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Andrew S Allen

Publications and source records attributed to Andrew S Allen.

17 recordsLinked to original sources
PubMed2026

Functional Annotation of the Major Histocompatibility Complex Locus.

The human major histocompatibility complex (MHC) locus has the greatest density of disease-associations in the human genome, including links to over 100 polygenic disorders. Its complex haplotype structure, rich gene density, and high degree of linkage disequilibrium combine to make deciphering the gene regulatory logic of the MHC locus extremely challenging. Employing complementary high-throughput CRISPR interference (CRISPRi) and activation (CRISPRa) epigenetic screens coupled with single-cell transcriptome profiling across three distinct human cell types, we identified hundreds of new connections between cis -regulatory elements (CREs) and their target genes in this locus. These CRE-gene links are largely cell type-specific and act as enhancers. Additionally, some CREs have complex features, including harboring both active and repressive histone marks, lacking chromatin accessibility, targeting multiple genes, or acting as silencers. Computational methods fail to predict a majority of these CRE-gene connections. These findings emphasize the potential for functional perturbation experiments to dissect complex loci and reveal shared and cell type-specific regulatory mechanisms relevant to genomics of complex diseases. Collectively, this study provides a unique resource for understanding the complex regulatory landscape within the MHC locus and supports the need for creating new models that encompass CRE-gene interactions, cell type-specific gene expression, and disease genetics in the noncoding genome.

Journal Article↗
PubMed2025

Bayesian estimation of allele-specific expression in the presence of phasing uncertainty.

MOTIVATION: Allele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors. RESULTS: We developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways. AVAILABILITY AND IMPLEMENTATION: The software is freely available from Github (https://github.com/x811zou/BEASTIE); and Zendo (DOI: 10.5281/zenodo.15062124).

Bayes Theorem↗
PubMed2006

Inference for case-control studies when exposure status is both informatively missing and misclassified.

In case-control studies, it is common for a categorical exposure variable to be misclassified. It is also common for exposure status to be informatively missing for some individuals, in that the probability of missingness may be related to exposure. Procedures for addressing the bias due to misclassification via validation data have been extensively studied, and related methods have been proposed for dealing with informative missingness based on supplemental sampling of some of those with missing data. In this paper, we introduce study designs and analytic procedures for dealing with both problems simultaneously in a 2x2 analysis. Results based on convergence in probability illustrate that the combined effects of missingness and misclassification, even when the latter is non-differential, can lead to naïve exposure odds ratio estimates that are inflated or on the wrong side of the null. The motivating example comes from a case-control study of the association between low birth weight and the diagnosis of breast cancer later in life, where self-reported birth weight for some women is supplemented by accurate information from birth certificates.

Adult↗
PubMed2006

Genetic association tests based on ranks (GATOR) for quantitative traits with and without censoring.

Linkage disequilibrium mapping of quantitative traits is a powerful method for dissecting the genetic etiology of complex phenotypes. Quantitative traits, however, often exhibit characteristics that make their use problematic. For example, the distribution of the trait may be censored, highly skewed, or contaminated with outlying values. We propose here a rank-based framework for deriving tests of gene and trait association that explicitly take censoring into account and are insensitive to skewness and outlying values. Standard methods for mapping quantitative traits do not take these characteristics into account, which leads to the discarding of valuable information or their improper application. We show how this framework can be applied in nuclear families and discuss its implementation in general pedigrees. The power and efficacy of the approach is illustrated through a series of simulation experiments in which the approach is compared to existing methods.

Age of Onset↗
PubMed2005

Application of a rank-based genetic association test to age-at-onset data from the Collaborative Study on the Genetics of Alcoholism study.

Association studies of quantitative traits have often relied on methods in which a normal distribution of the trait is assumed. However, quantitative phenotypes from complex human diseases are often censored, highly skewed, or contaminated with outlying values. We recently developed a rank-based association method that takes into account censoring and makes no distributional assumptions about the trait. In this study, we applied our new method to age-at-onset data on ALDX1 and ALDX2. Both traits are highly skewed (skewness > 1.9) and often censored. We performed a whole genome association study of age at onset of the ALDX1 trait using Illumina single-nucleotide polymorphisms. Only slightly more than 5% of markers were significant. However, we identified two regions on chromosomes 14 and 15, which each have at least four significant markers clustering together. These two regions may harbor genes that regulate age at onset of ALDX1 and ALDX2. Future fine mapping of these two regions with densely spaced markers is warranted.

