PMC:4564992 / 10844-12006 JSONTXT

{"project":"2_test","denotations":[{"id":"26320892-21181895-2052527","span":{"begin":92,"end":94},"obj":"21181895"},{"id":"26320892-22738121-2052527","span":{"begin":92,"end":94},"obj":"22738121"},{"id":"26320892-22738121-2052528","span":{"begin":1160,"end":1162},"obj":"22738121"}],"text":"For an overview of the methodology implemented in PREMIM and EMIM, see our previous work.20,21 Here, we shall describe only the essential components relevant to the current manuscript. EMIM uses genotype counts from pedigree data to estimate relative-risk parameters through the use of multinomial modeling. The accompanying program PREMIM pre-processes the pedigree data to supply EMIM with the required genotype count information. Parameters estimable by EMIM include child genotype effects R1 and R2 (the relative risks conferred by the presence of one or two copies of the risk allele in the child), maternal-genotype effects S1 and S2 (the relative risks conferred by the presence of one or two copies of the risk allele in the mother), and maternal and paternal parent-of-origin (or, imprinting) parameters Im and Ip, respectively, which correspond to the factor by which a child’s disease risk is multiplied if they inherit a risk allele from their mother or father. EMIM calculates a log likelihood at each SNP of interest, on the basis of the chosen parameters and assumptions, such as Hardy-Weinberg equilibrium or conditioning on parental genotypes.21"}