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Though a genetic basis for non-insulin-dependent diabetes mellitus (NIDDM) is clear, the likely mode of inheritance is not known. The segregation of NIDDM was studied in 64 nuclear South Indian pedigrees (449 individuals) ascertained through an affected proband having both parents and more than 1 sibling alive and available for oral glucose tolerance testing. A high proportion of parents were found to be of abnormal glucose tolerance [89 of 128 (70%) diabetic and 11 of 128 (9%) impaired]. Complex segregation analysis was performed using (1) POINTER which implements the mixed model and distinguishes major gene, multifactorial and non-transmitted environmental contributions to affection and (2) COMDS which implements an oligogenic model with major gene, modifier gene and environmental contributions to a) affection and b) diathesis (an ordered polychotomy amongst non-affected family members, based on 2-h plasma glucose level). Using POINTER, there was no formal support for a major gene and the most parsimonious solutions were achieved with multifactorial models. Using COMDS, we found i) significant improvements in models when information on glucose levels in nondiabetic family members (diathesis) was included, ii) support for segregation of a diallelic gene as well as background familial resemblance, and iii) under the best-supported model, this diallelic locus featured incomplete dominance (d = 0.8) and a disease-predisposing allele frequency of 14%. In South Indians, segregation of NIDDM is inadequately described by simple major gene models: more complex models provide more satisfactory descriptions. This finding, if applicable in other populations, has important implications for the search for diabetes-susceptibility genes.

Original publication




Journal article



Publication Date





1221 - 1230


Blood Glucose, Body Mass Index, Diabetes Mellitus, Type 2, Factor Analysis, Statistical, Fathers, Female, Genes, Dominant, Genes, Recessive, Glucose Intolerance, Glucose Tolerance Test, Humans, India, Male, Middle Aged, Models, Genetic, Models, Statistical, Mothers, Nuclear Family, Pedigree, Prevalence