Adult↗
PubMed2005

Robust testing of haplotype/disease association.

Haplotypes, the combination of closely linked alleles that fall on the same chromosome, show great promise for studying the genetic components of complex diseases. However, when only multilocus genotype data are available, statistical approaches need to be employed to resolve haplotype phase ambiguity. Recently, we have proposed an approach to testing and estimating haplotype/disease association that is invariant to any existing genetic structure in the population. Here we evaluate this approach by applying it to the Genetic Analysis Workshop 14 simulated data.

Bias↗
PubMed2005

Genetic association studies in cardiology.

The last decade has witnessed the emergence of the genetic association study into the mainstream clinical literature. Technological advances and the Human Genome Project have resulted in widespread interest in exploring these new variables in clinical disease. Although the concept of relating genetic variation to disease is exciting, this excitement must be tempered by the many limitations of such analyses. Here we offer a perspective piece on genetic association studies in cardiology. We begin with a fundamental research and clinical interest-linking genes with disease. We then concentrate the body of the article on the statistical aspects of these studies that are often overlooked or underemphasized. We then use specific examples from the literature to illustrate some of our statistical considerations. We then focus on the distinction between association and causation and conclude with perspectives on how this field of study may move forward in a productive manner.

Biomedical Research↗
PubMed2005

Physician-diagnosed depression as a correlate of hospitalizations in patients receiving long-term hemodialysis.

BACKGROUND: Hospital admissions consume a large proportion of costs for the end-stage renal disease (ESRD) program in the United States. We investigated whether a physician diagnosis of depression increases the risk for hospitalization or death in patients with ESRD receiving long-term hemodialysis (HD), independent of medical comorbidities. METHODS: Centralized Veterans Affairs (VA) databases were used to identify a population-based prevalence cohort of 1,588 male patients with ESRD receiving long-term HD in VA facilities between September 1, 2000, and September 30, 2000. International Classification of Diseases, Ninth Revision, codes were used to identify comorbidities and depression diagnosis. Negative binomial regression models were used to examine the association between depression diagnosis and number of hospitalizations and cumulative hospital days in a 2-year observation period. Logistic regression models were used to investigate the association between depression diagnosis and hospitalization, death, and death or hospitalization. RESULTS: The prevalence of physician-diagnosed depression was 14.7%. Patients with a depression diagnosis were more likely to be white and have more comorbidities. Depression diagnosis was associated with increased hospital days (rate ratio for adjusted model, 1.31; 95% confidence interval, 1.04 to 1.66) and increased number of hospitalizations (rate ratio for adjusted model, 1.30; 95% confidence interval, 1.11 to 1.52). Depression diagnosis was not statistically associated with death or the composite of death or hospitalization in adjusted models. CONCLUSION: Physician-diagnosed depression was associated significantly with both increased hospitalization rate and length of stay in patients with ESRD receiving outpatient HD in VA facilities, independent of demographics and comorbidities. Prospective studies should be conducted to assess whether treatment of depression will decrease hospitalization in these patients.

Aged↗
PubMed2004

Long-term survival estimates for imatinib versus interferon-alpha plus low-dose cytarabine for patients with newly diagnosed chronic-phase chronic myeloid leukemia.

BACKGROUND: The authors estimated survival among patients with chronic myeloid leukemia for a cost-effectiveness analysis of imatinib versus interferon-alpha plus low-dose cytarabine (IFN+LDAC). METHODS: Two-year survival and cytogenetic response were determined using data from 553 patients who received first-line imatinib in the International Randomized Interferon versus ST571 Study (IRIS). Long-term survival was modeled on complete cytogenetic response (CCyR) after 2 years. Long-term survival for patients with a CCyR was modeled using data from a cohort study of 317 patients with CCyRs. Long-term survival for patients without a CCyR was modeled using data from a trial of 275 patients who were treated with IFN+LDAC. Computation of lifetime survival estimates for imatinib assumed a proportional hazards relation between survival for an age-matched and gender-matched cohort and survival for patients with and without a CCyR. RESULTS: For IRIS patients receiving imatinib, the estimated survival was 95.8% and the CCyR rate was 73.8%. The average residual life expectancy was estimated to be 16.71 years for CCyR patients and 5.78 years for non-CCyR patients. The estimated life expectancy after treatment with imatinib was 15.30 years, compared with 9.07 years for patients who were treated with IFN+LDAC in previous studies. CONCLUSIONS: Assuming the relation between CCyR and survival with interferon-alpha holds for imatinib, higher CCyR rates with imatinib therapy will result in an estimated 6.23 life-years gained compared with treatment with IFN+LDAC.

