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Articles de revues sur le sujet « Genetic data »

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Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 20 février 2023

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1

Volkova, T., E. Furta, O. Dmitrieva et I. Shabalina. « Pattern Building Methods in Genetic Data Processing ». Journal on Selected Topics in Nano Electronics and Computing 1, no 2 (juin 2014) : 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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2

Taylor, Mark J. « Data Protection, Shared (Genetic) Data and Genetic Discrimination ». Medical Law International 8, no 1 (décembre 2006) : 51–77. http://dx.doi.org/10.1177/096853320600800103.

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3

Ross-Ibarra, Jeffrey. « Genetic Data Analysis II. Methods for Discrete Population Genentic Data ». Economic Botany 56, no 2 (avril 2002) : 216. http://dx.doi.org/10.1663/0013-0001(2002)056[0216:gdaimf]2.0.co;2.

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4

Slatkin, Montgomery, Wayne P. Maddison et B. S. Weir. « Genetic Data Analysis : Methods for Discrete Population Genetic Data. » Systematic Zoology 40, no 2 (juin 1991) : 248. http://dx.doi.org/10.2307/2992265.

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5

Chase, Gary A., et Bruce S. Weir. « Genetic Data Analysis : Methods for Discrete Population Genetic Data. » Journal of the American Statistical Association 86, no 413 (mars 1991) : 248. http://dx.doi.org/10.2307/2289745.

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6

Feytmans, E., et B. S. Weir. « Genetic Data Analysis : Methods for Discrete Population Genetic Data. » Biometrics 47, no 3 (septembre 1991) : 1205. http://dx.doi.org/10.2307/2532683.

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7

Morton, N. E. « Genetic Data Analysis. Methods for Discrete Population Genetic Data ». Journal of Medical Genetics 29, no 3 (1 mars 1992) : 216. http://dx.doi.org/10.1136/jmg.29.3.216.

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8

Slatkin, M., et W. P. Maddison. « Genetic Data Analysis : Methods for Discrete Population Genetic Data ». Systematic Biology 40, no 2 (1 juin 1991) : 248–49. http://dx.doi.org/10.1093/sysbio/40.2.248.

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9

Butler, Amy W., Sarah Cohen-Woods, Anne Farmer, Peter McGuffin et Cathryn M. Lewis. « Integrating Phenotypic Data For Depression ». Journal of Integrative Bioinformatics 7, no 3 (1 décembre 2010) : 290–99. http://dx.doi.org/10.1515/jib-2010-136.

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Abstract The golden era of molecular genetic research brings about an explosion of phenotypic, genotypic and sequencing data. Building on the common aims to exploit understanding of human diseases, it also opens up an opportunity for scientific communities to share and combine research data. Genome-wide association studies (GWAS) have been widely used to locate genetic variants, which are susceptible for common diseases. In the field of medical genetics, many international collaborative consortiums have been established to conduct meta-analyses of GWAS results and to combine large genotypic data sets to perform mega genetic analyses. Having an integrated phenotype database is significant for exploiting the full potential of extensive genotypic data. In this paper, we aim to share our experience gained from integrating four heterogeneous sets of major depression phenotypic data onto the MySQL platform. These data sets constitute clinical data which had been gathered for various genetic studies for the past decade. We also highlight in this report some generic data handling techniques, the costs and benefits regarding the use of integrated phenotype database within our own institution and under the consortium framework.
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10

Uzych, Leo. « Genetic Testing Data ». Journal of Occupational & ; Environmental Medicine 38, no 1 (janvier 1996) : 13–14. http://dx.doi.org/10.1097/00043764-199601000-00001.

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11

Rischitelli, Gary. « Genetic Testing Data ». Journal of Occupational & ; Environmental Medicine 38, no 1 (janvier 1996) : 14. http://dx.doi.org/10.1097/00043764-199601000-00002.

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12

Ahluwalia, Maninder. « Protecting genetic data ». New Scientist 247, no 3295 (août 2020) : 23. http://dx.doi.org/10.1016/s0262-4079(20)31409-3.

