Genome-wide data from medieval German Jews show that the Ashkenazi founder event pre-dated the 14th century.

We report genome-wide data from 33 Ashkenazi Jews (AJ), dated to the 14th century, obtained following a salvage excavation at the medieval Jewish cemetery of Erfurt, Germany. The Erfurt individuals are genetically similar to modern AJ, but they show more variability in Eastern European-related ancestry than modern AJ. A third of the Erfurt individuals carried a mitochondrial lineage common in modern AJ and eight carried pathogenic variants known to affect AJ today. These observations, together with high levels of runs of homozygosity, suggest that the Erfurt community had already experienced the major reduction in size that affected modern AJ. The Erfurt bottleneck was more severe, implying substructure in medieval AJ. Overall, our results suggest that the AJ founder event and the acquisition of the main sources of ancestry pre-dated the 14th century and highlight late medieval genetic heterogeneity no longer present in modern AJ.

Considerations for pooling real-world data as a comparator cohort to a single arm trial: a simulation study on assessment of heterogeneity.

BACKGROUND: Novel precision medicine therapeutics target increasingly granular, genomically-defined populations. Rare sub-groups make it challenging to study within a clinical trial or single real-world data (RWD) source; therefore, pooling from disparate sources of RWD may be required for feasibility. Heterogeneity assessment for pooled data is particularly complex when contrasting a pooled real-world comparator cohort (rwCC) with a single-arm clinical trial (SAT), because the individual comparisons are not independent as all compare a rwCC to the same SAT. Our objective was to develop a methodological framework for pooling RWD focused on the rwCC use case, and simulate novel approaches of heterogeneity assessment, especially for small datasets. METHODS: We present a framework with the following steps: pre-specification, assessment of dataset eligibility, and outcome analyses (including assessment of outcome heterogeneity). We then simulated heterogeneity assessments for a binary response outcome in a SAT compared to two rwCCs, using standard methods for meta-analysis, and an Adjusted Cochran's Q test, and directly comparing the individual participant data (IPD) from the rwCCs. RESULTS: We found identical power to detect a true difference for the adjusted Cochran's Q test and the IPD method, with both approaches superior to a standard Cochran's Q test. When assessing the impact of heterogeneity in the null scenario of no difference between the SAT and rwCCs, a lack of statistical power led to Type 1 error inflation. Similarly, in the alternative scenario of a true difference between SAT and rwCCs, we found substantial Type 2 error, with underpowered heterogeneity testing leading to underestimation of the treatment effect. CONCLUSIONS: We developed a methodological framework for pooling RWD sources in the context of designing a rwCC for a SAT. When testing for heterogeneity during this process, the adjusted Cochran's Q test matches the statistical power of IPD heterogeneity testing. Limitations of quantitative heterogeneity testing in protecting against Type 1 or Type 2 error indicate these tests are best used descriptively, and after careful selection of datasets based on clinical/data considerations. We hope these findings will facilitate the rigorous pooling of RWD to unlock insights to benefit oncology patients.

FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources).

Expanded clinical validation of Haploseek for comprehensive preimplantation genetic testing.

PURPOSE: We previously developed Haploseek, a method for comprehensive preimplantation genetic testing (PGT). However, some key features were missing, and the method has not yet been systematically validated. METHODS: We extended Haploseek to incorporate DNA from embryo grandparents and to allow testing of variants on chromosome X or in regions where parents share common haplotypes. We then validated Haploseek on 151 embryo biopsies from 27 clinical PGT cases. We sequenced all biopsies to low coverage (0.2×), and performed single-nucleotide polymorphism (SNP) microarray genotyping on the embryos' parents and siblings/grandparents. We used the extended Haploseek to predict chromosome copy-number variants (CNVs) and relevant variant-flanking haplotypes in each embryo. We validated haplotype predictions for each clinical sample against polymerase chain reaction (PCR)-based PGT case results, and CNV predictions against established commercial kits. RESULTS: For each of the 151 embryo biopsies, all Haploseek-derived haplotypes and CNVs were concordant with clinical PGT results. The cases included 17 autosomal dominant, 5 autosomal recessive, and 3 X-linked monogenic disorders. In addition, we evaluated 1 Robertsonian and 2 reciprocal translocations, and 17 cases of chromosome copy-number counting were performed. CONCLUSION: Our results demonstrate that Haploseek is clinically accurate and fit for all standard clinical PGT applications.

