Introduction
When a study sample includes a large proportion of long‐term survivors, mixture cure (MC) models that separately assess biomarker associations with long‐term recurrence‐free survival and ...time to disease recurrence are preferred to proportional‐hazards models. However, in samples with few recurrences, standard maximum likelihood can be biased.
Objective and Methods
We extend Firth‐type penalized likelihood (FT‐PL) developed for bias reduction in the exponential family to the Weibull‐logistic MC, using the Jeffreys invariant prior. Via simulation studies based on a motivating cohort study, we compare parameter estimates of the FT‐PL method to those by ML, as well as type 1 error (T1E) and power obtained using likelihood ratio statistics.
Results
In samples with relatively few events, the Firth‐type penalized likelihood estimates (FT‐PLEs) have mean bias closer to zero and smaller mean squared error than maximum likelihood estimates (MLEs), and can be obtained in samples where the MLEs are infinite. Under similar T1E rates, FT‐PL consistently exhibits higher statistical power than ML in samples with few events. In addition, we compare FT‐PL estimation with two other penalization methods (a log‐F prior method and a modified Firth‐type method) based on the same simulations.
Discussion
Consistent with findings for logistic and Cox regressions, FT‐PL under MC regression yields finite estimates under stringent conditions, and better bias‐and‐variance balance than the other two penalizations. The practicality and strength of FT‐PL for MC analysis is illustrated in a cohort study of breast cancer prognosis with long‐term follow‐up for recurrence‐free survival.
Abstract
Summary
For the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the ...genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20 000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within reasonable time and memory limits in personal computing environments.
Availability and implementation
The R package is available at https://bioconductor.org/packages/gpart.
Supplementary information
Supplementary data are available at Bioinformatics online.
Aim Genes that differentiate patients with psoriatic arthritis (PsA) from those with cutaneous psoriasis (PsC) may serve as markers for the development of PsA in patients with psoriasis. The authors ...aimed to identify human leucocyte antigen (HLA) alleles that are associated with the development of PsA in patients with psoriasis.
712 adult patients with PsA, 335 adult patients with PsC and 713 healthy controls were genotyped for HLA-A, HLA-B, HLA-C, HLA-DR and HLA-DQ alleles. Differences in allelic distributions for each of the HLA loci were compared using a likelihood ratio test. Logistic regression analysis of multiple loci was performed to account for linkage disequilibrium. Haplotype information was inferred using the expectation-maximisation algorithm (given HLA-C and HLA-B genotypes) and analysed similarly.
The following HLA alleles were found to be significantly associated with patients with PsA compared to patients with PsC in multivariate regression analysis: B*08 (OR 1.61, p=0.009), B*27 (OR 5.17, p<0.0001), B*38 (OR 1.65, p=0.026) and C*06 (OR 0.58, p=0.0002). HLA-B*27, HLA-B*38 and HLA-C*06 frequencies were also significantly higher in patients with PsA than in healthy controls (B*27: OR 3.05, p<0.0001; B*38: OR 5.9, p<0.0001; HLA-C*06: OR 1.71, p<0.0001). The following haplotypes were independently associated with PsA compared to PsC: HLA-B*18-C*07 (OR 10.1, p=0.004), HLA-B*27-C*01 (OR 41.1, p<0.0001), HLA-B*27-C*02 (OR 19.9, p<0.0001), HLA-B*38-C*12 (OR 2.9, p=0.01), HLA-B*08-C*07 (OR 2.6, p=0.004) and HLA-B*57-C*06 (OR 0.5, p=0.03).
Certain HLA-B and HLA-C alleles confer susceptibility to PsA among patients with psoriasis and may be used to identify patients with PsC who may develop PsA.
Abstract
Motivation
Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide ...polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation.
Results
We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods.
Availability and implementation
Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD.
Supplementary information
Supplementary data are available at Bioinformatics online.
Elevated MET receptor tyrosine kinase correlates with poor outcome in breast cancer, yet the reasons for this are poorly understood. We thus generated a transgenic mouse model targeting expression of ...an oncogenic Met receptor (Metmt) to the mammary epithelium. We show that Metmt induces mammary tumors with multiple phenotypes. These reflect tumor subtypes with gene expression and immunostaining profiles sharing similarities to human basal and luminal breast cancers. Within the basal subtype, Metmt induces tumors with signatures of WNT and epithelial to mesenchymal transition (EMT). Among human breast cancers, MET is primarily elevated in basal and ERBB2-positive subtypes with poor prognosis, and we show that MET, together with EMT marker, SNAIL, are highly predictive of poor prognosis in lymph node-negative patients. By generating a unique mouse model in which the Met receptor tyrosine kinase is expressed in the mammary epithelium, along with the examination of MET expression in human breast cancer, we have established a specific link between MET and basal breast cancer. This work identifies basal breast cancers and, additionally, poor-outcome breast cancers, as those that may benefit from anti-MET receptor therapies.
Post‐GWAS analysis, in many cases, focuses on fine‐mapping targeted genetic regions discovered at GWAS‐stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for ...complex traits and disease outcomes using next‐generation sequencing (NGS) technologies. Large‐scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two‐phase sampling design and analysis is a cost‐reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual‐dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at
github.com/egosv/TwoPhase_postGWAS.
The objectives of this study were to determine the prognostic significance of subgrouping estrogen receptor (ER)-positive breast tumors into low- and high-risk luminal categories using Ki67 index, ...TP53, or progesterone receptor (PR) status. The study group comprised 540 patients with lymph node negative, invasive breast carcinoma. Luminal A subtype was defined as being ER positive, HER2 negative, and Ki67 low (<14% cells positive) and luminal B subtype as being ER positive, HER2 negative, and Ki67 high (≥14% cells positive). Luminal tumors were also subgrouped into risk categories based on the PR and TP53 status. Survival analysis was performed. Patients with luminal B tumors (n=173) had significantly worse disease-free survival compared to those with luminal A tumors (n=186) (log rank P-value=0.0164; univariate Cox regression relative risk 2.00; 95% CI, 1.12–3.58; P=0.0187). Luminal subtype remained an independent prognostic indicator on multivariate analysis including traditional prognostic factors (relative risk 2.12; 95% CI, 1.16–3.88; P=0.0151). Using TP53 status or PR negativity rather than Ki67 to classify ER-positive luminal tumors gave similar outcome results to those obtained using the proliferation index. However, it was a combination of the three markers, which proved the most powerful prognostically. Ki67 index, TP53 status, or PR negativity can be used to segregate ER-positive, HER2-negative tumors into prognostically meaningful subgroups with significantly different clinical outcomes. These biomarkers particularly in combination may potentially be used clinically to guide patient management.
Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample ...sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.
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