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The Lancet Oncology. « Consolidating genetic data ». Lancet Oncology 6, no 6 (juin 2005) : 351. http://dx.doi.org/10.1016/s1470-2045(05)70177-7.

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14

Rutledge, Louis C. « Genetic Data Analysis ». Annals of the Entomological Society of America 84, no 6 (1 novembre 1991) : 639. http://dx.doi.org/10.1093/aesa/84.6.639a.

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15

Mick Richardson, P. « Genetic data analysis ». Biochemical Systematics and Ecology 18, no 5 (août 1990) : 387. http://dx.doi.org/10.1016/0305-1978(90)90013-6.

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16

Lorey, Fred. « Human Genetics Data Applied to Genetic Screening Programs ». Practicing Anthropology 20, no 2 (1 avril 1998) : 30–33. http://dx.doi.org/10.17730/praa.20.2.n84728r821185380.

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The uses of human genetic data in genetic screening are multifaceted and dynamic, creating an ongoing stream of useful prevalence data, ethnicity data, and natural history information. Since the primary facility for generation of these data is a large public health genetic screening program, however, the results must be continually analyzed and evaluated in the context of testing parameters. For example, presumptive positive rates (initial screening test positives, only a portion of which will become diagnosed cases), false positive rates, detection rates, and analytical values must be constantly checked to ensure the screening program is running smoothly and effectively. Any departures from the expected must be investigated so that the cause(s) can be determined and corrected. On a longitudinal basis, outcomes must be evaluated to ensure that the intended purpose of preventing mortality and reducing morbidity through intervention is achieved.
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17

Weir, B. S. « Genetic Data Analysis II. » Biometrics 53, no 1 (mars 1997) : 392. http://dx.doi.org/10.2307/2533134.

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18

Ott, Jurg. « Genetic data analysis II ». Trends in Genetics 13, no 9 (septembre 1997) : 379. http://dx.doi.org/10.1016/s0168-9525(97)81169-9.

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19

Coupland, Robin, Sophie Martin et Maria-Teresa Dutli. « Protecting everybody's genetic data ». Lancet 365, no 9473 (mai 2005) : 1754–56. http://dx.doi.org/10.1016/s0140-6736(05)66563-4.

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20

Jorde, L. B. « Genetic Data Analysis : Methods for Discrete Population Genetic Data. Bruce S. Weir ». Quarterly Review of Biology 66, no 4 (décembre 1991) : 488–89. http://dx.doi.org/10.1086/417362.

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21

Spector-Bagdady, Kayte, Amanda Fakih, Chris Krenz, Erica E. Marsh et J. Scott Roberts. « Genetic data partnerships : academic publications with privately owned or generated genetic data ». Genetics in Medicine 21, no 12 (17 juin 2019) : 2827–29. http://dx.doi.org/10.1038/s41436-019-0569-z.

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22

Chikhi, L. « Genetic markers : How accurate can genetic data be ? » Heredity 101, no 6 (1 octobre 2008) : 471–72. http://dx.doi.org/10.1038/hdy.2008.106.

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23

Deckard, Jamalynne, Clement J. McDonald et Daniel J. Vreeman. « Supporting interoperability of genetic data with LOINC ». Journal of the American Medical Informatics Association 22, no 3 (5 février 2015) : 621–27. http://dx.doi.org/10.1093/jamia/ocu012.

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Abstract Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms follow the recommendations and nomenclature of other standards such as the Human Genome Organization Gene Nomenclature Committee’s terminology for gene names. In addition to the narrative text they report now, we recommend that laboratories always report as discrete variables chromosome analysis results, genetic variation(s) found, and genetic variation(s) tested for. By adopting and implementing data standards like LOINC, information systems can help care providers and researchers unlock the potential of genetic information for delivering more personalized care.
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24

Suksut, Keerachart, Kittisak Kerdprasop et Nittaya Kerdprasop. « Support Vector Machine with Restarting Genetic Algorithm for Classifying Imbalanced Data ». International Journal of Future Computer and Communication 6, no 3 (septembre 2017) : 92–96. http://dx.doi.org/10.18178/ijfcc.2017.6.3.496.