How to choose a time zero for patients in external control arms.

When a sponsor carries out a single-arm trial of a novel oncology compound, it may wish to assess the efficacy of the compound via comparison of overall survival to an external control arm, constructed using patients included in some retrospective registry. If efficacy of the novel compound is compared to efficacy of physician's choice of chemotherapy, patients in the retrospective registry might qualify for inclusion in the external control arm at multiple different points in time, when they receive different chemotherapy treatments. For example, a patient might qualify at the start of their second, third and fourth lines of therapy. From the start of which line of therapy should this patient's survival be compared to survival of participants in the single-arm trial? Some sponsors have elected to include patients in the external control arm from the last available line of therapy in the retrospective database. Another possibility is to randomly select a line of therapy for each external control arm patient from among those available. In this paper, we show, via probabilistic arguments and also via simulation based on real data, that both of these methods give rise to a bias in favor of the single-arm trial. We further show that this bias can be avoided by instead including external control arm patients multiple times in the external control arm, once for each time they receive qualifying treatment.

Nonnegative decomposition of functional count data.

We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans.

Haploseek: a 24-hour all-in-one method for preimplantation genetic diagnosis (PGD) of monogenic disease and aneuploidy.

PURPOSE: To develop an economical, user-friendly, and accurate all-in-one next-generation sequencing (NGS)-based workflow for single-cell gene variant detection combined with comprehensive chromosome screening in a 24-hour workflow protocol. METHODS: We subjected single lymphoblast cells or blastomere/blastocyst biopsies from four different families to low coverage (0.3×-1.4×) genome sequencing. We combined copy-number variant (CNV) detection and whole-genome haplotype phase prediction via Haploseek, a novel, user-friendly analysis pipeline. We validated haplotype predictions for each sample by comparing with clinical preimplantation genetic diagnosis (PGD) case results or by single-nucleotide polymorphism (SNP) microarray analysis of bulk DNA from each respective lymphoblast culture donor. CNV predictions were validated by established commercial kits for single-cell CNV prediction. RESULTS: Haplotype phasing of the single lymphoblast/embryo biopsy sequencing data was highly concordant with relevant ground truth haplotypes in all samples/biopsies from all four families. In addition, whole-genome copy-number assessments were concordant with the results of a commercial kit. CONCLUSION: Our results demonstrate the establishment of a reliable method for all-in-one molecular and chromosomal diagnosis of single cells. Important features of the Haploseek pipeline include rapid sample processing, rapid sequencing, streamlined analysis, and user-friendly reporting, so as to expedite clinical PGD implementation.

A test for deviations from expected genotype frequencies on the X chromosome for sex-biased admixed populations.

Genome-wide scans for deviations from expected genotype frequencies, as determined by the Hardy-Weinberg equilibrium (HWE), are commonly applied to detect genotyping errors and deviations from random mating. In contrast to the autosomes, genotype frequencies on the X chromosome do not reach HWE within a single generation. Instead, if allele frequencies in males and females initially differ, they oscillate for a few generations toward equilibrium. Allele frequency differences between the sexes are expected in populations that have experienced recent sex-biased admixture, namely, their male and female founders differed in ancestry. Sex-biased admixture does not allow testing for HWE on X, because deviations are naturally expected, even under random mating (post admixture) and error-free genotyping. In this paper, we develop a likelihood ratio test and a χ2 test to detect deviations from expected genotype frequencies on X, beyond natural deviations due to sex-biased admixture. We demonstrate by simulations that our tests are powerful for detecting deviations due to non-random mating, while at the same time they do not reject the null under historical sex-biased admixture and random mating thereafter. We also demonstrate that when applied to 1000 Genomes project populations, our likelihood ratio test rejects fewer SNPs than other tests, but we describe limitations in the interpretation of the results.

Association of Patient Characteristics and Tumor Genomics With Clinical Outcomes Among Patients With Non-Small Cell Lung Cancer Using a Clinicogenomic Database.

