The Experts below are selected from a list of 169965 Experts worldwide ranked by ideXlab platform
Jason D Boardman - One of the best experts on this subject based on the ideXlab platform.
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mortality selection in a Genetic Sample and implications for association studies
International Journal of Epidemiology, 2017Co-Authors: Benjamin W Domingue, Daniel W Belsky, Amal Harrati, Dalton Conley, David R Weir, Jason D BoardmanAbstract:Background Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in Genetic epidemiology. We tested the hypothesis that mortality selection may bias Genetic association estimates, using data from the US-based Health and Retirement Study (HRS). Methods We tested mortality selection into the HRS Genetic database by comparing HRS respondents who survive until Genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying Genetic associations (i.e. gene-by-cohort interactions). Results We observed mortality selection into the HRS Genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012. Conclusions Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of Genetic association studies, but the magnitude of confounding varies by trait.
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mortality selection in a Genetic Sample and implications for association studies
bioRxiv, 2016Co-Authors: Benjamin W Domingue, Daniel W Belsky, Amal Harrati, Dalton Conley, David R Weir, Jason D BoardmanAbstract:Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection---the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection---has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from Genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the Genetic Sample of the HRS. Mortality selection leads to modest drift in estimating time-varying Genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how Genetic association studies using HRS data can adjust for mortality selection from study entry to time of Genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in Genetically-informed Samples from other cohort studies.
Yuruo Zhang - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving Genetic relatedness test
In: Proceedings of the 3rd International Workshop on Genome Privacy and Security (GenoPri'16). International Workshop on Genome Privacy and Security: , 2016Co-Authors: Emiliano De Cristofaro, Kaitai Liang, Yuruo ZhangAbstract:An increasing number of individuals are turning to Direct-To-Consumer Genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. Direct-to-consumer companies like 23andme and Ancestry.com have started to offer popular ancestry and genealogy tests, with services allowing users to find unknown relatives and long-distant cousins. Naturally, access and possible dissemination of Genetic data prompts serious privacy concerns, thus motivating the need to design efficient primitives supporting private Genetic tests. In this paper, we present an effective protocol for privacy-preserving Genetic relatedness test, enabling a cloud server to run relatedness tests on input an encrypted Genetic database and a test facility’s encrypted Genetic Sample. We reduce the test to a data matching problem and perform it, “secretly”, using searchable encryption. Finally, performance evaluation for privacy-preserving hamming distance attests to the practicality of our proposals.
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privacy preserving Genetic relatedness test
arXiv: Cryptography and Security, 2016Co-Authors: Emiliano De Cristofaro, Kaitai Liang, Yuruo ZhangAbstract:An increasing number of individuals are turning to Direct-To-Consumer (DTC) Genetic testing to learn about their predisposition to diseases, traits, and/or ancestry. DTC companies like 23andme and Ancestry.com have started to offer popular and affordable ancestry and genealogy tests, with services allowing users to find unknown relatives and long-distant cousins. Naturally, access and possible dissemination of Genetic data prompts serious privacy concerns, thus motivating the need to design efficient primitives supporting private Genetic tests. In this paper, we present an effective protocol for privacy-preserving Genetic relatedness test (PPGRT), enabling a cloud server to run relatedness tests on input an encrypted Genetic database and a test facility's encrypted Genetic Sample. We reduce the test to a data matching problem and perform it, privately, using searchable encryption. Finally, a performance evaluation of hamming distance based PP-GRT attests to the practicality of our proposals.
Benjamin W Domingue - One of the best experts on this subject based on the ideXlab platform.
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mortality selection in a Genetic Sample and implications for association studies
International Journal of Epidemiology, 2017Co-Authors: Benjamin W Domingue, Daniel W Belsky, Amal Harrati, Dalton Conley, David R Weir, Jason D BoardmanAbstract:Background Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in Genetic epidemiology. We tested the hypothesis that mortality selection may bias Genetic association estimates, using data from the US-based Health and Retirement Study (HRS). Methods We tested mortality selection into the HRS Genetic database by comparing HRS respondents who survive until Genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying Genetic associations (i.e. gene-by-cohort interactions). Results We observed mortality selection into the HRS Genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012. Conclusions Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of Genetic association studies, but the magnitude of confounding varies by trait.