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25

Lyutov, N. L. « Genetic discrimination and protection of personal genetic data : Adapting legal standards to advances in genetics ». Journal of Physics : Conference Series 2210, no 1 (1 mars 2022) : 012001. http://dx.doi.org/10.1088/1742-6596/2210/1/012001.

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Abstract Genetics has advanced to the point where genetic data on an individual could mark them as predisposed to hereditary illness or unsuitable for certain kinds of jobs. There is widespread apprehension that workers with ‘problematic’ genetics will be singled out by employers and insurance companies for treatment as second-class citizens with restrictions placed on their rights. The article takes up the legal issues involved in defining the concept of genetic data, in regulating genetic information as a type of personal information, and in applying genetic antidiscrimination laws in various countries. Legal restraints on genetic information must be more extensive than on other personal information about a worker because genetic data has implications for their biological relatives. Protection against genetic discrimination must therefore begin with barring employers from collecting genetic information on workers unless it is necessary to prevent hazards to people’s lives or health.
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26

Atramentova, L., et H. Ehyakonandeh. « Molecular genetic data in terms of associative and population genetics ». 36, no 36 (25 août 2021) : 35–40. http://dx.doi.org/10.26565/2075-5457-2021-36-4.

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In studies on associative genetics of various multifactorial diseases, it is most often found that the minor allele’s frequency in the group of patients is higher than in the group of healthy people. Due to reduced adaptation, the minor allele manifests itself as a disease. In the group of patients, the number of homozygotes by major allele is reduced, the number of heterozygous carriers of the provocative allele is increased, and the frequency of homozygotes by the provocative allele is significantly increased. The aim of this article was to analyze the unusual result for SNP 1298A/C of the MTHFR gene in multiple sclerosis, previously obtained by one of the authors. The allele frequencies in the control group and in the group of multiple sclerosis do not differ, but the distribution of genotypes in the patients does not correspond to the Hardy–Weinberg ratio in compare to healthy people. Among patients, the number of heterozygotes is increased and the number of each homozygote is decreased. The increase in the proportion of heterozygotes can be explained by the presence of one provocative allele, but the large shortage of homozygotes for the minor allele is unclear. To explain this fact, the composition of the group of patients was analysed. The patients are of different ages, this group is heterogeneous in sex and form of multiple sclerosis, but none of these indicators has not be taken into account in the analysis of the distribution of genotypes. The age of the disease is a diagnostic sign and may depend on the genotype. The manifestation of multifactorial diseases depends on gender as well. Clinical forms of the disease usually have a different genetic basis. Due to the neglect of these conditions, a genetically heterogeneous group is formed, and any result, difficult for explanation, can be obtained. The lack of СС genotypes may be due to increased mortality, which reduces the probability of patients to be investigated in the sample. These facts once again indicate the need to form homogeneous groups for research on associative genetics.
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27

Rajavarma, V. N., et S. P. Rajagopala. « Feature Selection in Data-Mining for Genetics Using Genetic Algorithm ». Journal of Computer Science 3, no 9 (1 septembre 2007) : 723–25. http://dx.doi.org/10.3844/jcssp.2007.723.725.

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28

Greytak, Ellen M., David H. Kaye, Bruce Budowle, CeCe Moore et Steven L. Armentrout. « Privacy and genetic genealogy data ». Science 361, no 6405 (30 août 2018) : 857.1–857. http://dx.doi.org/10.1126/science.aav0330.

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29

Židanavičiutė, J. « LOGIT ANALYSIS OF GENETIC DATA ». Mathematical Modelling and Analysis 13, no 1 (31 mars 2008) : 135–44. http://dx.doi.org/10.3846/1392-6292.2008.13.135-144.