Importance: Data sets linking comprehensive genomic profiling (CGP) to clinical outcomes may accelerate precision medicine. Objective: To assess whether a database that combines EHR-derived clinical data with CGP can identify and extend associations in non-small cell lung cancer (NSCLC). Design, Setting, and Participants: Clinical data from EHRs were linked with CGP results for 28 998 patients from 275 US oncology practices. Among 4064 patients with NSCLC, exploratory associations between tumor genomics and patient characteristics with clinical outcomes were conducted, with data obtained between January 1, 2011, and January 1, 2018. Exposures: Tumor CGP, including presence of a driver alteration (a pathogenic or likely pathogenic alteration in a gene shown to drive tumor growth); tumor mutation burden (TMB), defined as the number of mutations per megabase; and clinical characteristics gathered from EHRs. Main Outcomes and Measures: Overall survival (OS), time receiving therapy, maximal therapy response (as documented by the treating physician in the EHR), and clinical benefit rate (fraction of patients with stable disease, partial response, or complete response) to therapy. Results: Among 4064 patients with NSCLC (median age, 66.0 years; 51.9% female), 3183 (78.3%) had a history of smoking, 3153 (77.6%) had nonsquamous cancer, and 871 (21.4%) had an alteration in EGFR, ALK, or ROS1 (701 [17.2%] with EGFR, 128 [3.1%] with ALK, and 42 [1.0%] with ROS1 alterations). There were 1946 deaths in 7 years. For patients with a driver alteration, improved OS was observed among those treated with (n = 575) vs not treated with (n = 560) targeted therapies (median, 18.6 months [95% CI, 15.2-21.7] vs 11.4 months [95% CI, 9.7-12.5] from advanced diagnosis; P < .001). TMB (in mutations/Mb) was significantly higher among smokers vs nonsmokers (8.7 [IQR, 4.4-14.8] vs 2.6 [IQR, 1.7-5.2]; P < .001) and significantly lower among patients with vs without an alteration in EGFR (3.5 [IQR, 1.76-6.1] vs 7.8 [IQR, 3.5-13.9]; P < .001), ALK (2.1 [IQR, 0.9-4.0] vs 7.0 [IQR, 3.5-13.0]; P < .001), RET (4.6 [IQR, 1.7-8.7] vs 7.0 [IQR, 2.6-13.0]; P = .004), or ROS1 (4.0 [IQR, 1.2-9.6] vs 7.0 [IQR, 2.6-13.0]; P = .03). In patients treated with anti-PD-1/PD-L1 therapies (n = 1290, 31.7%), TMB of 20 or more was significantly associated with improved OS from therapy initiation (16.8 months [95% CI, 11.6-24.9] vs 8.5 months [95% CI, 7.6-9.7]; P < .001), longer time receiving therapy (7.8 months [95% CI, 5.5-11.1] vs 3.3 months [95% CI, 2.8-3.7]; P < .001), and increased clinical benefit rate (80.7% vs 56.7%; P < .001) vs TMB less than 20. Conclusions and Relevance: Among patients with NSCLC included in a longitudinal database of clinical data linked to CGP results from routine care, exploratory analyses replicated previously described associations between clinical and genomic characteristics, between driver mutations and response to targeted therapy, and between TMB and response to immunotherapy. These findings demonstrate the feasibility of creating a clinicogenomic database derived from routine clinical experience and provide support for further research and discovery evaluating this approach in oncology.

Monitoring prescribing patterns using regression and electronic health records.

BACKGROUND: It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are often not explicitly linked in the health record. Here we demonstrate the use of statistical methods together with data from the electronic health care record (EHR) to analyze prescribing patterns at an institution. METHODS: As a demonstration of our method, which is based on regression, we collect EHR data from outpatient notes and use a case/control study design to determine the medications that are associated with hypertension. We also use regression to determine which conditions are associated with a preferential use of one or more classes of hypertension agents. Finally, we compare our method to methods based on tabulation. RESULTS: Our results show that regression methods provide more reasonable and useful results than tabulation, and successfully distinguish between medications that treat hypertension and medications that do not. These methods also provide insight into in which circumstances certain drugs are preferred over others. CONCLUSIONS: Our method can be used by health care institutions to monitor physician prescribing patterns and ensure the appropriateness of treatment.

CANOES: detecting rare copy number variants from whole exome sequencing data.

