Veronica J. Vieland, Ph.D.

Veronica J. Vieland, Ph.D., is Vice President for Computational Research and the Director of the Battelle Center for Mathematical Medicine at The Research Institute at Nationwide Children’s Hospital. She is a Professor in the Department of Pediatrics at The Ohio State University College of Medicine and in OSU’s Department of Statistics. Dr. Vieland’s research focuses on statistical and computational methods for discovery of genetic influences on human disease. She is also interested more generally in the measurement of evidence in biomedical research.

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Gender:

• Female

Languages Spoken:

• English

Research Center:

• Battelle Center for Mathematical Medicine

Areas of Interest:

• My focus is on the statistical and computational techniques that underlie human gene-mapping (localization of genes on the human genome), gene identification, and characterization of the genetic architecture of complex clinical phenotypes. My group has developed a novel class of quasi-Bayesian models, and much of our attention is devoted to ongoing expansion of the set of genetic features handled by the underlying likelihoods, together with evaluation of the efficacy of the models in the context of typical human data sets; the other arm of this work is the development of the high-performance computational approaches required for application of our statistical methods. I also collaborate actively on a number of clinical genetic studies. I have several funded projects focused on the genetics of autism, including, in addition to our own basic research, two projects in which our Center serves as the data coordinating site for large, international collaborations. Other projects in which I’ m currently involved are studying the genetics of cleft lip and palate, schizophrenia, autoimmune thyroid disease, systemic lupus erythematosus, and congenital heart defects.
  Robust Likelihoods for Genetic Analysis: All statistical work in the Vieland lab is in the likelihood framework. Likelihoods are equations that can be used to capture all of the evidential information in a given set of data. However, in human genetics, likelihoods are also always approximations: we use "summary" or approximating likelihoods to capture key genetic information based on relatively small numbers of parameters. An important line of research therefore involves the assessment of particular approximating likelihoods for robustness in human genetic applications. In addition, because most standard theoretical results regarding likelihoods are derived in the context of exact likelihoods (e.g., a normal likelihood applied to normally distributed data), and because they do not necessarily apply to approximating likelihoods, special investigations are required to assay and document properties of approximating genetic likelihoods. A great deal of our focus has been on defining, evaluating, and adapting a core set of relatively simple likelihoods that appear to be highly robust in the context of human genetic studies. These investigations form the basis of our signature "PPL Framework," but also have important statistical genetic implications in their own right.
• Absolute Evidence Measures: In human genetics, we tend to equate the strength of statistical evidence with either maximum likelihood ratio statistics (maxLRs) or p-values. However, both maxLRs and p-values have some undesirable properties as evidence measures when applied to human genetics and in many other settings. We are developing a coherent theoretical approach to formalizing statistical evidence measures, drawing heavily on lessons gleaned from the development of an absolute thermometric scale in the context of thermodynamics. We expect this project to lead to more effective ways of both finding and excluding genes for specific disease processes, and to have utility for other disciplines and experimental modalities as well.
• KELVIN Development: Kelvin is a computer package in support of application of the PPL framework for measurement of evidence in statistical genetics. Kelvin’s approach to modeling genetic architecture is computationally intensive and requires continuous software development. Ongoing work focuses on efficient methods for constructing, storing, and evaluating genetic likelihoods represented as high-order polynomials, and for efficiently distributing computations across multiple compute nodes and/or making optimal use of multiple processors within nodes. An additional computational challenge is the need for integration of moderately high-dimensional functions lacking closed form solution. We use a modified version of DCUHRE, a sub-region adaptive algorithm using an embedded family of fully symmetric multiple quadrature rules, which integrates polynomials exactly over hypercubes in an efficient manner, and which we have adatpted for our specific purposes. Ongoing software engineering focuses on extending the complexity of the calculations that Kelvin can perform in order to address emerging data analytic needs, including such things as multipoint likelihood calculations in large and complex pedigrees based on dense SNP maps, and genetic analyses involving multiple interacting loci. Two closely related projects are the Modeling Genetic Architecture for Human Diseases, which utilizes Kelvin to implement a broad and flexible class of statistical genetic models, and Absolute Evidence Measurement, which focuses on optimal measurement of evidence in biological applications.Another related project is Kelviz, a suite of programs for visualization of Kelvin output.
• Modeling Genetic Architecture for Human Diseases: We have developed the PPL framework as a unified platform for modeling multiple aspects of genetic architecture across disparate data structures. We have implemented this framework in the software package Kelvin. Kelvin can perform linkage and trait-marker linkage disequilibrium (association) analysis in pedigrees, “trios,” and/or case-control data, for dichotomous or quantitative traits. The framework includes flexible models for handling genetic complexity, including intra- and inter-dataset heterogeneity, gene-gene interactions, imprinting, and covariate effects. The addition of new data analytic functionality is ongoing. Two closely related projects are Kelvin Development, which harnasses state-of-the-art software engineering and high performance computing in support of Kelvin applications, and Absolute Evidence Measurement, which focuses on optimal measurement of evidence in biological applications.

