Exact genetic linkage computations for general pedigrees

MOTIVATION Genetic linkage analysis is a useful statistical tool for mapping disease genes and for associating functionality of genes with their location on the chromosome. There is a need for a program that computes multipoint likelihood on general pedigrees with many markers that also deals with two-locus disease models. RESULTS In this paper we present algorithms for performing exact multipoint likelihood calculations on general pedigrees with a large number of highly polymorphic markers, taking into account a variety of disease models. We have implemented these algorithms in a new computer program called SUPERLINK which outperforms leading linkage software with regards to functionality, speed, memory requirements and extensibility.

[1]  R. Elston,et al.  A general model for the genetic analysis of pedigree data. , 1971, Human heredity.

[2]  Stefan Arnborg,et al.  Efficient algorithms for combinatorial problems on graphs with bounded decomposability — A survey , 1985, BIT.

[3]  K. Lange,et al.  An algorithm for automatic genotype elimination. , 1987, American journal of human genetics.

[4]  Derek G. Corneil,et al.  Complexity of finding embeddings in a k -tree , 1987 .

[5]  E. Lander,et al.  Construction of multilocus genetic linkage maps in humans. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[7]  N. Risch Linkage strategies for genetically complex traits. I. Multilocus models. , 1990, American journal of human genetics.

[8]  N. Risch Linkage strategies for genetically complex traits. II. The power of affected relative pairs. , 1990, American journal of human genetics.

[9]  A A Schäffer,et al.  Faster sequential genetic linkage computations. , 1993, American journal of human genetics.

[10]  N. Schork,et al.  Two-trait-locus linkage analysis: a powerful strategy for mapping complex genetic traits. , 1993, American journal of human genetics.

[11]  Ross D. Shachter,et al.  Global Conditioning for Probabilistic Inference in Belief Networks , 1994, UAI.

[12]  A A Schäffer,et al.  Avoiding recomputation in linkage analysis. , 1994, Human heredity.

[13]  Nevin L. Zhang,et al.  A simple approach to Bayesian network computations , 1994 .

[14]  Jurg Ott,et al.  Handbook of Human Genetic Linkage , 1994 .

[15]  M. Daly,et al.  Rapid multipoint linkage analysis of recessive traits in nuclear families, including homozygosity mapping. , 1995, American journal of human genetics.

[16]  J. O’Connell,et al.  The VITESSE algorithm for rapid exact multilocus linkage analysis via genotype set–recoding and fuzzy inheritance , 1995, Nature Genetics.

[17]  L Kruglyak,et al.  Parametric and nonparametric linkage analysis: a unified multipoint approach. , 1996, American journal of human genetics.

[18]  Rina Dechter,et al.  Bucket elimination: A unifying framework for probabilistic inference , 1996, UAI.

[19]  Michael I. Jordan Learning in Graphical Models , 1999, NATO ASI Series.

[20]  Michael I. Jordan Graphical Models , 1998 .

[21]  D. Schaid Mathematical and Statistical Methods for Genetic Analysis , 1999 .

[22]  Nicolas Produit,et al.  Parametric and nonparametric multipoint linkage analysis with imprinting and two-locus-trait models: application to mite sensitization. , 2000, American journal of human genetics.

[23]  Jeffrey R. O’Connell,et al.  Rapid Multipoint Linkage Analysis via Inheritance Vectors in the Elston-Stewart Algorithm , 2001, Human Heredity.

[24]  Rina Dechter,et al.  Topological parameters for time-space tradeoff , 1996, Artif. Intell..