We present CANOES, an algorithm for the detection of rare copy number variants from exome sequencing data. CANOES models read counts using a negative binomial distribution and estimates variance of the read counts using a regression-based approach based on selected reference samples in a given dataset. We test CANOES on a family-based exome sequencing dataset, and show that its sensitivity and specificity is comparable to that of XHMM. Moreover, the method is complementary to Gaussian approximation-based methods (e.g. XHMM or CoNIFER). When CANOES is used in combination with these methods, it will be possible to produce high accuracy calls, as demonstrated by a much reduced and more realistic de novo rate in results from trio data.

SHaploseek is a sequencing-only, high-resolution method for comprehensive preimplantation genetic testing.

Recent advances in genomic technologies expand the scope and efficiency of preimplantation genetic testing (PGT). We previously developed Haploseek, a clinically-validated, variant-agnostic comprehensive PGT solution. Haploseek is based on microarray genotyping of the embryo's parents and relatives, combined with low-pass sequencing of the embryos. Here, to increase throughput and versatility, we aimed to develop a sequencing-only implementation of Haploseek. Accordingly, we developed SHaploseek, a universal PGT method to determine genome-wide haplotypes of each embryo based on low-pass (≤ 5x) sequencing of the parents and relative(s) along with ultra-low-pass (0.2-0.4x) sequencing of the embryos. We used SHaploseek to analyze five single lymphoblast cells and 31 embryos. We validated the genome-wide haplotype predictions against either bulk DNA, Haploseek, or, at focal genomic sites, PCR-based PGT results. SHaploseek achieved > 99% concordance with bulk DNA in two families from which single cells were derived from grown-up children. In embryos from 12 PGT families, all of SHaploseek's focal site haplotype predictions were concordant with clinical PCR-based PGT results. Genome-wide, there was > 99% median concordance between Haploseek and SHaploseek's haplotype predictions. Concordance remained high at all assayed sequencing depths ≥ 2x, as well as with only 1ng of parental DNA input. In subtelomeric regions, significantly more haplotype predictions were high-confidence in SHaploseek compared to Haploseek. In summary, SHaploseek constitutes a single-platform, accurate, and cost-effective comprehensive PGT solution.

Accounting for Delayed Entry in Analyses of Overall Survival in Clinico-Genomic Databases.

BACKGROUND: Clinico-genomic databases favor inclusion of long-term survivors, leading to potentially biased overall survival (OS) analyses. Risk set adjustments relying on the independent delayed entry assumption may mitigate this bias. We aimed to determine whether this assumption is satisfied in a dataset of patients with advanced non-small cell lung cancer (aNSCLC), and to give guidance for clinico-genomic OS analyses when the assumption is not satisfied. METHODS: We analyzed the association of timing of next-generation sequencing (NGS) testing with real-world OS (rwOS) in patient data from a United States-based nationwide longitudinal deidentified electronic health records-derived database. Estimates of rwOS using risk set adjustment were compared with estimates computed with respect to all patients, regardless of NGS testing. RESULTS: The independent delayed entry assumption was not satisfied in this database, and later sequencing had a negative association with the hazard of death after sequencing. In a model adjusted for relevant characteristics, each month delay in sequencing was associated with a 2% increase in the hazard of death. However, until the median survival time, estimates of OS using risk set adjustment are similar to estimates computed for all patients, regardless of NGS testing. CONCLUSIONS: rwOS analyses in clinico-genomic databases should assess the independent delayed entry assumption. Comparisons versus broader population may be useful to evaluate the rwOS differences between calculations using risk set adjustment and patient cohorts where the bias relates to overrepresentation of long survivors. IMPACT: This study illustrates practices that can increase the interpretability of findings from OS analyses in clinico-genomic databases.

Amivantamab compared with real-world therapies in patients with advanced non-small cell lung cancer harboring EGFR exon 20 insertion mutations who progressed after platinum-based chemotherapy.