Undergraduate School

• Barnard College
  Date Completed: 06/30/1979

Post Doctoral

• Columbia University
  Date Completed: 06/30/1986

Graduate School

• Columbia University
  Date Completed: 06/30/1988

Fellowship

• Columbia University
  Date Completed: 06/30/1990

2011–present

• Vice-President for Computational Research, The Research Institute at Nationwide Children's Hospital

2007–present

• Battelle Chair in Quantitative and Computational Biology, The Research Institute at Nationwide Children's Hospital

2007–present

• Director, Battelle Center for Mathematical Medicine (formerly known as the Center for Quantitative and Computational Biology), The Research Institute at Nationwide Children's Hospital

2007–present

• Professor, Department of Statistics (Secondary), The Ohio State University

2006–present

• Professor of Pediatrics, The Ohio State University

2007–2010

• Adjunct Professor, Department of Genetics, Rutgers University

2006–2007

• Director, Center for Quantitative and Computational Biology, The Research Institute at Nationwide Children's Hospital

2006–2007

• Dwight E. Peters and Juanita R. Curran Professor of Pediatric Research, The Research Institute at Nationwide Children's Hospital

2003–2006

• Professor and Head, Department of Public Health Genetics; Professor of Psychiatry and Genetics, The University of Iowa

2000–2006

• Director, Center for Statistical Genetics Research, The University of Iowa

2000–2003

• Director, Division of Statistical Genetics, Department of Biostatistics, The University of Iowa

1995–1999

• Associate Professor of Biostatistics, Psychiatry, and Genetics, The University of Iowa

1990–1995

• Assistant Professor, Departments of Psychiatry and Biostatistics, Columbia University

