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RESEARCH INTERESTS The primary goal of my research is to identify vulnerability and protective alleles that underlies the pathogenesis of common complex diseases and targets for therapeutic intervention using genetics, genomics, biostatistics and computational biology tools. The identification of these alleles will lead to a better understanding of mechanisms of vulnerability, individualization of treatment, and definition of gene-environment interaction. In farm animal species, this will lead to the use of molecular information on animal selection and breeding programs to develop animals that are genetically resistant to economically important complex diseases as well as top food producers. The basic approach in these studies is to perform whole genome linkage and association scans, and direct scan of candidate genes in large clinically evaluated samples from well defined populations. I am interested in determining the molecular genetic and
environmental basis of common complex diseases and drug response in humans
and animals. I have a special interest on these human diseases and research
projects: 1. Addiction to alcohol and other drugs and
related psychiatric disorders 2. Mental disorders such as schizophrenia
and bipolar disorder 3. Neurodegenerative disorders such
as Alzheimer’s and Parkinson’s disease 4. Multiple sclerosis, diabetes type
2, heart diseases and cancers 5. Pharmacogenetics and nutritional
genomics I am also interested in these complex diseases of farm animals: 1.
Susceptibility to bacterial and viral induced
diseases 2.
Metabolic, reproductive and foot and leg disorders 3.
Behavioral and stress related disorders 4.
Bovine spongiform encephalopathy (BSE) 5.
Growth, milk, meat and other production traits 6.
Nutritional genomics Genetics Background and Rationale: The multifactorial genetic model is appropriate for the
investigation of most common complex diseases including cancers,
cardiovascular diseases and drug response. This model postulates that an
unobservable liability variable, which is a composite of many genetic and
environmental contributions, underlies the outward expression of the
multifactorial disorder. The
clinically defined disorder is observed when liability exceeds a certain
threshold value (Falconer and MacKay, 1996). Alleles at a particular locus may
contribute to liability, but it is unlikely that any allele or genotype is
sufficient or necessary for expression of the disorder. The main objective of research on
multifactorial disorders is to identify specific loci that segregate alleles
that contribute to individual differences in liability. By identifying these genes we hope to
better understand the primary physiology of the disorder and predisposing
factors. A greater knowledge of
genetic risk factors may increase our ability to focus on important
environmental disease risk factors and facilitate the development of
effective prevention and treatment strategies. Genetic linkage and association analysis provides the
basis for a broad class of strategies aimed at identifying gene polymorphisms
that contribute to liability for a multifactorial disorder and drug
response. The essence of this
strategy is to show that an unknown polymorphic locus that contributes a
portion of the variance of liability to a disease is linked to a polymorphic
marker locus with a known location on the genetic map. Thus, the marker identifies the
position of the unknown susceptibility locus on a specific chromosome. The ultimate goal is to use this
information on position to identify the disease locus (i.e. positional
cloning). Another broad class of
strategies for identifying polymorphic genes that contribute to liability to
common diseases is the identification of candidate genes with known
biological function or functions related to disease pathways. Once a candidate gene has been
proposed, the goal is to associate a phenotype to the presence/absence or
number of alleles within genotypes at this locus. Approaches and Research Tools: To achieve my research goals, I use genetics (population
& quantitative), genomics (statistical, functional, & comparative),
and computational biology tools (bioinformatics). To identify the genes that
constitute risk factors for common complex diseases and drug response, I will
use family- and population-based study designs. To collect blood-tissue samples from
patient populations and clinical trials, I would advocate for the
collaboration with an MD scientist or a clinician with expertise in the
disease under study. I will
use information on positional cloning, gene expression profiling, and disease
biology and pathways to define a set of plausible candidate genes for the
disease under study. Then
using high throughput mutation analysis methods and in silico approaches, I
will search for sequence variants or SNPs in the disease candidate
genes. These SNPs will be
genotyped on population- or family-based samples using high throughput
genotyping approaches. To
assess the effect of the gene sequence variants on the disease trait
variation, I will develop and implement the use of novel and powerful methods
of linkage and linkage disequilibrium analysis. In the genetic data analyses, instead
of inefficient two-point methods of analysis, I will carry out large-scale genome
data analysis (e.g., multipoint linkage analysis, multilocus haplotype-based
analysis). Pharmacogenetics: I am also interested in the identification of genetic and
environmental factors that determines an individual’s response to
therapeutic drugs used in the treatment of common complex diseases. This is a challenging task and
for a meaningful contribution of pharmacogenetics on the realization of a
personalized genomic medicine, we have to use all the rigorous methods used
in the genetic dissection of common complex diseases and multidisciplinary
research strategies. I will
use multiple candidate gene and genomic-based approaches for unrevealing the
genetic basis of variable drug response. As genotyping technologies
become faster and cheaper, the completion of the haplotype map project will
provide insights in the use of a minimal number of SNPs to capture the bulk
of the common sequence variability in the human genome. Microarray technology is attractive
where the relevant tissue for drug response is easily accessible (e.g.
cancers). However, in situations
where multiple organs or tissues are involved in the drug response and/or the
relevant tissue is not readily obtainable (e.g. brain, heart), a SNP-based
approach is more applicable in pharmacogenetics. Gene-Environment
Interaction & Nutritional Genomics: Genes account only for a fraction of the disease trait
variation and the environment plays a major role on the expression of complex
diseases. The disease onset and prognosis can be delayed or minimized,
respectively. The disease also may never occur if environments that favor
disease expression are avoided.
The knowledge on the genetic risk factors for complex diseases will
facilitate the identification of the most important environmental risk
factors for these diseases. To
identify important environmental risk factors for complex diseases, we will
need measurements on environmental variables that likely constitute risk
factors for the disease under study (e.g. type of diet, extent of physical
exercise, caffeinated beverage intake, drinking and smoking history or
exposure to other drugs, exposure to toxins/carcinogens or pathogens, history
of stressful life events, etc.).
A multivariate analysis of these environmental variables and known genetic
risk factors will help to asses the effect of genes, environment, and their
interactions on the onset and severity of complex diseases. Similarly, nutritional genomics is the
study of the gene-environment (in this case nutritional diet) interaction effect
on the onset and severity of common complex diseases. By identifying an
“individualized” diet, we hope to delay onset of disease and
optimize and maintain health in humans and animals. This is a challenging task that can be
accomplished by a collaborative effort of statistical geneticists, nutritionists
and clinical scientists.
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Penn State | College of Agricultural Sciences | Dairy and Animal Science | Graduate School |
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2001 |
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