BACKGROUND: In the single-arm CHRYSALIS study, amivantamab showed durable responses and manageable safety in patients with advanced non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) exon 20 insertion mutations (ex20ins) who progressed on prior platinum-based chemotherapy. External controls can provide context for interpreting amivantamab efficacy. METHODS: External controls were selected from three US-based databases (ConcertAI, COTA, and Flatiron). Key inclusion criteria were diagnosis of EGFR ex20ins advanced NSCLC, prior platinum-based chemotherapy, and performance status score ≤ 1. Duplicate external controls were identified using a tokenization procedure and removed, and adjustment for differences in baseline characteristics between amivantamab-treated and external control cohorts was achieved using propensity score weighting. RESULTS: Amivantamab-treated and pooled external control cohorts included 81 and 125 patients, respectively. Baseline characteristics were generally similar across cohorts, except more amivantamab-treated patients were Asian (56% vs 13%). Most common therapies received by external controls were non-platinum-based chemotherapy (25.1%), immuno-oncology therapies (24.2%), EGFR tyrosine kinase inhibitors (16.3%), and platinum-based chemotherapy (16.3%). Overall response rate was 40% among amivantamab-treated patients and 16% among external controls. Amivantamab-treated patients had longer progression-free survival (median 8.3 vs 2.9 months; hazard ratio [HR; 95% CI]: 0.47 [0.34-0.65]), time to next therapy (median 14.8 vs 4.8 months; HR [95% CI]: 0.40 [0.28-0.57]), and overall survival (median 22.8 vs 12.8 months; HR [95% CI]: 0.49 [0.31-0.77]) than external controls. Results were consistent in sensitivity analyses comparing each external control dataset against the amivantamab-treated group separately. CONCLUSION: Among post-platinum patients with EGFR ex20ins advanced NSCLC, those treated with amivantamab had improved outcomes, including 10-month longer overall survival, versus external controls.

Utility of polygenic embryo screening for disease depends on the selection strategy.

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

Comparative clinical outcomes for patients with advanced NSCLC harboring EGFR exon 20 insertion mutations and common EGFR mutations.

INTRODUCTION: Real-world clinical outcomes in patients with advanced NSCLC harboring EGFR exon 20 insertion (exon20ins) mutations have not been extensively studied. We conducted a retrospective cohort study to assess the clinical outcomes of EGFR exon20ins compared with common EGFR (cEGFR) mutations. METHODS: Adults with advanced NSCLC harboring any EGFR mutations in the NSCLC Flatiron registry (2011 through May 2020) were included. To compare the relative prognosis (prognostic value) of exon20ins vs cEGFR, real-world overall survival (rwOS) was the primary endpoint. Separately, to compare the relative response to tyrosine kinase inhibitor (TKI) treatment (predictive value), real-world progression-free survival (rwPFS) was the primary endpoint. RESULTS: For the prognostic value analysis, 3014 patients with EGFR mutant NSCLC (cEGFR, n = 2833; EGFR exon20ins, n = 181) were eligible. The median (95% CI) rwOS was 16.2 (11.04-19.38) months in the EGFR exon20ins cohort vs 25.5 (24.48-27.04) months in the cEGFR cohort (adjusted HR, 1.75 [1.45-2.13]; p < 0.0001); 5-year rwOS was 8% and 19%, respectively. For the predictive value analysis, 2825 patients received TKI treatment and were eligible (cEGFR, n = 2749; EGFR exon20ins, n = 76). The median (95% CI) rwPFS from start of the first TKI was 2.9 (2.14-3.91) months in the EGFR exon20ins cohort vs 10.5 (10.05-10.94) months in the cEGFR cohort (adjusted HR, 2.69 [2.05-3.54]; p < 0001). Among patients with EGFR exon20ins, the most common prescribed first-line therapy was platinum-based chemotherapy (61.3%) followed by EGFR TKIs (21.5%); second-line treatments were varied, with no clear standard of care. CONCLUSIONS: Patients with EGFR exon20ins have poor prognosis and receive little benefit from EGFR TKI treatment. More effective therapies are needed in this difficult-to-treat population.

Single cell RNA-Seq reveals pre-cDCs fate determined by transcription factor combinatorial dose.