• Vieland VJ. 2013. An evidential interpretation of the 1st and 2nd laws of thermodynamics.  arXiv.org. Vol. arXiv:1301.2150, no. January: e..
• Anney R, Klei L, Pinto D for the Autism Genome Project (AGP). 2012. Individual common variants exert weak effects on risk for autism spectrum disorders.  Hum Molec Genet. Vol. 21, no. 21. (November): 4781-4792.
• Vieland VJ, Das J, Hodge SE, Seok S-C. 2012. Measurement of statistical evidence on an absolute scale following thermodynamic principles.  ArXiv.org. Vol. arXiv:1206.3543v1, no. June: e..
• Casey JP, Magalhaes T, Conroy JM, et al for the Autism Genome Project (AGP). 2012. A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder.  Hum Genet. Vol. 131, no. 4. (April): 565-579.
• Vieland VJ, Huang Y, Seok SC, Burian J, Catalyurek U, O'Connell J, Segre AM and Valentine-Cooper W. 2011. KELVIN: A Software package for rigorous measurement of statistical evidence in human genetics.  Hum Hered. Vol. 72, no. 4. (December): 276-288.
• Vieland VJ, Devoto M. 2011. Next generation linkage analysis.  Hum Hered. Vol. 72, no. 4. (December): e227.
• Casey JP, Magalhaes T, Conroy JM, et al for the Autism Genome Project (AGP). 2011. A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder.  Hum Genet. Vol. 4, no. DOI 10.1007/s00439-011-1094-6. (October): 565-579.
• Vieland VJ, Hodge SE. 2011. Measurement of evidence and evidence of measurement.  Stat Applications in Genet & Molec Biol. Vol. 10, no. 1. (July): 1-11.
• Anney R, Kenny EM, O'Dushlaine C, Yaspan BL, Parkhomenka E, The Autism Genome Project (AGP), Buxbaum JD, Sutcliffe J, Gill M and Gallagher L. 2011. Gene-ontology enrichment analysis in two independent family-based samples highlights biologically plausible processes for autism spectrum disorders.  European J of Hum Genetics. Vol. 19, no. 10. (April): 1082-1089.
• Vieland VJ. 2011. Where's the evidence?.  Invited Commentary, Hum Hered. Vol. 71, no. 1. (April): 59-66.
• Vieland VJ. 2011. Where's the Evidence?.  Hum Hered.
• Vieland VJ, Hallmayer J, Huang Y, Pagnamenta AT, Pinto D, Khan H, Monaco AP, Paterson AD, Scherer SW, Sutcliffe JS, Szatmari P and the Autism Genome Project (AGP). 2011. Novel method for combined linkage and genomewide association analysis finds evidence of distinct genetic architecture for two subtypes of autism.  J Neurodev Disord. Vol. 3, no. 2. (January): 113-123.
• Anney R, Lambertus K, Pinto D, et al for the Autism Genome Project. 2010. A genomewide scan for common alleles affecting risk for autism.  Human Molecular Genetics. Vol. 19, no. 20. (July): 4072-4082.
• Pinto D, Pagnamenta AT, Klei L, et al for the Autism Genome Project. 2010. Functional impact of global rare copy number variation in autism spectrum disorders.  Nature. Vol. 466, no. 7304. (July): 368-372.
• Huang Y, Vieland VJ. 2010. Association statistics under the PPL framework.  Genet Epidem. Vol. 34, no. 8. (January): 835-845.
• Noor A, Whibley A, Marshall CR, et al for the Autism Genome Project. 2010. Disruption at the PTCHD1 Locus on Xp22.11 in Autism Spectrum Disorder and Intellectual Disability.  Science Translational Medicine.
• Pagnamenta AT, Khan H, et al for the Autism Genome Project. 2010. Rare familial 16q21 microdeletions under a linage peak implicate cadherin 8 (CDH8) in susceptibility to autism and learning disability.  J of Medical Genetics. Vol. 48, no. 1. (January): 48-54.
• Flax JF, Hare A, Azaro MA, Vieland VJ, Brzustowicz LM. 2010. Combined linkage and linkage disequilibrium analysis of a motor speech phenotype within families ascertained for autism risk loci.  J Neurodevelopmental Disorders. Vol. 2, no. 4. (January): 210-223.
• Hodge SE, Vieland VJ. 2010. Expected monotonicity- A desirable property for evidence measures?.  Hum Hered. Vol. 70, no. 3. (January): 151-166.
