scholarly journals Genetics of Factor VIII Inhibitor Development in Hemophilia Patients: Novel Statistical Approaches in the PATH Study

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1199-1199
Author(s):  
Vincent P. Diego ◽  
Marcio A. Almeida ◽  
Bernadette W. Luu ◽  
Karin Haack ◽  
Meera B. Chitlur ◽  
...  
Keyword(s):  
Factor Viii ◽  
Covariance Matrix ◽  
Random Effects ◽  
Variance Component ◽  
Multiple Hypothesis Testing ◽  
Power Curve ◽  
Mutation Matrix ◽  
Equity Ownership ◽  
Patent Applications ◽  
Biotechnology Corporation

Abstract Here we apply state-of-the-art statistical genetic approaches toward investigating the genetic architecture of factor VIII (FVIII) inhibitor (FEI) development in Hemophilia A (HA). A total of 442 North American HA patients (237 Whites and 205 Blacks; 88% severely affected) enrolled in the PATH Study were: 1) ImmunoChip genotyped at ~167,000 single nucleotide polymorphisms (SNPs) in genes previously implicated in autoimmune disease risk; 2) Evaluated by DNA sequencing and assays for the recurrent intron (I)1 and I22 inversions to identify their causative F8 mutations; and 3) Tested with the Bethesda assay to determine their FEI status. The ImmunoChip genotypes were used to construct a genetic relationship matrix (GRM), denoted by K, following our previously published method,1 and the F8 sequence data along with results from the I1 and I22 inversion assays were used to construct a shared F8-mutation matrix, denoted by F. We analyzed a dichotomous FEI variable under the statistical genetic threshold/liability model (a probit regression in the fixed effects) in conjunction with a variance components model for the FEI liability phenotypic covariance matrix, denoted by P, to model potentially important random effects. For the latter, we specifically assumed independent additive genetic, F8-mutation, and residual environmental random effects. By the independence assumption, the covariance matrix is then decomposable as a sum of the additive genetic (Va), F8-mutation (Vf), and residual environmental (Ve) variances respectively structured by K, F, and the identity matrix I. The variance component model is given as: P = K*Va + F*Vf + I*Ve. Heritability, denoted by h2, is defined as the ratio of Va to the total phenotypic variance (Vp): h2 = Va / Vp. We can further speak of the total heritability given as: h2t = h2r + h2f + h2snp, where the subscripts t, r, f, and snp respectively denote total, residual additive genetic, F8-mutation-specific, and SNP heritabilities. Using eigenstructure methods,2 we can compute power under a simpler model in which Va and Vf are combined as a single variance component. We computed power to detect genetic association as measured by SNP-specific heritability for a set of 403 SNPs in or near 14 candidate immune response genes previously implicated in FEI risk. To account for multiple hypothesis testing, power was computed at the Bonferroni-adjusted significance level of 0.05/403 = 1.2 × 10-4. Under the simplified model, we computed the statistical power to detect causal SNPs for our sample and study design for the sample FEI prevalences, denoted by Kp, for Whites (22.5%) and Blacks (45%), across a range of total heritabilities, h2t = 15%, 35%, and 55%, where the lattermost total heritability was observed for FEI liability in the current study (Figure 1). It should be noted that because the liability heritability is known to be biased upward, we applied the Dempster-Lerner correction to both the total and SNP-specific heritabilities.3 Close inspection of Figure 1 reveals that varying h2t from 15% to 35% to 55% results in slight decreases in power due to the decreasing ratio of the SNP-specific heritability to the total heritability. However, as seen in all three panels, the more important determinant of power is clearly the FEI prevalence in that the power curve for a Kp of 45% is associated with greater power than the power curve for a Kp of 22.5% across the range of total heritabilities examined. As seen in Figure 1, we have adequate power to detect SNP heritabilities as low as 5% and 6%, respectively, for a Kp of 45% and 22.5%. As noted above, we observed a FEI liability total heritability of 55% consisting of a 47% residual additive genetic heritability (p = 0.019) and 8% F8-mutation specific heritability (p = 0.005). This is the first study to use a GRM based on genotype data and a shared causal F8 mutation matrix to model additive genetic and F8-mutation specific effects.Almeida M, Peralta J, Farook V, …, Blangero J. Pedigree-based random effect tests to screen gene pathways. BMC Proc. 2014; 8(Suppl 1 Genetic Analysis Workshop): S100.Blangero J, Diego VP, Dyer T, …, Göring H. A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genetics. 2013; 81: 1-31.Glahn D, Williams J, McKay D, …, Blangero J. Discovering schizophrenia endophenotypes in randomly ascertained pedigrees. Biol Psychiatry. 2015; 77(1): 75-83. Disclosures Chitlur: Baxter, Bayer, Biogen Idec, and Pfizer: Honoraria; Novo Nordisk Inc: Consultancy. Dinh:Haplomics Biotechnology Corporation: Employment, Equity Ownership. Howard:Haplomics Biotechnology Corporation: Equity Ownership, Other: Chief Scientific Officer, Patents & Royalties: Patent applications and provisional patent applications ; CSL Behring: Research Funding.