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mortality selection in a Genetic Sample and implications for association studies
bioRxiv, 2016Co-Authors: Benjamin W Domingue, Daniel W Belsky, Amal Harrati, Dalton Conley, David R Weir, Jason D BoardmanAbstract:Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection---the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection---has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from Genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the Genetic Sample of the HRS. Mortality selection leads to modest drift in estimating time-varying Genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how Genetic association studies using HRS data can adjust for mortality selection from study entry to time of Genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in Genetically-informed Samples from other cohort studies.
Patrick F Sullivan - One of the best experts on this subject based on the ideXlab platform.
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the association between family history and genomic burden with schizophrenia mortality a swedish population based register and Genetic Sample study
Translational Psychiatry, 2021Co-Authors: Kaarina Kowalec, Jie Song, Christina Dalman, Christina M Hultman, Henrik Larsson, Paul Lichtenstein, Patrick F SullivanAbstract:Individuals with schizophrenia (SCZ) have a 2-3-fold higher risk of mortality than the general population. Heritability of mortality in psychiatric disorders has been proposed; however, few have investigated SCZ family history and Genetic variation, with all-cause and specific causes of death. We aimed to identify correlates of SCZ mortality using Genetic epidemiological and Genetic modelling in two Samples: a Swedish national population Sample and a genotyped subSample. In the Swedish national population Sample followed from the first SCZ treatment contact until emigration, death or end of the follow-up, we investigated a standardised measure of SCZ family history. In a subgroup with comprehensive Genetic data, we investigated the impact of common and rare Genetic variation. Cox proportional hazards regression was used to estimate the association between various factors and mortality (all and specific causes). A total of 13727 SCZ cases fulfilled criteria for the population-based analyses (1268 deaths, 9.2%). The genomic subset contained 4991 cases (1353 deaths, 27.1%). Somatic mutations associated with clonal hematopoiesis with unknown drivers were associated with all-cause mortality (HR 1.77, 95% CI: 1.26-2.49). No other heritable measures were associated with all-cause mortality nor with any specific causes of death. Future studies in larger, comparable cohorts are warranted to further understand the association between hereditary measures and mortality in SCZ.
Bruno A Frazier - One of the best experts on this subject based on the ideXlab platform.
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an active microfluidic system packaging technology
Sensors and Actuators B-chemical, 2007Co-Authors: Kiho Han, Rachel D Mcconnell, Christopher J Easley, Joan M Bienvenue, Jerome P Ferrance, James P Landers, Bruno A FrazierAbstract:This paper presents the design, fabrication, and characterization of a microfluidic system interface (MSI) technology for integration of complex microfluidic systems containing multiple functionalities. The microfluidic system interface technology provided a simple method for realizing complex arrangements of on-chip/off-chip microfluidic interconnects, integrated microvalves for fluid control, and optical windows for on-chip optical processes. The microfluidic interconnects were designed to provide one-step plug-in fluid interconnection utilizing a post assembled o-ring to ensure a tight seal. The integrated microvalves were completed when the MSI was assembled atop of the microfluidic system. The microvalves have been designed for zero dead volume with a displaced channel volume of 24 nl in the closed state. The valve was pneumatically actuated up to a valve pressure of 450 kPa. The optical windows were designed to allow for analysis operations such as infrared polymerase chain reaction and conventional fluorescence detection. A microfluidic system for Genetic Sample preparation was used as the test vehicle to prove the usefulness of the MSI technology with respect to complex microfluidic systems containing multiple functionalities. The miniaturized Genetic Sample preparation system consisted of several functional compartments including cell purification, cell separation, cell lysis, solid phase DNA extraction, polymerase chain reaction and capillary electrophoresis. Use of the MSI technology to enable integration of this complex lab-on-a-chip system in a hybrid multi-chip format was demonstrated. Additionally, functional operation of the solid phase extraction and PCR thermocycling compartments was demonstrated using the MSI.