Your search: "author:"Ziv, Elad""
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Potential public health impact of new tuberculosis vaccines.
Ziv, Elad;
Daley, Charles L;
Blower, Sally
Developing effective tuberculosis (TB) vaccines is a high priority. We use mathematical models to predict the potential public health impact of new TB vaccines in high-incidence countries. We show that preexposure vaccines would be almost twice as effective as postexposure vaccines in reducing the number of new infections. Postexposure vaccines would initially have a substantially greater impact, compared to preexposure vaccines, on reducing the number of new cases of disease. However, the effectiveness of postexposure vaccines would diminish over time, whereas the effectiveness of preexposure vaccines would increase. Thus, after 20 to 30 years, post- or preexposure vaccination campaigns would be almost equally effective in terms of cumulative TB cases prevented. Even widely deployed and highly effective (50%-90% efficacy) pre- or postexposure vaccines would only be able to reduce the number of TB cases by one third. We discuss the health policy implications of our analyses.
Categorization of humans in biomedical research: genes, race and disease.
Risch, Neil;
Burchard, Esteban;
Tang, Hua
A debate has arisen regarding the validity of racial/ethnic categories for biomedical and genetic research. An epidemiologic perspective on the issue of human categorization in biomedical and genetic research strongly supports the continued use of self-identified race and ethnicity.
High mammographic density in women of Ashkenazi Jewish descent
Caswell, Jennifer L;
Kerlikowske, Karla;
Shepherd, John A;
Cummings, Steven R;
Hu, Donglei;
Huntsman, Scott;
Ziv, Elad
Abstract Introduction Percent mammographic density (PMD) adjusted for age and body mass index is one of the strongest risk factors for breast cancer and is known to be approximately 60% heritable. Here we report a finding of an association between genetic ancestry and adjusted PMD. Methods We selected self-identified Caucasian women in the California Pacific Medical Center Research Institute Cohort whose screening mammograms placed them in the top or bottom quintiles of age-adjusted and body mass index-adjusted PMD. Our final dataset included 474 women with the highest adjusted PMD and 469 with the lowest genotyped on the Illumina 1 M platform. Principal component analysis (PCA) and identity-by-descent analyses allowed us to infer the women's genetic ancestry and correlate it with adjusted PMD. Results Women of Ashkenazi Jewish ancestry, as defined by the first principal component of PCA and identity-by-descent analyses, represented approximately 15% of the sample. Ashkenazi Jewish ancestry, defined by the first principal component of PCA, was associated with higher adjusted PMD (P = 0.004). Using multivariate regression to adjust for epidemiologic factors associated with PMD, including age at parity and use of postmenopausal hormone therapy, did not attenuate the association. Conclusions Women of Ashkenazi Jewish ancestry, based on genetic analysis, are more likely to have high age-adjusted and body mass index-adjusted PMD. Ashkenazi Jews may have a unique set of genetic variants or environmental risk factors that increase mammographic density.
Cummings, Steven;
Caswell, JL;
Shepherd, JA;
Cummings, SR;
Hu, D;
Huntsman, S
Introduction: Percent mammographic density (PMD) adjusted for age and body mass index is one of the strongest risk factors for breast cancer and is known to be approximately 60% heritable. Here we report a finding of an association between genetic ancestry
Response to comment on: Torres-Mejía et al. Moderate-intensity physical activity ameliorates the breast cancer risk in diabetic women. Diabetes care 2012;35:2500-2502
Torres-Mejía, G;
Ortega-Olvera, C;
Martínez-Matsushita, L;
Lazcano-Ponce, E;
Angeles-Llerenas, A
PCA-correlated SNPs for structure identification in worldwide human populations.
Paschou, Peristera;
Burchard, Esteban G;
Choudhry, Shweta;
Rodriguez-Cintron, William;
Mahoney, Michael W;
Drineas, Petros
Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.
The landscape of host genetic factors involved in infection to common viruses and SARS-CoV-2.
