in other ethnic populations. CONCLUSIONS: The empirical explorations reported here provide further evidence in support of the application of this approach for predicting HLA alleles with GWAS-derived SNP data. Utilizing all available samples, we have built "state of the art" predictive models for HLA-A, -B, -C, -DRB1, -DQB1 and -DPB1. The HLA allele predictive models, along with the program used to carry out the prediction, are available on our website.

Treatment as Prevention: Characterization of Partner Infections in the HIV Prevention Trials Network 052 Trial.

HIV Prevention Trials Network 052 demonstrated that antiretroviral therapy (ART) prevents HIV transmission in serodiscordant couples. HIV from index-partner pairs was analyzed to determine the genetic linkage status of partner infections. Forty-six infections were classified as linked, indicating that the index was the likely source of the partner's infection. Lack of viral suppression and higher index viral load were associated with linked infection. Eight linked infections were diagnosed after the index started ART: 4 near the time of ART initiation and 4 after ART failure. Linked infections were not observed when the index participant was stably suppressed on ART.

Tumor evolution and intratumor heterogeneity of an oropharyngeal squamous cell carcinoma revealed by whole-genome sequencing.

Head and neck squamous cell carcinoma (HNSCC) is characterized by significant genomic instability that could lead to clonal diversity. Intratumor clonal heterogeneity has been proposed as a major attribute underlying tumor evolution, progression, and resistance to chemotherapy and radiation. Understanding genetic heterogeneity could lead to treatments specific to resistant and metastatic tumor cells. To characterize the degree of intratumor genetic heterogeneity within a single tumor, we performed whole-genome sequencing on three separate regions of an human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma and two separate regions from one corresponding cervical lymph node metastasis. This approach achieved coverage of approximately 97.9% of the genome across all samples. In total, 5701 somatic point mutations (SPMs) and 4347 small somatic insertions and deletions (indels)were detected in at least one sample. Ninety-two percent of SPMs and 77% of indels were validated in a second set of samples adjacent to the discovery set. All five tumor samples shared 41% of SPMs, 57% of the 1805 genes with SPMs, and 34 of 55 cancer genes. The distribution of SPMs allowed phylogenetic reconstruction of this tumor's evolutionary pathway and showed that the metastatic samples arose as a late event. The degree of intratumor heterogeneity showed that a single biopsy may not represent the entire mutational landscape of HNSCC tumors. This approach may be used to further characterize intratumor heterogeneity in more patients, and their sample-to-sample variations could reveal the evolutionary process of cancer cells, facilitate our understanding of tumorigenesis, and enable the development of novel targeted therapies.