Research Interests

Behnam Abasht, Associate Professor, Animal and Food Sciences, 035 Townsend Hall Newark, DE 19716 Phone: (302) 831-8876 Fax number: (302) 831-2822 Email:

I. Genetics and Genomics

  • Genomic Imprinting and Dosage Compensation
  • Linkage Disequilibrium and Genome-Wide Association Analysis

II. Wooden Breast Disease

  • Transcriptomics
  • Metabolomics
  • Histology and Electron Microscopy

III. Bioinformatics

  • Allele Specific Expression
  • Messenger RNA Expression
  • 3′-UTR Sequencing



I. Genetics and Genomics

Genomic Imprinting

RNA-Seq Analyses Identify Frequent Allele Specific Expression and No Evidence of Genomic Imprinting in Specific Embryonic Tissues of Chicken.  Zhuo et al., 2017. Scientific Reports [just-accepted]

Imprinted genes have been identified in several animal species and are frequently associated with embryonic development and growth. Yet whether genomic imprinting exists in chickens remains debatable, as previous studies have reported conflicting evidence. We interrogated the existence or absence of genomic imprinting in 12-day chicken embryonic brain and liver by examining ASE in F1 reciprocal crosses of two highly inbred chicken lines (Fayoumi and Leghorn).  There was no evidence detected of genomic imprinting in chicken 12-day embryonic brain and liver. Additionally, while ruling out the possibility of imprinted Z-chromosome inactivation, our results indicated that Z-linked gene expression is partially compensated between sexes in chickens.

Balanced expression at genome-wide and chromosome-wide levels: a. The distribution of paternal and maternal expression ratios at genome-wide level. b. Box plot of paternal allele expression ratio for each chromosome.

Linkage Disequilibrium and Genomic Signatures of Recent Selection

Linkage disequilibrium in crossbred and pure line chickens. Fu et al., 2015. Genetics Selection Evolution  

Decay of linkage disequilibrium with distance on different categories of chromosomes in different populations.

Length of haploblocks and haplotype homozygosity in different categories of chromosome in different populations.

Genomic signatures of recent selection in commercial broiler chickens. Fu et al., 2016. BMC Genetics

II. Wooden Breast Disease

A major part of research in Dr. Abasht’s lab has focused on studying Wooden Breast Disease, a novel muscle disorder of unknown etiology in commercial broiler chickens. The most severe cases of the disease are characterized by extreme stiffness of the breast muscle (pectoralis major), which can be detected by manual palpation. Using a combination of methods, including transcriptome sequencing (RNA-seq), histology, electron microscopy, and bioinformatics, Dr. Abasht’s lab investigates and hopes to identify factors causing this myopathy in modern broiler chickens. The lab uses multidisciplinary approaches to track the flow of biological information from DNA to RNA and protein, and eventually its impact on metabolism at molecular, cellular and whole-organismal levels.


Oxidative Stress and Metabolic Perturbations in Wooden Breast Disorder in Chickens. Abasht et al., 2016. Plos One


Characterization of a novel chicken muscle disorder through differential gene expression and pathway analysis using RNA-sequencing. Mutryn et al., 2015. BMC Genomics

Hypoxia, oxidative stress and intracellular buildup of calcium in Wooden Breast Disease.

Histology and Electron Microscopy

Evidence and role of phlebitis and lipid infiltration in the onset and pathogenesis of Wooden Breast Disease in modern broiler chickens. Papah et al., 2017. Avian Pathology


III. Bioinformatics

3′-UTR Sequencing

The study of gene expression in animals, plants, and microorganisms has been revolutionized in recent years by the use of RNA sequencing (RNA-seq). RNA-seq has been applied to characterize plant and animal phenotypes in a wide variety of experiments. However, due to its high cost, this technology has mainly been used in experiments with a limited number of samples or a large budget. To lower the sequencing cost, 3’-RNA-seq has been proposed as an alternative RNA sequencing method for quantification of gene expression. This method confines sequencing to the 3’-end of mRNA and produces only one fragment per transcript (Fig 1). To compare gene expression measures between 3’-RNA-seq and RNA-seq technologies, we applied the QuantSeq 3′ mRNA sequencing method for RNA quantification and used data from a set of 20 samples that had been used earlier in a RNA-seq study of feed efficiency. We obtained a high correlation (r = 0.92) of the log2 (fold-change) for gene expression between these methods (Fig 2). Overall, the 3’-RNA-seq method sharply reduced the sequencing cost by a factor of ~20, making it applicable to population genetic studies of gene expression such as expression Quantitative Trait Locus (eQTL) mapping.

Fig 1                                                                                  Fig 2