Ani W. Manichaikul

Ani ManichaikulDegree(s): PhD
Graduate School: Johns Hopkins University
Primary Appointment: Assistant Professor, Public Health Sciences

Research Interests
I perform data driven research in complex trait genetics for both human and experimental populations, with an emphasis on gene-gene interaction, gene-environment interaction, and network modelling.
Website:  http://people.virginia.edu/~am3xa/
Email Address: am3xa@virginia.edu

Research Description

My research lies in the areas of mathematical and statistical modeling of biological systems, with an emphasis on 1) quantitative trait mapping in experimental crosses, 2) analysis of genome-wide association studies, and 3) pathway analysis and gene network modelling. I am also using integrative genomic and bioinformatic approaches toward the ultimate goal of mapping functional variants for complex genetic traits.

Complex genetic diseases are increasingly thought to be influenced by multiple genes or gene networks, presenting a need to move beyond one-at-a-time hypothesis testing. My work in quantitative trait locus (QTL) model selection responds to this trend, providing a systematic framework for probing multiple QTL models with gene-gene interaction in experimental crosses. We take a penalized likelihood approach with penalties derived from single and two-dimensional permutation thresholds. Doing so gives the model selection procedure the desirable property that the false positive rate is kept under control, a major concern given the large model space imposed by incorporation of epistatic interaction. A key feature of this approach is that it is easily automated, facilitating consideration of gene-gene interaction models by non-statisticians. Moreover, an automated approach enables systematic treatment of the high-throughput phenotype data generating by emerging metabolic and proteomic studies.

Genome-wide association studies from multi-center and large-scale consortia provide high-throughput genotype data (~1,000,000 SNPs) for thousands of individuals often phenotyped across a range of physiological and behavioral traits. These rich data sets provide novel opportunities for development of statistical methodology. For example, we are currently developing new methods for relationship inference which take advantage of high-throughput genotype data for rapid and accurate estimation of key genetic parameters. As an active member of the analysis team for ongoing genome-wide association studies in the Center for Public Health Genomics, I am also probing phenotype specific questions by considering gene-environment interaction and pathway analysis.

Selected Publications

  • Chen WM, Manichaikul A, Rich SS (2009) A generalized family-based association test for dichotomized traits. Am J Hum Genet 85:364-76.
  • Manichaikul A, Ghamsari L, Hom EFY, Lin C, Murray RR, Chang RL, Balaji S, Hao T, Shen Y, Chavail AK, Thiele I, Yang X, Fan C, Mello E, Hill DE, Vidal M, Salehi-Ashtiani K, Papin JA (2009) Metabolic network analysis integrated with transcript verification for sequenced genomes. Nat Methods 6:589-92.
  • Fushan AA, Simons CT, Slack JP, Manichaikul A, Drayna D (2009) Allelic polymorphism within the TAS1R3 promoter is associated with human taste sensitivity to sucrose. Current Biology 19:1288-93.
  • Manichaikul A, Moon JY, Sen S, Yandell BS, Broman KW (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. Genetics 181: 1077-86.

PubMed listing for this faculty member

Contact Information
Mailing Address:  PO Box 800717, Charlottesville, VA 22908
Phone:  434-982-1612
Fax:  434-982-1815