Table 1 Examples of regressions and expected regression coefficients for different data types

From: Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Observed genotypes

Yi1 regressed on

\({\it{E}}\left( {{\it{\hat \uptheta }}} \right)\)

Proband (Fig. 1h)

gi1

δ + (αp + αm + ηs)/2

Sibling pairs (Fig. 1e)

gi1

δ

gi2

ηs

\(\hat g_{{{{\mathrm{par(i)}}}}}\)

(αp + αm − ηs)/2

Father–child pairs (Fig. 1c)

gi1

δ

gp(i)

αp

\(\hat g_{{{{\mathrm{m(i)}}}}}\)

αm

Mother–child pairs (Fig. 1d)

gi1

δ

\(\hat g_{p\left( i \right)}\)

αp

gm(i)

αm

Trios (Fig. 1b)

gi1

δ

gp(i)

αp

gm(i)

αm

Quads (Fig. 1a)

gi1

δ

gi2

ηs

gp(i)

αp− ηs/2

gm(i)

αm − ηs/2

  1. In the first column, we give the data type in terms of the observed genotypes in the nuclear family, referencing the relevant panel of Fig. 1; in the second column, we give an example of a regression that could be performed using that data type and parental genotypes imputed from the observed genotypes; in the third column, we give the expected column vector of regression coefficients from performing the regression. Yi1 is the phenotype of sibling 1 in family i; gij is the genotype of sibling j in family i; gp(i) is the paternal genotype, and \(\hat g_{p(i)}\) is the imputed paternal genotype; gm(i) is the maternal genotype, and \(\hat g_{m(i)}\) is the imputed maternal genotype; \(\hat g_{{{{\mathrm{par(i)}}}}}\) is the imputed sum of maternal and paternal genotypes; δ is the direct effect; ηs is the indirect sibling effect; and αp and αm are, respectively, the paternal and maternal NTCs.