scholarly journals The Role of Class II Human Leukocyte Antigens (cII-HLAs) in Determining the Immunogenic Potential of Therapeutic Factor VIII Proteins in Hemophilia Patients: The "Gate Keeper" Hypothesis

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 5022-5022 ◽  
Author(s):  
Vincent P. Diego ◽  
Bernadette W. Luu ◽  
Marco Hofmann ◽  
Marcio A. Almeida ◽  
Jose Antonio G. Hernandez ◽  
...  
Keyword(s):  
Immune Responses ◽  
Factor Viii ◽  
Confidence Intervals ◽  
Null Hypothesis ◽  
Research Funding ◽  
Population Level ◽  
Interaction Terms ◽  
Equity Ownership ◽  
Patent Applications ◽  
Biotechnology Corporation

Abstract The development of neutralizing antibodies-termed "inhibitors"-to infused therapeutic (t) factor VIII proteins (tFVIIIs) is the most serious obstacle to effective treatment of bleeding in Hemophilia A (HA) patients. As clinically significant FVIII immune responses are only initiated if dendritic cell (DC) cII-HLAs can present foreign tFVIII-derived peptides to naïve FVIII-specific T cells, we posit the "Gate Keeper" hypothesis in which the limiting determinant of inhibitor formation are patients' cII-HLA repertoires with the majority being individually distinct and each contributing slightly to the vast population level diversity of cII-HLAs. While cII-HLAs are critical at the cellular level for initiating immune responses, conflicting results from population studies have led some to describe their encoding HLA-II structural genes as weak determinants of inhibitor causation. Our main objective here is to test a hypothesis that gets at the heart of this disconnect between molecular-based expectations and population-level data by analyzing cII-HLA peptidomic data from DC-protein processing and presentation assays (PPPAs). The chief variable of DC-PPPA data is the peptide count, which we assume to be directly proportional to immunogenic potential (IP). Our working model is that inhibitor formation requires at minimum, in its initial stages, a complex between cII-HLAs and specific tFVIII-derived peptides. A testable null hypothesis under this thinking posits that a given cII-HLA allotype will have the same IP when exposed to several tFVIIIs. To test this hypothesis, we first performed model selection to determine the best set of predictor allotypes. To analyze the data, we employed a log-linear model where the peptide count is the dependent variable and allotype is a categorical independent variable consisting of 29 levels for 29 allotypes (8 DP, 10 DQ, and 11 DR allotypes). We used elastic net regression (ENR) to select the best set of allotype levels thus giving the best overall model consisting now of only four DR allotypes (Table 1). We then performed interaction analysis under the best-selected allotypes model in which we introduced as additional predictor variables, a tFVIII categorical variable consisting of five levels for five different tFVIIIs, namely full length (FL)-recombinant (r) FVIII (FL-rFVIII) ± von Willebrand Factor (VWF), B domain truncated (BDT)-rFVIII ± VWF, and plasma derived (pd) FVIII (pdFVIII) + VWF, and 12 interaction terms for the (4 - 1) × (5 - 1) possible interactions between the cII-HLA allotype and tFVIII variables. We found significant cII-HLA allotype × tFVIII interactions (Table 2). To get at the specific null hypothesis of interest, we examined within-allotype risk ratios (RRs) and their appropriately adjusted confidence intervals (CIs).1-4 It can be shown that an 84% CI is sufficient to achieve a significance level of α = 0.05 for the CI difference.2-4 Although there are 12 total interaction terms, per allotype there are only three possible CI comparisons on using the interaction term with the highest RR as a fixed reference. On constructing the adjusted CIs and correcting for multiple hypothesis testing,2 we found that two comparisons in Table 2 corresponded to significantly different RRs. We determined statistical power to detect a CI difference.1,3 As seen in Table 2, our study was extremely underpowered, which may explain why only two significant differences were found. Thus, at least for the two comparisons showing significant difference, we have refuted the null hypothesis of no difference across tFVIIIs for a given allotype, and have affirmed our working model that specific combinations of cII-HLAs and tFVIII-derived peptides are the triggering factor in inhibitor development.Schenker N, Gentleman J. On judging the significance of differences by examining the overlap between confidence intervals. Am Statistician. 2001; 55(3): 182-6.Julious S. Using confidence intervals around individual means to assess statistical significance between two means. Pharmaceut Statist. 2004; 3: 217-22.Maghsoodloo S, Huang C-Y. Comparing the overlapping of two independent confidence intervals with a single confidence interval for two normal population parameters. J Statist Plan & Infer. 2010; 140: 3295-305.Knol M, Pestman W, Grobbee D. The (mis)use of overlap of confidence intervals to assess effect modification. Eur J Epidemiol. 2011; 26(4): 253-4. Disclosures Hofmann: CSL Behring: Employment. Dinh:Haplomics Biotechnology Corporation: Employment, Equity Ownership. Escobar:Pfizer: Research Funding; Bayer, CSL Behring, Genentech, Hemabiologics, Kedrion, Novo Nordisk, Octapharma, Pfizer and Shire: Consultancy. Maraskovsky:CSL Behring: Employment. Howard:CSL Behring: Research Funding; Haplomics Biotechnology Corporation: Equity Ownership, Other: Chief Scientific Officer, Patents & Royalties: Patent applications and provisional patent applications .