Kachuri, Linda;
Francis, Stephen S;
Morrison, Maike;
Bossé, Yohan;
Cavazos, Taylor B;
Rashkin, Sara R;
Witte, John S
Introduction:Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and informs public health interventions. Methods:We conducted a comprehensive study linking germline genetic variation and gene expression with antibody response to 28 antigens for 16 viruses using serological data from 7924 participants in the UK Biobank cohort. Using test results from 2010 UK Biobank subjects, we also investigated genetic determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Results:Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. Genome-wide association analyses discovered 7 novel genetic loci associated with viral antibody response (P<5.0×10-8), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for Merkel cell polyomavirus (MCV), as well as CXCR5 (11q23.3) and TBKBP1 (17q21.32) for human herpesvirus 7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR: P=5.0×10-15 (MCV), NTN5: P=1.1×10-9 (BKV), and P2RY13: P=1.1×10-8 (Epstein-Barr virus nuclear antigen). We also demonstrated pleiotropy between viral response genes and complex diseases, such as C4A expression in varicella zoster virus and schizophrenia. Finally, our analyses of SARS-CoV-2 revealed the first genome-wide significant infection susceptibility signal in EHF, an epithelial-specific transcriptional repressor implicated in airway disease. Targeted analyses of expression quantitative trait loci suggest a possible role for tissue-specific ACE2 expression in modifying SARS-CoV-2 susceptibility. Conclusions:Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants of host-virus interaction. Our results may have implications for complex disease etiology and COVID-19.
The landscape of host genetic factors involved in immune response to common viral infections.
Morrison, Maike L;
Wendt, George A;
Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities.
We conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort.
Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr virus (EBV), Varicella zoster virus (VZV), human herpesvirus 7, (HHV7), and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response (P < 5.0 × 10-8), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for MCV, and CXCR5 (11q23.3) and TBKBP1 (17q21.32) for HHV7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR: P = 5.0 × 10-15 (MCV), NTN5: P = 1.1 × 10-9 (BKV), and P2RY13: P = 1.1 × 10-8 EBV nuclear antigen. We also demonstrated pleiotropy between viral response genes and complex diseases, from autoimmune disorders to cancer to neurodegenerative and psychiatric conditions.
Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants beyond the HLA that contribute to host-virus interaction.
Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction.
Graff, Rebecca E;
Smith-Byrne, Karl;
Meyers, Travis J;
Witte, John S;
Johansson, Mattias
Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores.
Pharmacogenetic testing affects choice of therapy among women considering tamoxifen treatment
Lorizio, Wendy;
Rugo, Hope;
Beattie, Mary S;
Tchu, Simone;
Melese, Teri;
Melisko, Michelle;
Wu, Alan HB;
Lawrence, H Jeffrey;
Nikoloff, Michele;
Abstract Background Pharmacogenetic testing holds major promise in allowing physicians to tailor therapy to patients based on genotype. However, there is little data on the impact of pharmacogenetic test results on patient and clinician choice of therapy. CYP2D6 testing among tamoxifen users offers a potential test case of the use of pharmacogenetic testing in the clinic. We evaluated the effect of CYP2D6 testing in clinical practice to determine whether genotype results affected choice of hormone therapy in a prospective cohort study. Methods Women planning to take or currently taking tamoxifen were considered eligible. Participants were enrolled in an informational session that reviewed the results of studies of CYP2D6 genotype on breast cancer recurrence. CYP2D6 genotyping was offered to participants using the AmpliChip CYP450 Test. Women were classified as either poor, intermediate, extensive or ultra-rapid metabolizers. Results were provided to clinicians without specific treatment recommendations. Follow-up was performed with a structured phone interview 3 to 6 months after testing to evaluate changes in medication. Results A total of 245 women were tested and 235 completed the follow-up survey. Six of 13 (46%) women classified as poor metabolizers reported changing treatment compared with 11 of 218 (5%) classified as intermediate, extensive or ultra-rapid metabolizers (P < 0.001). There was no difference in treatment choices between women classified as intermediate and extensive metabolizers. In multi-variate models that adjusted for age, race/ethnicity, educational status, method of referral into the study, prior knowledge of CYP2D6 testing, the patients' CYP2D6 genotype was the only significant factor that predicted a change in therapy (odds ratio 22.8; 95% confidence interval 5.2 to 98.8). Genetic testing did not affect use of co-medications that interact with CYP2D6. Conclusions CYP2D6 genotype testing led to changes in therapy among poor metabolizers, even in the absence of definitive data that an alternative medicine improved outcomes. Pharmacogenetic testing can affect choice of therapy, even in the absence of definitive data on clinical impact.