• Weiss LA, Arking DE; Gene Discovery Project of Johns Hopkins & the Autism Consortium, Daly MJ, Chakravarti A. 2009. A genome-wide linkage and association scan reveals novel loci for autism.  Nature. Vol. 461, no. 7265. (October): 802-808.
• Wratten NS, Memoli H, Huang Y, Dulencin AM, Matteson PG, Comacchia MA, Azaro MA, Messenger J, Hayter JE, Bassett AS, Buyske S, Millonig JH, Vieland VJ, Brzustowicz LM. 2009. Identification of a schizophrenia associated functional non-coding variant in NOS1AP.  Amer J Psychiatry. Vol. 166, no. March: 434-441.
• Nouanesengsy B, Seok S-C, Shen H-W, Vieland VJ. 2009. Using projection and 2D plots to visually explore multidimensional genetic likelihood spaces.  IEEE VAST 2009 Proceedings.
• Psychiatric GWAS Consortium Coordinating Committee. 2009. Genomewide association studies: history, rationale, and prospects for psychiatric disorders.  Am J Psychiatry. Vol. 166, no. 5. (January): 540-556.
• Seok S, Evans M, Vieland VJ. 2009. Fast and accurate calculation of a computationally intensive statistic for mapping disease genes.  J Comput Biol. Vol. 16, no. 5. (January): 659-676.
• Vieland VJ, Huang Y, Bartlett C, Davies TF, Tomer Y. 2008. A multilocus model of the genetic architecture of autoimmune thyroid disorder, with clinical implications.  Am J Hum Genet. Vol. 82, no. 6. (June): 1349-1356.
• Govil M, Segre AM, Vieland VJ. 2008. MLIP: using multiple processors to compute the posterior probability of linkage.  BMC Bioinformatics. Vol. 9, no. May: eSuppl 6:S2.
• Wassink TH, Vieland VJ, Sheffield VC, Bartlett CW, Goedken R, Childress D, Piven J. 2008. Posterior probability of linkage analysis of autism dataset identifies linkage to chromosome 16.  Psychiatr Genet. Vol. 18, no. 2. (April): 85-91.
• Liu XQ, Paterson AD, Szatmari P, Autism Genome Project Consortium. 2008. Genome-wide linkage analyses of quantitative and categorical autism subphenotypes.  Biol Psychiatry. Vol. 64, no. 7. (January): 561-570.
• Govil M, Vieland VJ. 2008. Practical considerations for dividing data into subsets prior to PPL analysis.  Hum Hered. Vol. 66, no. January: 223-237.
• Govil M, Vieland VJ. 2008. Practical considerations for dividing data into subsets prior to PPL analysis.  Hum Hered. Vol. 66, no. January: 223-237.
• Autism Genome Project. 2007. Mapping autism risk loci using genetic linkage and chromosomal rearrangements.  Nature Genetics. Vol. 39, no. 3. (January): 319-328.
• Huang Y, Bartlett CW, Segre AM, O'Connell JR, Mangin LV, Vieland VJ. 2007. Exploiting gene × gene interaction in linkage analysis.  BMC Proceedings. Vol. 1, no. January: eS64.
• Wang H, Segre AM, Huang Y, O'Connell J, VielandVJ. 2007. Rapid Computation of Large Numbers of LOD Scores in Linkage Analysis through Polynomial Expression of Genetic Likelihoods,.  Proceedings of IEEE Workshop on High-Throughput Data Analysis for Proteomics and Genomics (Silicon Valley). Vol. Nov 2-4, no. January: 197-204.
• Bartlett CW, Vieland VJ. 2007. Accumulating quantitative trait linkage evidence across multiple datasets using the posterior probability of linkage.  Genetic Epidemiology. Vol. 31, no. 2. (January): 91-102.
• Bartlett CW, Vieland VJ, on behalf of Group 7. 2007. Discussing Gene-gene interaction: Warning- Translating equations to English may result in jabberwocky.  Genetic Epidemiology. Vol. 31, no. Supplement 1. (January): S61-S67.
• Cordell HJ, de Andrade M, Babron MC, Bartlett CW, Beyene J, Bickeböller H, Culverhouse R, Cupples LA, Daw EW, Dupuis J, Falk CT, Ghosh S, Goddard KA, Goode EL, Hauser ER, Martin LJ, Martinez M, North KE, Saccone NL, Schmidt S, Tapper W, Thomas D, Tritchle. 2007. Genetic Analysis Workshop 15: gene expression analysis and approaches to detecting multiple functional loci.  BMC Proceedings. Vol. 1, no. January: eS1.