A new Bayesian approach for QTL mapping of family data

2021 ◽  
Author(s):  
Daiane Aparecida Zuanetti ◽  
Luis Aparecido Milan
Keyword(s):  
Qtl Mapping ◽  
Random Effects ◽  
Bayesian Approach ◽  
Variance Component ◽  
Mendelian Inheritance ◽  
Family Data ◽  
Data Sets ◽  
Mendelian Segregation ◽  
Gaw17 Data ◽  
The Bayesian Approach

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs’ effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents’ genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs’ position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.

scholarly journals Estimation of the covariance matrix of random effects in longitudinal studies

2007 ◽  
Vol 35 (6) ◽  
pp. 2795-2814 ◽  
Author(s):  
Yan Sun ◽  
Wenyang Zhang ◽  
Howell Tong
Keyword(s):  
Longitudinal Studies ◽  
Covariance Matrix ◽  
Random Effects

scholarly journals Factor VIII Use in the Treatment of Breakthrough Bleeds in Hemophilia A Patients without Inhibitors on Emicizumab Prophylaxis: The Phase 3 HAVEN 3 Study Experience

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2395-2395
Author(s):  
Michael Callaghan ◽  
Benjamin Trzaskoma ◽  
Richard H. Ko ◽  
Lucy Lee ◽  
Anisha M. Patel ◽  
...  
Keyword(s):  
Factor Viii ◽  
Exposure Time ◽  
Cumulative Dose ◽  
Research Funding ◽  
Hemophilia A ◽  
Phase 3 ◽  
Breakthrough Bleeding ◽  
On Demand ◽  
Equity Ownership ◽  
The Individual

