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Biomedical subjects

M G Ehm

Publications and source records attributed to M G Ehm.

2 recordsLinked to original sources

Autosomal genomic scan for loci linked to obesity and energy metabolism in Pima Indians.

An autosomal genomic scan to search for linkage to obesity and energy metabolism was completed in Pima Indians, a population prone to obesity. Obesity was assessed by percent body fat (by hydrodensitometry) and fat distribution (the ratio of waist circumference to thigh circumference). Energy metabolism was measured in a respiratory chamber as 24-h metabolic rate, sleeping metabolic rate, and 24-h respiratory quotient (24RQ), an indicator of the ratio of carbohydrate oxidation to fat oxidation. Five hundred sixteen microsatellite markers with a median spacing of 6.4 cM were analyzed, in 362 siblings who had measurements of body composition and in 220 siblings who had measurements of energy metabolism. These comprised 451 sib pairs in 127 nuclear families, for linkage analysis to obesity, and 236 sib pairs in 82 nuclear families, for linkage analysis to energy metabolism. Pointwise and multipoint methods for regression of sib-pair differences in identity by descent, as well as a sibling-based variance-components method, were used to detect linkage. LOD scores >=2 were found at 11q21-q22, for percent body fat (LOD=2.1; P=.001), at 11q23-q24, for 24-h energy expenditure (LOD=2.0; P=.001), and at 1p31-p21 (LOD=2.0) and 20q11.2 (LOD=3.0; P=.0001), for 24RQ, by pointwise and multipoint analyses. With the variance-components method, the highest LOD score (LOD=2.3 P=.0006) was found at 18q21, for percent body fat, and at 1p31-p21 (LOD=2.8; P=.0003), for 24RQ. Possible candidate genes include LEPR (leptin receptor), at 1p31, and ASIP (agouti-signaling protein), at 20q11.2.

Chromosome Mapping

Error detection for genetic data, using likelihood methods.

As genetic maps become denser, the effect of laboratory typing errors becomes more serious. We review a general method for detecting errors in pedigree genotyping data that is a variant of the likelihood-ratio test statistic. It pinpoints individuals and loci with relatively unlikely genotypes. Power and significance studies using Monte Carlo methods are shown by using simulated data with pedigree structures similar to the CEPH pedigrees and a larger experimental pedigree used in the study of idiopathic dilated cardiomyopathy (DCM). The studies show the index detects errors for small values of theta with high power and an acceptable false positive rate. The method was also used to check for errors in DCM laboratory pedigree data and to estimate the error rate in CEPH-chromosome 6 data. The errors flagged by our method in the DCM pedigree were confirmed by the laboratory. The results are consistent with estimated false-positive and false-negative rates obtained using simulation.

Cardiomyopathy, Dilated