Adult↗
PubMed2003

Bootstrap calibration of TRANSMIT for informative missingness of parental genotype data.

Informative missingness of parental genotype data occurs when the genotype of a parent influences the probability of the parent's genotype data being observed. Informative missingness can occur in a number of plausible ways and can affect both the validity and power of procedures that assume the data are missing at random (MAR). We propose a bootstrap calibration of MAR procedures to account for informative missingness and apply our methodology to refine the approach implemented in the TRANSMIT program. We illustrate this approach by applying it to data on hypertensive probands and their parents who participated in the Framingham Heart Study.

Adult Children↗
PubMed2003

Power calculations for large multi-arm placebo-controlled studies of dichotomous outcomes.

Several statistical methods can be used to test for differences in placebo-controlled dichotomous multi-arm studies. These tests include linear and quadratic trends, Dunnett's test, Hochberg's test, and the overall chi-square test. We computed the power of these tests under a wide variety of alternatives. For two arms versus a control, the power was computed exactly. For more than two arms, the power was calculated from 100000 simulations per alternative. Against monotonic alternatives, the linear trend test tended to be superior. Against convex alternatives, the quadratic trend test tended to be superior. Against general alternatives, Dunnett's test, Hochberg's test, and the overall chi-square test were superior and had very similar power curves. The Hochberg test was generally less powerful than Dunnett's test but the difference was offset by its increased power to reject multiple hypotheses. These results remained nearly constant over a wide range of sample sizes, average event rates and average effect sizes.

Abciximab↗
PubMed2003

Missing data in the 2 x 2 table: patterns and likelihood-based analysis for cross-sectional studies with supplemental sampling.

Standard measures of crude association in the context of a cross-sectional study are the risk difference, relative risk and odds ratio as derived from a 2x 2 table. Most such studies are subject to missing data on disease, exposure, or both, introducing bias into the usual complete-case analysis. We describe several scenarios distinguished by the manner in which missing data arise, and for each we adjust the natural multinomial likelihood to properly account for missing data. The situations presented allow for increasing levels of generality with regard to the missing data mechanism. The final case, quite conceivable in epidemiologic studies, assumes that the probability of missing exposure depends on true exposure and disease status, as well as upon whether disease status is missing (and conversely for the probability of missing disease information). When parameters relating to the missing data process are inestimable without strong assumptions, we propose maximum likelihood analysis subsequent to collecting supplemental data in the spirit of a validation study. Analytical results give insight into the bias inherent in complete-case analysis for each scenario, and numerical results illustrate the performance of likelihood-based point and interval estimates in the most general case. Adjustment for potential confounders via stratified analysis is also discussed.

Bias↗
PubMed2003

Informative missingness in genetic association studies: case-parent designs.

We consider the effect of informative missingness on association tests that use parental genotypes as controls and that allow for missing parental data. Parental data can be informatively missing when the probability of a parent being available for study is related to that parent's genotype; when this occurs, the distribution of genotypes among observed parents is not representative of the distribution of genotypes among the missing parents. Many previously proposed procedures that allow for missing parental data assume that these distributions are the same. We propose association tests that behave well when parental data are informatively missing, under the assumption that, for a given trio of paternal, maternal, and affected offspring genotypes, the genotypes of the parents and the sex of the missing parents, but not the genotype of the affected offspring, can affect parental missingness. (This same assumption is required for validity of an analysis that ignores incomplete parent-offspring trios.) We use simulations to compare our approach with previously proposed procedures, and we show that if even small amounts of informative missingness are not taken into account, they can have large, deleterious effects on the performance of tests.

Computer Simulation↗
PubMed2003

Characteristics of treated hypertension in incident hemodialysis and peritoneal dialysis patients.