Introduction HAVEN 3 was a phase 3 study investigating the use of emicizumab as prophylaxis in adult and adolescent (≥12 years old) persons with hemophilia A (PwHA) without factor VIII (FVIII) inhibitors (NCT02847637; Mahlangu et al. 2018). HAVEN 3 demonstrated that emicizumab prophylaxis once weekly or every two weeks was safe and highly effective in bleed prevention. The primary analysis of HAVEN 3 included an intrapatient comparison of 48 participants who received FVIII prophylaxis in a non-interventional study (NIS) prior to enrollment in HAVEN 3. Compared with emicizumab prophylaxis during the HAVEN 3 study, emicizumab prophylaxis resulted in an annualized bleed rate that was 68% lower than the rate with previous FVIII prophylaxis (1.5 vs 4.8, p<0.001). No dosing guidance was provided regarding the use of on-demand FVIII in HAVEN 3, and investigators prescribed FVIII at their own discretion. In this subsequent analysis, we characterize the dose and frequency of replacement FVIII used for the treatment of breakthrough bleeding in these 48 participants. Methods The primary comparisons in our analyses are focused on on-demand FVIII use for breakthrough bleeding while participants were on FVIII prophylaxis during the NIS versus its use while on emicizumab prophylaxis during HAVEN 3. Any use of on demand FVIII other than to manage breakthrough bleeding (e.g. prior to activity) was not included in our analyses. Given that, collectively, the total exposure time to emicizumab during HAVEN 3 was more than twice the exposure time to FVIII prophylaxis during the NIS (75.8 vs 28.6 years respectively), any treatment comparisons are drawn on an annualized basis. Annualized on-demand FVIII use was calculated by dividing by the number of days in the efficacy period and multiplying the resulting daily consumption by 365.25 days. The number of infusions and cumulative doses of on-demand FVIII use are described at the participant level as well as at the individual bleed level and are presented descriptively for both the NIS and HAVEN 3 exposure periods. No formal statistical inferences (i.e. calculation of p-values) have been conducted. All analyses were based on an October 2018 data cutoff. Results A total of 48 participants who were treated with FVIII prophylaxis during the NIS were then treated with emicizumab prophylaxis during HAVEN 3 and thus make up the total cohort for our analyses. Annualized infusion rates of on-demand FVIII per participant and cumulative doses of on-demand FVIII (in international units [IU] per kilogram) per participant were higher during the FVIII prophylaxis period when compared with the emicizumab exposure period (mean 15.3 vs 7.2; median 3.6 vs 0.6 annual infusions per participant and mean 602.4 IU/kg vs 209.0 IU/kg; median 75.5 IU/kg vs 19.1 IU/kg, respectively). At the individual bleed level, FVIII infusions and total cumulative dose suggested that participants were administered a similar amount of medication to treat bleeds during both the NIS and HAVEN 3 study periods: median number of infusions per bleed were 1.0 (interquartile range [IQR]=1.0) versus 2.0 (IQR=3.0) and median cumulative doses were 43.5 (IQR=35.1) versus 50.0 (IQR=72.7) IU/kg, respectively (Table 1). Conclusions This analysis revealed a lower annualized infusion rate and a correspondingly lower annualized cumulative dose of FVIII for treatment of breakthrough bleeds during emicizumab prophylaxis compared with FVIII prophylaxis. At the individual bleed level, the amount of on-demand FVIII used per bleeding episode was comparable between NIS and HAVEN 3 exposure periods. Thus, based on this single analysis, it appears that patients received less on-demand FVIII during emicizumab prophylaxis compared with FVIII prophylaxis, as a result of overall reduction of bleed frequency, while the treatment of individual bleeds appeared similar regardless of the prophylaxis therapy administered. Disclosures Callaghan: Octapharma: Consultancy; Novonordisk: Consultancy, Speakers Bureau; Global Blood Therapeutics: Consultancy; Sanofi: Consultancy; Takeda: Consultancy, Research Funding; Bayer: Consultancy, Speakers Bureau; Alnylum: Equity Ownership; Biomarin, Bioverativ, Grifols, Kedrion, Pfizer, Roche/Genentech, Shire, and Spark Therapeutics: Consultancy; Roche/Genentech: Speakers Bureau; Shire/Takeda: Speakers Bureau; Pfizer: Research Funding; Roche: Research Funding. Trzaskoma:Genentech: Employment, Equity Ownership. Ko:Genentech, Inc.: Employment. Lee:Genentech, Inc.: Employment. Patel:Genentech: Employment; Roche/Genentech: Equity Ownership. Tzeng:Genentech, Inc.: Employment. Shah:Genentech: Employment. Chang:Genentech, Inc.: Employment; Genentech/Roche: Equity Ownership. Niggli:F. Hoffmann-La Roche Ltd: Employment. Dhalluin:F. Hoffmann-La Roche Ltd: Employment. Mahlangu:Sanofi: Research Funding; Pfizer: Research Funding; Novartis: Research Funding; Biomarin: Research Funding; Spark: Consultancy, Honoraria, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novo Nordisk: Consultancy, Honoraria, Research Funding, Speakers Bureau; Catalyst Biosciences: Consultancy, Honoraria, Research Funding, Speakers Bureau; CSL Behring: Consultancy, Honoraria, Research Funding, Speakers Bureau; Baxalta: Consultancy, Honoraria, Research Funding, Speakers Bureau; Unique: Research Funding.