• Wang H, Segre AM, Huang Y, O’Connell JR, Vieland VJ. 2007. Fast computation of human genetic linkage. Bioinformatics and Bioengineering , BIBE 2007.  Proceedings of the 7th IEEE International Conference. Vol. Oct 14-17, no. January: 857-863.
• Vieland VJ. 2006. Thermometers: something for statistical geneticists to think about.  Human Heredity. Vol. 61, no. 3. (January): e144.
• Logue MW; Brzustowicz LM; Bassett AS; Chow EW; Vieland VJ. 2006. A posterior probability of linkage-based re-analysis of schizophrenia data yields evidence of linkage to chromosomes 1 and 17.  Human Heredity. Vol. 62, no. 1. (January): e47.
• Wassink TH; Piven J; Vieland VJ; Jenkins L; Frantz R; Bartlett CW; Goedken R; Childress D; Spence MA; Smith M; Sheffield VC. 2005. Evaluation of the chromosome 2q37.3 gene CENTG2 as an autism susceptibility gene.  American Journal Of Medical Genetics. Part B, Neuropsychiatric Genetics: The Official Publication Of The International Society Of Psychiatric Genetics. Vol. 136B, no. 1. (July): e36.
• Wassink TH; Losh M; Frantz RS; Vieland VJ; Goedken R; Piven J; Sheffield VC. 2005. A case of autism and uniparental disomy of chromosome 1.  Human Genetics. Vol. 117, no. 2-3. (July): e200.
• Vieland VJ; Hodge SE. 2005. Ascertainment bias in linkage analysis: comments on Ginsburg et al.  Genetic Epidemiology. Vol. 28, no. 3. (April): e283.
• Bartlett CW; Goedken R; Vieland VJ. 2005. Effects of updating linkage evidence across subsets of data: reanalysis of the autism genetic resource exchange data set.  American Journal Of Human Genetics. Vol. 76, no. 4. (April): e688.
• Yang X; Huang J; Logue MW; Vieland VJ. 2005. The posterior probability of linkage allowing for linkage disequilibrium and a new estimate of disequilibrium between a trait and a marker.  Human Heredity. Vol. 59, no. 4. (January): e210.
• Logue MW; George AW; Spence MA; Vieland VJ. 2005. Performance comparison of two-point linkage methods using microsatellite markers flanking known disease locations.  BMC Genetics. Vol. 6 Suppl 1, no. January: eS141.
• Vieland VJ.  Heterogeneity:  GAW Group 15.  Genet Epidem Supp S110-S115.  2005.
• George AW; Mangin LA; Bartlett CW; Logue MW; Segre AM; Vieland VJ. 2005. Calculation of multipoint likelihoods using flanking marker data: a simulation study.  BMC Genetics. Vol. 6 Suppl 1, no. January: eS44.
• Logue MW; Vieland VJ. 2005. The incorporation of prior genomic information does not necessarily improve the performance of Bayesian linkage methods: an example involving sex-specific recombination and the two-point PPL.  Human Heredity. Vol. 60, no. 4. (January): e196.
• Bartlett CW; Vieland VJ. 2005. Two novel quantitative trait linkage analysis statistics based on the posterior probability of linkage: application to the COGA families.  BMC Genetics. Vol. 6 Suppl 1, no. January: eS121.
• Vieland VJ. 2005. Heterogeneity: GAW Group 15.  Genetic Epidemiology. Vol. 29 Suppl 1, no. January: eS110.
• Logue MW, Vieland VJ.  The incorporation of prior genomic information does not necessarily improve the performance of Bayesian linkage methods:  An example involving sex-specific recombination and the two-point PPL.  Human Hered 60:196-205.  2005 PubMed ID: 16397399
• Bartlett CW, Vieland VJ.  Two novel quantitative trait linkage analysis statistics based on the posterior probability of linkage:  Application to the COGA families.  BMC Genetics 6(Suppl 1):S121.  2005. PubMed ID: 16451579
• Wassink TH; Piven J; Vieland VJ; Pietila J; Goedken RJ; Folstein SE; Sheffield VC. 2004. Examination of AVPR1a as an autism susceptibility gene.  Molecular Psychiatry. Vol. 9, no. 10. (October): e968.
• Bartlett CW; Flax JF; Logue MW; Smith BJ; Vieland VJ; Tallal P; Brzustowicz LM. 2004. Examination of potential overlap in autism and language loci on chromosomes 2, 7, and 13 in two independent samples ascertained for specific language impairment.  Human Heredity. Vol. 57, no. 1. (January): e10.
