Stephen E. Lincoln

MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations.

With the advent of RFLPs, genetic linkage maps are now being assembled for a number of organisms including both inbred experimental populations such as maize and outbred natural populations such as humans. Accurate construction of such genetic maps requires multipoint linkage analysis of particular types of pedigrees. We describe here a computer package, called MAPMAKER, designed specifically for this purpose. The program uses an efficient algorithm that allows simultaneous multipoint analysis of any number of loci. MAPMAKER also includes an interactive command language that makes it easy for a geneticist to explore linkage data. MAPMAKER has been applied to the construction of linkage maps in a number of organisms, including the human and several plants, and we outline the mapping strategies that have been used.

Genetic mapping of a gene causing hypertension in the stroke-prone spontaneously hypertensive rat.

The stroke-prone spontaneously hypertensive rat (SHRSP) is a well-characterized model for primary hypertension in humans. High blood pressure in SHRSP shows polygenic inheritance, but none of the loci responsible have previously been identified. To locate genes controlling this quantitative trait, we mapped a large collection of DNA polymorphisms in a cross between SHRSP and the normotensive WKY strain. Here we report strong genetic evidence that a gene, Bp1, having a major effect on blood pressure maps to rat chromosome 10 with a LOD score of 5.10 and is closely linked to the rat gene encoding angiotensin-converting enzyme (ACE), an enzyme that plays a major role in blood pressure homeostasis and is an important target of anti-hypertensive drugs. We also find significant, albeit weaker, linkage to a locus, Bp2, on chromosome 18. We discuss the implications of genetic dissection of quantitative disease-related phenotypes in mammals.

Systematic detection of errors in genetic linkage data

Construction of dense genetic linkage maps is hampered, in practice, by the occurrence of laboratory typing errors. Even relatively low error rates cause substantial map expansion and interfere with the determination of correct genetic order. Here, we describe a systematic method for overcoming these difficulties, based on incorporating the possibility of error into the usual likelihood model for linkage analysis. Using this approach, it is possible to construct genetic maps allowing for error and to identify the typings most likely to be in error. The method has been implemented for F2 intercrosses between two inbred strains, a situation relevant to the construction of genetic maps in experimental organisms. Tests involving both simulated and real data are presented, showing that the method detects the vast majority of errors.