Estimators of Random Effects Variance Components in Meta-Analysis

2000 ◽  
Vol 25 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Lynn Friedman
Keyword(s):  
Closed Form ◽  
Random Effects ◽  
Quadratic Forms ◽  
Relative Efficiency ◽  
Variance Component ◽  
Linear Models ◽  
Random Effect ◽  
Linear Functions ◽  
Effect Sizes ◽  
Random Effects Model

In meta-analyses, groups of study effect sizes often do not fit the model of a single population with only sampling, or estimation, variance differentiating the estimates. If the effect sizes in a group of studies are not homogeneous, a random effects model should be calculated, and a variance component for the random effect estimated. This estimate can be made in several ways, but two closed form estimators are in common use. The comparative efficiency of the two is the focus of this report. We show here that these estimators vary in relative efficiency with the actual size of the random effects model variance component. The latter depends on the study effect sizes. The closed form estimators are linear functions of quadratic forms whose moments can be calculated according to a well-known theorem in linear models. We use this theorem to derive the variances of the estimators, and show that one of them is smaller when the random effects model variance is near zero; however, the variance of the other is smaller when the model variance is larger. This leads to conclusions about their relative efficiency.

ANOVA models with random effects: an approach via symmetry

1986 ◽  
Vol 23 (A) ◽  
pp. 355-368 ◽  
Author(s):  
T. P. Speed
Keyword(s):  
Covariance Matrix ◽  
Random Effects ◽  
Spectral Decomposition ◽  
Linear Models ◽  
Random Variables ◽  
Components Of Variance

The standard ANOVA models with random effects for multi-indexed arrays of random variables with an arbitrary nesting structure on the indices are considered from the viewpoint of symmetry. It is found that the covariance matrix of such an array has sufficient symmetry to permit viewing the usual components of variance as a generalised spectrum and the linear models of random effects as a generalised spectral decomposition.

scholarly journals Clonal Analysis of Human Bone Marrow CD34+ Cells Edited By BCL11A-Targeting Zinc Finger Nucleases Reveals Clinically Relevant Levels of Fetal Globin Expression in Edited Erythroid Progeny

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3234-3234 ◽  
Author(s):  
Kai-Hsin Chang ◽  
Timothy Sullivan ◽  
Mei Liu ◽  
Xiao Yang ◽  
Chao Sun ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Zinc Finger ◽  
Zinc Finger Nucleases ◽  
Erythroid Cells ◽  
Coding Region ◽  
Employment Equity ◽  
Cd34 Cells ◽  
F Cells ◽  
Equity Ownership ◽  
Patent Applications

