Random Forest as a tumour genetic marker extractor

Identifying tumour genetic markers is an essential task for biomedicine. In this thesis, we analyse a dataset of chromosomal rearrangements of cancer samples and present a methodology for extracting genetic markers from this dataset by using a Random Forest as a feature selection tool

Efficient Identification of Equivalences in Dynamic Graphs and Pedigree Structures

We propose a new framework for designing test and query functions for complex structures that vary across a given parameter such as genetic marker position. The operations we are interested in include equality testing, set operations, isolating unique states, duplication counting, or finding equivalence classes under identifiability constraints. A motivating application is locating equivalence classes in identity-by-descent (IBD) graphs, graph structures in pedigree analysis that change over genetic marker location. The nodes of these graphs are unlabeled and identified only by their connecting edges, a constraint easily handled by our approach. The general framework introduced is powerful enough to build a range of testing functions for IBD graphs, dynamic populations, and other structures using a minimal set of operations. The theoretical and algorithmic properties of our approach are analyzed and proved. Computational results on several simulations demonstrate the effectiveness of our approach.Comment: Code for paper available at http://www.stat.washington.edu/~hoytak/code/hashreduc

Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations

We present a mathematical model, and the corresponding mathematical analysis, that justifies and quantifies the use of principal component analysis of biallelic genetic marker data for a set of individuals to detect the number of subpopulations represented in the data. We indicate that the power of the technique relies more on the number of individuals genotyped than on the number of markers.Comment: Corrected typos in Section 3.1 (M=120, N=2500) and proof of Lemma

Cost-benefit Analysis of a Genetic Marker on Cow-calf Operations Differentiated by Pasture and Breed

Genetic sequencing in beef cattle (Bos taurus L.) is expected to aid producers with selecting breeding stock. Using data from experimental trials conducted with Angus, Brahman, and their reciprocal cross, the single nucleotide polymorphism (SNP) P450 C994G marker expression was investigated for use in selecting genetics suited to grazing endophyte-infected tall fescue (Festuca arundinacea Schreb. L.) compared to bermudagrass (Cynodon dactylon L.) pasture. The study is unique in the sense that actual cow-calf breeding failure rates (open cows were not culled) were tracked from 1991 to 1997 on herds that were bred to calf in spring and were either exposed to fungal endophyte-infected (Acremonium coenophialum L.) tall fescue grazing and hay or not. The study used the Forage and Cattle Analysis and Planning (FORCAP) decision support software to assess economic performance driven by birth weight, weaning weight, and breeding failure rate differences across treatment. Results suggest that for reciprocal cross herds primarily grazing bermudagrass pastures, the P450 C994C genotype (CC) was most favorable; whereas, the P450 G994C genotype (GC) was more profitable with tall fescue. Adding genetic market information when selecting a production strategy led to approximately $15/head in added profitability. In comparison to the prorated cost of$2.40/head over the life of a dam, the collection, interpretation, and management of genetic information under the conditions observed in this study may be worthwhile

Estimation of pedigree errors in the UK dairy population using microsatellite markers and the impact on selection

The proportion of cows in the UK dairy herd whose sires were misidentified was estimated using DNA markers. Genetic marker genotypes were determined on 568 cows (from 168 milk samples and 400 hair samples) and 96 putative sires (from semen samples). The estimated pedigree error rate from the hair samples was 8.8%, and from the milk samples, 13.1%, giving an overall estimate of the error rate of 10%. This level of pedigree errors will have a relatively large impact on the efficiency of progeny testing and the accuracy of cow predicted breeding values. We predict a loss of response to selection of approximately 2 to 3% given this error rate

Amino Acid Diversity on the Basis of Cytochrome B Gene in Kacang and Ettawa Grade Goats

The objectives of study were to identify and assess the amino acid diversity of Cytochrome b (Cyt b) gene, genetic marker and characteristic of specific amino acid in Kacang and Ettawa Grade goat. Nineteen heads of Kacang goat (KG) and twelve heads of Ettawa Grade goat (EG) were purposively sampled. The genomic DNA was isolated by Genomic DNA Mini Kit (Geneaid) and amplified Cyt b using PCR method with CytbCapF and CytbCapR primers and was sequenced. The results showed that there were two specific amino acids that distinguish KG and EG goat with C. hircus and C. aegagrus and four specific amino acids that distinguish KG and EG goat with C. falconeri, but there were no specific amino acids can be used as a genetic marker to distinguish between Kacang and EG goat. In conclusion, specific amino acids in Cyt b gene can be used as a genetic marker among KG and EG goat with 3 goat others comparator