• Logue MW; Vieland VJ. 2004. A new method for computing the multipoint posterior probability of linkage.  Human Heredity. Vol. 57, no. 2. (January): e90.
• Logue MW; Vieland VJ; Goedken RJ; Crowe RR. 2003. Bayesian analysis of a previously published genome screen for panic disorder reveals new and compelling evidence for linkage to chromosome 7.  American Journal Of Medical Genetics. Part B, Neuropsychiatric Genetics: The Official Publication Of The International Society Of Psychiatric Genetics. Vol. 121B, no. 1. (August): e95.
• Vieland VJ; Huang J. 2003. Two-locus heterogeneity cannot be distinguished from two-locus epistasis on the basis of affected-sib-pair data.  American Journal Of Human Genetics. Vol. 73, no. 2. (August): e223.
• Spence MA; Greenberg DA; Hodge SE; Vieland VJ. 2003. The emperor's new methods.  American Journal Of Human Genetics. Vol. 72, no. 5. (May): e1084.
• Yang X; Wang K; Huang J; Vieland VJ. 2003. Genome-wide linkage analysis of blood pressure under locus heterogeneity.  BMC Genetics. Vol. 4 Suppl 1, no. January: eS78.
• Logue MW; Goedken RJ; Vieland VJ. 2003. A model-integrated multipoint Bayesian analysis of hypertension in the Framingham Heart Study data finds little evidence of linkage.  BMC Genetics. Vol. 4 Suppl 1, no. January: eS75.
• Bassett AS; Chow EW; Vieland VJ; Brzustowicz L. 2002. Is schizophrenia linked to chromosome 1q?.  Science (New York, N.Y.). Vol. 298, no. 5602. (December): e2277; author reply 2277.
• Bartlett CW; Flax JF; Logue MW; Vieland VJ; Bassett AS; Tallal P; Brzustowicz LM. 2002. A major susceptibility locus for specific language impairment is located on 13q21.  American Journal Of Human Genetics. Vol. 71, no. 1. (July): e45.
• Wassink TH; Piven J; Vieland VJ; Pietila J; Goedken RJ; Folstein SE; Sheffield VC. 2002. Evaluation of FOXP2 as an autism susceptibility gene.  American Journal Of Medical Genetics. Vol. 114, no. 5. (July): e566.
• Hodge SE; Vieland VJ; Greenberg DA. 2002. HLODs remain powerful tools for detection of linkage in the presence of genetic heterogeneity.  American Journal Of Human Genetics. Vol. 70, no. 2. (February): e556.
• Vieland VJ; Logue M. 2002. HLODs, trait models, and ascertainment: implications of admixture for parameter estimation and linkage detection.  Human Heredity. Vol. 53, no. 1. (January): e23.
• Buxbaum JD, Bolshakova N, Brownfield JM, Anney R, Bender P, Bernier R, Cook EH, Coon H, Cuccaro M, Freitag CM, Hallmayer J, Geshwind D, Klauck SB, Lehner T, Burnberger JI, Oliveira G, Pinto D, Poustka F, Scherer S, Shih A, Sutcliffe JS, Szatmari P, Vicente AM, Vieland VJ, Gallagher L. The Autism Simplex Collection: An international, expertly phenotyped autism sample for genetic and phenotypic analyses.  Molecular Autism.
• Buxbaum JD< Bolshakova N, Brownfield JM, Anney R, Bender P, Bernier R, Cook EH, Coon H, Cuccaro M, Freitag CM, Hallmayer J, Geshwind D, Klauck SB, Lehner T, Burnberger JI, Oliveira G, Pinto D, Poustka F, Scherer S, Shih A, Sutcliffe JS, Szatmari P, Vicente Am, Vieland VJ, Gallagher L. The Autism Simplex Collection: An international, Expertly phenotyped autism sample for genetic and phenotypic analyses.  Molecular Autism, 2012.
• Stewart WCL, Huang Y, Greenberg DA, Vieland VJ. Next generation linkage and association methods applied to hypertension: A multi-faceted approach to the analysis of sequence data.  BMC Genetics Proceedings.
• Buxbaum JD, Bolshakova N, Brownfield JM, Anney R, Bender P, Bernier R, Cook EH, Coon H, Cuccaro M, Freitag CM, Hallmayer J, Geshwind D, Klauck SB, Lehner T, Burnberger JI, Oliveira G, Pinto D, Poustka F, Scherer S, Shih A, Sutcliffe JS, Szatmari P, Vicente AM, Vieland VJ, Gallagher L. The Autism Simplex Collection: An international, expertly phenotyped autism sample for genetic and phenotypic analyses.  Molecular Autism.