Abstract Sickle cell disease (SCD) is one of the most common inherited blood disorders and is caused by a mutation at the adult beta globin gene resulting in substitution of valine for glutamic acid at position 6 in the encoded protein. While SCD can be cured by hematopoietic stem cell transplant (HSCT), complete donor chimerism is not required to achieve clinical benefits. Stable mixed chimerism of 10-15% in bone marrow or peripheral blood nucleated cells with >70% donor-derived RBCs has been reported to achieve transfusion independence and a symptom-free state in a SCD patient. It has also been proposed that SCD can be treated by reactivating developmentally silenced fetal gamma globin to form fetal hemoglobin (alpha2gamma2, HbF), which inhibits polymerization of HbS. The effect of HbF is predicted to be maximal when HbF content per cell exceeds 10 pg (~30% of total Hb). Furthermore, pathology is prevented when protective F cells (>30% HbF per cell) constitute >70% of total RBCs. We hypothesize that in a gene therapy setting, if >15% of SCD patients' autologous HSCs are programmed to produce protective F cells during erythropoiesis, it will translate into >70% protective F cells in circulation and provide significant alleviation of clinical symptoms. Genome wide association studies have identified BCL11A as a major modifier of HbF levels. Subsequent studies have shown that BCL11A plays a critical role in the fetal to adult globin developmental switch and in repressing fetal globin expression in adult erythroid cells. Conditional inactivation of BCL11A in adult erythroid cells leads to high levels of pan-cellular fetal globin expression and correction of hematologic and pathologic defects in a humanized SCD mouse model. Previously, we have reported that zinc finger nucleases (ZFNs) targeting BCL11A either in the coding region or the GATAA motif in the erythroid-specific enhancer efficiently disrupt the BCL11A locus in human primary CD34+ cells following electroporation of ZFN-encoding mRNA. Elevated fetal globin expression in bulk erythroid cultures was observed following disruption. To determine what percentage of HSPCs have been modified and whether the HbF/F cell content has reached the hypothesized therapeutic level, we analyzed erythroid cells clonally derived from ZFN-transfected CD34+ cells. Genotype of each clonal culture was determined by deep sequencing and globin production was analyzed by a highly sensitive UPLC method. We found that up to 80% of the BFU-Es had both BCL11A alleles edited, half of which had KO/KO alleles (either out of frame mutations for coding region or elimination of the GATAA motif in the enhancer). BCL11A coding KO/KO cells expressed on average 79.1% ± 12.2% fetal globin (Mean ± SD) whereas GATAA motif enhancer region KO/KO cells expressed approximately 48.4% ± 14.1% fetal globin, in comparison with 14.5% ± 9.6% in WT/WT cells . These levels of fetal globin should be sufficiently high to confer protection against HbS polymerization in sickle cells. WT/KO cells in both coding and enhancer editing experiments showed an intermediate phenotype with fetal globin averaging 26.9%± 9.9% and 25.79% ± 12.6%, respectively. Interestingly, when background (WT/WT) fetal globin level was subtracted, the fetal globin levels in WT/KO cells are comparable to those observed in patients with BCL11A haploinsufficiency, which average 14.6%± 10.3%. Together, our data demonstrate that genome editing of BCL11A using highly efficient ZFNs can lead to clinically relevant levels of fetal globin expression in KO/KO erythroid cells. If the frequency of KO/KO BFU-Es we observed in vitro reflects the frequency of KO/KO HSCs in bone marrow after autologous transplantation, genome editing of BCL11A has the potential to provide significant clinical benefit for patients with SCD. Disclosures Chang: Biogen: Employment, Equity Ownership. Sullivan:Biogen: Employment, Equity Ownership. Liu:Biogen: Employment, Equity Ownership. Yang:Biogen: Employment, Equity Ownership. Sun:Biogen: Employment, Equity Ownership. Vieira:Biogen: Employment, Equity Ownership. Zhang:Biogen: Employment. Hong:Biogen: Employment, Equity Ownership. Chen:Biogen: Employment, Equity Ownership. Smith:Biogen: Employment, Equity Ownership. Tan:Biogen: Employment, Equity Ownership. Reik:Sangamo BioSciences: Employment, Equity Ownership, Patents & Royalties: Patent applications have been filed based on this work. Urnov:Sangamo BioSciences: Employment, Equity Ownership, Patents & Royalties: Patent applications have been filed based on this work. Rebar:Sangamo BioSciences: Employment. Danos:Biogen: Employment, Equity Ownership. Jiang:Biogen: Employment, Equity Ownership.

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