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A new framework of genetic sequence statistical analysis based on generalized logit model is introduced. Logit analysis is applied to assess the dependence structure (interactions) between DNA nucleotides and to test hypothesis about Markov order of these dependencies. The procedure proposed seeks the non‐coding subsequences which are homogeneous but yet non‐Markov. It has been shown, that even homogeneous DNA regions can not be treated as the first order Markov sequences.
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30

Check Hayden, Erika. « Data barriers limit genetic diagnosis ». Nature 494, no 7436 (février 2013) : 156–57. http://dx.doi.org/10.1038/494156a.

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31

Elkan, C. « Access to genetic sequence data ». Science 255, no 5045 (7 février 1992) : 663. http://dx.doi.org/10.1126/science.1738833.

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32

Lawson, Daniel John, et Daniel Falush. « Population Identification Using Genetic Data ». Annual Review of Genomics and Human Genetics 13, no 1 (22 septembre 2012) : 337–61. http://dx.doi.org/10.1146/annurev-genom-082410-101510.

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33

Sariyar, Murat, Stephanie Suhr et Irene Schlünder. « How Sensitive Is Genetic Data ? » Biopreservation and Biobanking 15, no 6 (1 décembre 2017) : 494–501. http://dx.doi.org/10.1089/bio.2017.0033.

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34

Drechsler, R., et N. Göckel. « Genetic algorithm for data sequencing ». Electronics Letters 33, no 10 (1997) : 843. http://dx.doi.org/10.1049/el:19970600.

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35

Otlowski, Margaret F., Sandra D. Taylor et Kristine K. Barlow-Stewart. « Genetic discrimination : Too few data ». European Journal of Human Genetics 11, no 1 (janvier 2003) : 1–2. http://dx.doi.org/10.1038/sj.ejhg.5200910.

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36

Bhasin, Harsh, et Neha Singla. « Cellular-genetic test data generation ». ACM SIGSOFT Software Engineering Notes 38, no 5 (26 août 2013) : 1–9. http://dx.doi.org/10.1145/2507288.2507303.

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37

Sorani, Marco D., John K. Yue, Sourabh Sharma, Geoffrey T. Manley, Adam R. Ferguson, Shelly R. Cooper, Kristen Dams-O’Connor et al. « Genetic Data Sharing and Privacy ». Neuroinformatics 13, no 1 (18 octobre 2014) : 1–6. http://dx.doi.org/10.1007/s12021-014-9248-z.

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38

Bowman, K. O., et M. A. Kastenbaum. « Overdispersion of aggregated genetic data ». Mutation Research/Environmental Mutagenesis and Related Subjects 272, no 2 (octobre 1992) : 133–37. http://dx.doi.org/10.1016/0165-1161(92)90041-j.

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Giles, Barbara E. « Genetic biodiversity : analysing the data ». Trends in Ecology & ; Evolution 9, no 9 (septembre 1994) : 317–19. http://dx.doi.org/10.1016/0169-5347(94)90150-3.

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40

S. Waples, Robin. « Guidelines for genetic data analysis ». J. Cetacean Res. Manage. 18, no 1 (24 janvier 2023) : 33–80. http://dx.doi.org/10.47536/jcrm.v18i1.421.

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The IWC Scientific Committee recently adopted guidelines for quality control of DNA data. Once data have been collected, the next step is to analyse the data and make inferences that are useful for addressing practical problems in conservation and management of cetaceans. This is a complex exercise, as numerous analyses are possible and users have a wide range of choices of software programs for implementing the analyses. This paper reviews the underlying issues, illustrates application of different types of genetic data analysis to two complex management problems (involving common minke whales and humpback whales), and concludes with a number of recommendations for best practices in the analysis of population genetic data. An extensive Appendix provides a detailed review and critique of most types of analyses that are used with population genetic data for cetaceans.
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41

Yandell, Brian S. « Graphical Data Presentation, with Emphasis on Genetic Data ». HortScience 42, no 5 (août 2007) : 1047–51. http://dx.doi.org/10.21273/hortsci.42.5.1047.

