Annotation of the Bovine (Cow) genome:
Bovine genomics has recently been invigorated by the availability of the whole
genome sequence. Analyses of this data are underway in preparation of several
manuscripts that will be published in tandem with the high impact genome paper.
My association with Bovine functional genomics laboratory at USDA can be useful
for students to help participate in the genome annotation effort and earn high
quality research publications. Research publications on CV will help in establishing
a scientific career.
Algorithms for SNP Discovery from EST and genome sequence data:
Sequence variation is responsible for most of the pheonotypic variation among
individuals such as disease resistance and susceptibility. High throughput sequencing
and genotyping technologies are facilitating in generation of large-scale high
quality sequence data. Analyses of this data for detecting SNPs is an important
step for associating sequence variation with phenotypes. We have recently developed
a new experimental method for high throughput SNP sequencing and development
of new methods for analyzing these data will have high impact.
Bovine Gene Atlas:
Microarray experiments have limitations in detecting the complete range of gene
expression because of the availability of complementary probe sequences on the
chip. This limitation can be overcome by sequencing mRNA using the next generation
sequencers. We have recently completed sequencing of mRNA from 100 different
tissues for cataloging the gene expression and to characterize the differences
observed between different tissue types. This methodology is novel and the analyses
will result in high quality publications.
MicroRNA in Cattle:
The role of microRNAs in gene expression control has been recently recognized.
Most of the microRNAs are evolutionarily conserved. We are sequencing several
cattle libraries to characterize their expression using high throughput next
generation sequencing technologies. Analysis of microRNA expression in tandem
with mRNA expression will lead to interesting results for identifying the potential
targets for the miRNAs and pathways that are being influenced by these small
molecules.
Prediction of genetic merit from Cattle genotype data:
Sequencing of the cow genome and SNP discovery has facilitated the development
of SNP chips for genotyping animals. The genotype data from these animals can
be applied to select superior animals for milk and beef production and can replace
traditional methods of selection. We are currently in the final phase of SNP
chip design and will be genotyping more than 10,000 dairy animals. The genotype
data will then be used to predict the genetic merit of future animals. Various
statistical models will be evaluated for accuracy.
Copy Number Variation in Cattle
A central focus of bovine genetics is to associate phenotype with genetic polymorphisms
to improve the selection for productive efficiency, health and well-being of
cattle. Considerable emphasis is placed on the analysis of bovine single-nucleotide
polymorphisms (SNP) as the main source of genetic and phenotypic variation.
However, over the last few years, it has become apparent that previously unappreciated
genomic structural variation contributes significantly to individual health
and disease in humans and rodents. This type of structural variation ranging
from 1kb to 5Mb comprises copy number variation (insertions and deletions =
1 kb), as well as inversions and translocations spanning millions of nucleotides
of heterogeneity within a genome. These large-scale copy number variations have
been reported at polymorphic levels (7-20% allele frequency) within the human
population. Analyses of these variations have provided compelling evidence of
linkage disequilibrium with flanking SNP. As a complement to the bovine HapMap
project, we propose to assess the normal copy number variation within the bovine
HapMap population using array comparative genomic hybridization (array CGH).
We also propose to characterize such events among cows with higher early embryonic
loss. Our strategy based on genome higher-order architecture variation will
represent a powerful approach for the identification of novel genomic variation
and candidate genes for important economic traits. These structural variation
data will be deposited with the bovine HapMap data housed locally to facilitate
research that enhances four of the five USDA strategic goals. Overall, this
project has great economic potential, features a strong and experienced research
team, and will make effective and timely use of new information emerging from
the bovine genome project.
Lakshmi Kumar Matukumalli 