Monte Carlo methods in genetic analysis

Conference · Tue Dec 31 04:00:00 EST 1996

OSTI ID:495275

Lin, Shili ^[1]

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1. Univ. of California, Berkeley, CA (United States)

Many genetic analyses require computation of probabilities and likelihoods of pedigree data. With more and more genetic marker data deriving from new DNA technologies becoming available to researchers, exact computations are often formidable with standard statistical methods and computational algorithms. The desire to utilize as much available data as possible, coupled with complexities of realistic genetic models, push traditional approaches to their limits. These methods encounter severe methodological and computational challenges, even with the aid of advanced computing technology. Monte Carlo methods are therefore increasingly being explored as practical techniques for estimating these probabilities and likelihoods. This paper reviews the basic elements of the Markov chain Monte Carlo method and the method of sequential imputation, with an emphasis upon their applicability to genetic analysis. Three areas of applications are presented to demonstrate the versatility of Markov chain Monte Carlo for different types of genetic problems. A multilocus linkage analysis example is also presented to illustrate the sequential imputation method. Finally, important statistical issues of Markov chain Monte Carlo and sequential imputation, some of which are unique to genetic data, are discussed, and current solutions are outlined. 72 refs.

• Research Organization: Minnesota Univ., Minneapolis, MN (United States). Inst. for Mathematics and Its Applications
• OSTI ID: 495275
• Report Number(s): CONF-9407205--Vol.81; CNN: Grant R01 HG01093-01
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 GENERAL AND MISCELLANEOUS
BIOLOGICAL MARKERS
DNA SEQUENCING
GENES
GENETIC MAPPING
GENETICS
GENOTYPE
HUMAN CHROMOSOMES
L CODES
MONTE CARLO METHOD
PROBABILISTIC ESTIMATION
STATISTICAL MODELS