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42

Kuru, Taner, et Iñigo de Miguel Beriain. « Your genetic data is my genetic data : Unveiling another enforcement issue of the GDPR ». Computer Law & ; Security Review 47 (novembre 2022) : 105752. http://dx.doi.org/10.1016/j.clsr.2022.105752.

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43

Diaconescu, Ioana, et Sorin Hostiuc. « Pharmacogenomics : Ethical Issues in Data Management ». Studia Universitatis Babeş-Bolyai Bioethica 66, Special Issue (9 septembre 2021) : 69. http://dx.doi.org/10.24193/subbbioethica.2021.spiss.40.

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"Pharmacogenomics uses a DNA sequence in order to create a “genetic map” that determines which drugs are the most efficient for a specific disease, in a particular patient. The needed information for developing personalized therapies needs, besides genetic data, various non-genetic factors might interfere with some mechanisms of drug action, and they should also be considered. The assumption that the genetic data is more important than any other type of non-genetic medical information may severely alter the reliability of pharmacogenomics. In order to decrease the risk for non-genetic factors to significantly alter the pharmacogenomics-related therapies, patients need to provide detailed information about them. This, however, is often not specifically sought upon by neither the patient (who sees this information as trivial when the physician interacts directly with her/his genes), nor the physician (who is often a genetics/ pharmacogenetics expert, who tends to see the genetic information as supreme). One of the main targets in data management is privacy. A lot of effort is needed to keep the data anonymous and creating a detailed informed consent to determine the patient to acknowledge the risks and the benefits of pharmacogenomics. However, proper management of data also includes obtaining all the relevant information to maximize beneficence, this being especially important in frontier techniques, such as pharmacogenomics. The purpose of this study is to analyze the main ethical issues in data management in pharmacogenomics, with an emphasis on the way the physician-patient relationship should be developed to maximize relevant data extraction and optimize its management. "
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Z, Mouammine. « Big Data with Distributed Architecture Using Genetic Algorithm in Intelligent Transport Systems ». Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 juillet 2020) : 1405–15. http://dx.doi.org/10.5373/jardcs/v12sp7/20202243.

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Butil, John Carlo M., Ma Lei Frances Magsisi, John Hart Pua, Prince Kevin Se et Ria Sagum. « The Application of Genetic Algorithm in Motion Detection for Data Storage Optimization ». International Journal of Computer and Communication Engineering 3, no 3 (2014) : 199–202. http://dx.doi.org/10.7763/ijcce.2014.v3.319.

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Hallinan, Dara, Michael Friedewald et Paul De Hert. « Genetic Data and the Data Protection Regulation : Anonymity, multiple subjects, sensitivity and a prohibitionary logic regarding genetic data ? » Computer Law & ; Security Review 29, no 4 (août 2013) : 317–29. http://dx.doi.org/10.1016/j.clsr.2013.05.013.

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Jiang, Rong, Simon Tavaré et Paul Marjoram. « Population Genetic Inference From Resequencing Data ». Genetics 181, no 1 (3 novembre 2008) : 187–97. http://dx.doi.org/10.1534/genetics.107.080630.

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Mech, L. David. « Non-Genetic Data Supporting Genetic Evidence for the Eastern Wolf ». Northeastern Naturalist 18, no 4 (décembre 2011) : 521–26. http://dx.doi.org/10.1656/045.018.0409.

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Waller, Niels G., et Bengt O. Muth�n. « Genetic tobit factor analysis : Quantitative genetic modeling with censored data ». Behavior Genetics 22, no 3 (mai 1992) : 265–92. http://dx.doi.org/10.1007/bf01066662.

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Ziegler, Andreas, Nora Bohossian, Vincent P. Diego et Chen Yao. « Genetic Prediction in the Genetic Analysis Workshop 18 Sequencing Data ». Genetic Epidemiology 38, S1 (11 août 2014) : S57—S62. http://dx.doi.org/10.1002/gepi.21826.

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