BACKGROUND: Classic dendritic cells (cDCs) play a central role in the immune system by processing and presenting antigens to activate T cells, and consist of two major subsets: CD141+ cDC (cDC1) and CD1c+ cDC (cDC2). A population of migratory precursor cells, the pre-cDCs, is the immediate precursors to both cDC subsets. Previous studies showed that there were two pre-committed pre-cDC subpopulations. However, the key molecular drivers of pre-commitment in human pre-cDCs were not investigated. RESULTS: To identify the key molecular drivers for pre-commitment in human pre-cDCs, we performed single cell RNA sequencing (RNA-Seq) of two cDC subsets and pre-cDCs, and bulk RNA-Seq of pre-cDCs and cDCs from human peripheral blood. We found that pre-DC subpopulations cannot be separated by either variable genes within pre-cDCs or differentially expressed genes between cDC1 and cDC2. In contrast, they were separated by 16 transcription factors that are themselves differentially expressed or have regulated targets enriched in the differentially expressed genes between bulk cDC1 and cDC2, with one subpopulation close to cDC1 and the other close to cDC2. More importantly, these two pre-cDC sub-populations are correlated with ratio of IRF8 to IRF4 expression level more than their individual expression level. We also verified these findings using three recently published datasets. CONCLUSIONS: In this study, we demonstrate that single cell transcriptome profiling can reveal pre-cDCs differentiation map, and our results suggest the concept that combinatorial dose of transcription factors determines cell differentiation fate.

Modeling motor learning using heteroskedastic functional principal components analysis.

We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across subjects and covariate values, modeling the variance of these scores provides a flexible and interpretable way to explore factors that affect the variability of functional data. Our work is motivated by a novel dataset from an experiment assessing upper extremity motor control, and quantifies the reduction in motion variance associated with skill learning.

Reduced survival in adult cystic fibrosis despite attenuated lung function decline.

BACKGROUND: There is limited data on disease progression and survival in adult diagnosis cystic fibrosis (CF). This study evaluates change of lung function over time and rates of death/lung transplant in adult diagnosis CF. METHODS: The CF Foundation Patient Registry was reviewed for patients diagnosed 1993-2003. Rate of FEV1 decline was calculated up to 2010 for age groups 6-11, 12-17, and 18 and above. Kaplan Meier method was used for 10 and 15year survival rate calculations for patients diagnosed as adults. Cox Proportional hazards models using predictors affecting disease progression and survival without transplant were run. RESULTS: Between 1993 and 2003, 11,884 patients were diagnosed with CF, of which 2848 were ages 6 and older. Annual rate of change of FEV1% predicted over 5years differed by diagnosis age group: -1.42% per year for ages 6-11, -2.04% for ages 12-17 and -1.13% for ages 18-65 (p<0.0001). Pseudomonas aeruginosa infection was associated with faster rates of lung function decline in all age groups. Survival without transplant for CF patients diagnosed at ≥18years were 76% and 65% by 10 and 15years, respectively. Of adults with FEV1 of >70% predicted at diagnosis, 95% were alive without transplant at 10years, whereas of those with FEV1<40% predicted at diagnosis, 31% were alive without transplant at 10years. CONCLUSIONS: Lung function declines at a slower rate in adult diagnosis CF. However, particularly in those with low lung function at diagnosis, rates of death or transplant in adult diagnosis CF after 10 and 15years is not negligible.

Using Rich Data on Comorbidities in Case-Control Study Design with Electronic Health Record Data Improves Control of Confounding in the Detection of Adverse Drug Reactions.

Recent research has suggested that the case-control study design, unlike the self-controlled study design, performs poorly in controlling confounding in the detection of adverse drug reactions (ADRs) from administrative claims and electronic health record (EHR) data, resulting in biased estimates of the causal effects of drugs on health outcomes of interest (HOI) and inaccurate confidence intervals. Here we show that using rich data on comorbidities and automatic variable selection strategies for selecting confounders can better control confounding within a case-control study design and provide a more solid basis for inference regarding the causal effects of drugs on HOIs. Four HOIs are examined: acute kidney injury, acute liver injury, acute myocardial infarction and gastrointestinal ulcer hospitalization. For each of these HOIs we use a previously published reference set of positive and negative control drugs to evaluate the performance of our methods. Our methods have AUCs that are often substantially higher than the AUCs of a baseline method that only uses demographic characteristics for confounding control. Our methods also give confidence intervals for causal effect parameters that cover the expected no effect value substantially more often than this baseline method. The case-control study design, unlike the self-controlled study design, can be used in the fairly typical setting of EHR databases without longitudinal information on patients. With our variable selection method, these databases can be more effectively used for the detection of ADRs.