BACKGROUND: The treatment of hypertension in dialysis patients is prevalent and poorly characterized. beta-Blockers and calcium channel blockers (CCBs) have been associated with reduced all-cause and cardiovascular mortality. This study describes the treatment of hypertension and assesses the association between mortality and class of antihypertensive medication among a cohort of dialysis patients. METHODS: The US Renal Data System (USRDS) Dialysis Morbidity and Mortality Study Wave II cohort was analyzed. A total of 2,877 patients initiating hemodialysis or peritoneal dialysis in 1996 or 1997 and treated with antihypertensives were included in this analysis. Vital status was followed until November 2000. RESULTS: Calcium channel blockers were prescribed to 70.3% of patients. Only 31.5% and 27.0% of patients with cardiovascular disease were prescribed angiotensin-converting enzyme inhibitors and beta-blockers, respectively. Mono-, double-, triple-, and more than triple-therapy were reported in 48.0%, 36.1%, 13.2%, and 2.7% of the cohort, respectively. In multivariable, fully adjusted models, no individual class of antihypertensives was associated with changes in all-cause mortality. In all patients, nondihydropyridine CCBs (non-DHP CCBs) were associated with a reduced risk of cardiovascular death (hazard ratio, 0.78; 95% confidence interval, 0.62 to 0.97) and among end-stage renal disease patients with preexisting cardiovascular disease, dihydropyridine CCBs (DHP CCBs) and non-DHP CCBs were associated with reduced risk of all-cause and cardiovascular mortality. CONCLUSION: Calcium channel blocker use is widespread among hypertensive dialysis patients. Antihypertensive prescription patterns suggest a lack of consensus regarding treatment of hypertension. Multivariable analysis of associations between antihypertensive class and mortality reveals results of uncertain clinical significance. Hypertension treatment trials in dialysis patients should be performed to appropriately inform treatment decisions.

Adrenergic alpha-Agonists↗
PubMed2002

Estimating crude or common odds ratios in case-control studies with informatively missing exposure data.

In case-control studies, the crude odds ratio derived from a 2 x 2 table and the common odds ratio adjusted for stratification variables are staple measures of exposure-disease association. While missing exposure data are encountered in the majority of such studies, formal attempts to deal with them are rare, and a complete-case analysis is the norm. Furthermore, the probability that exposure is missing may depend on true exposure status, so the missing-at-random assumption is often unreasonable. In this paper, the authors present an adjustment to the usual product binomial likelihood to properly account for missing data. Estimation of model parameters without restrictive assumptions requires an additional data collection effort akin to a validation study. Closed-form results are provided to facilitate point and confidence interval estimation of crude and common odds ratios after properly accounting for informatively missing data. Simulations assess performance of the likelihood-based estimates and inferences, and they display the potential for bias in complete-case analyses. An example is presented to illustrate the approach.

Bias↗
PubMed2002

Joint models for toxicology studies with dose-dependent number of implantations.

Many chemicals interfere with the natural reproductive processes in mammals. The chemicals may prevent the fertilization of an egg or keep a zygote from implanting in the uterine wall. For this reason, toxicology studies with pre-implantation exposure often exhibit a dose-related trend in the number of observed implantations per litter. Standard methods for analyzing developmental toxicology studies are conditioned on the number of implantations in the litter and therefore cannot estimate this effect of the chemical on the reproductive process. This article presents a joint modeling approach to estimating risk in toxicology studies with pre-implantation exposure. In the joint modeling approach, both the number of implanted fetuses and the outcome of each implanted fetus is modeled. Using this approach we show how to estimate the overall risk of a chemical that incorporates the risk of lost implantation due to pre-implantation exposure. Our approach has several distinct advantages over previous methods: (1) it is based on fitting a model for the observed data and, therefore, diagnostics of model fit and selection apply; (2) all assumptions are explicitly stated; and (3) it can be fit using standard software packages We illustrate our approach by analyzing a dominant lethal assay data set (Luning et al., 1966, Mutation Research, 3, 444-451) and compare ourresults with those of Rai and Van Ryzin (1985, Biometrics, 41,1-9) and Dunson (1998, Biometrics, 54, 558-569). In a simulation study, our approach has smaller bias and variance than the multiple imputation procedure of Dunson.

Abnormalities, Drug-Induced↗