Results: 5

1.
Figure 2

Figure 2. From: Selection of eating-disorder phenotypes for linkage analysis.

Flow chart for structured approach to variable selection.

Cynthia M. Bulik, et al. Am J Med Genet B Neuropsychiatr Genet. ;139B(1):81-87.
2.
Figure 4

Figure 4. From: Selection of eating-disorder phenotypes for linkage analysis.

Clustered variables for the BN cohort. Dissimilarities are calculated as one minus the squared correlation each pair of variables.

Cynthia M. Bulik, et al. Am J Med Genet B Neuropsychiatr Genet. ;139B(1):81-87.
3.
Figure 3

Figure 3. From: Selection of eating-disorder phenotypes for linkage analysis.

Clustered variables for the AN cohort. Dissimilarities are calculated as one minus the squared correlation each pair of variables.

Cynthia M. Bulik, et al. Am J Med Genet B Neuropsychiatr Genet. ;139B(1):81-87.
4.
Figure 5

Figure 5. From: Selection of eating-disorder phenotypes for linkage analysis.

ASP values for food obsessions (top) and age at menarche for the AN (left) and BN cohorts.

Cynthia M. Bulik, et al. Am J Med Genet B Neuropsychiatr Genet. ;139B(1):81-87.
5.
Figure 1

Figure 1. From: Selection of eating-disorder phenotypes for linkage analysis.

Idealized distributions for a quantitative trait amenable to QTL linkage analysis (a) and a trait that identifies populations in the sample (b). We refer to the distribution in 1a as “classical” because the quantitative trait values are normally distributed (mean μ = 0, standard deviation σ = 1) and show correlation ρ = 0.7 between family members, in this case siblings whose trait values are randomly sampled under the specified model (1a, right). Correlation typically would be measured as the intraclass correlation, which formally is the ratio of the between-family component of variance divided by the total variance of the trait. We describe the trait distribution in 1b as a trait that clusters families, which can be seen using simple diagnostics plotting covariate values for one sibling against the other (1b, right). Formally the model generating these data is a mixture model, in which fraction 1-π = 0.6 families are randomly sampled with trait values μ = 0, σ = 1, and ρ = 0.2 and fraction π = 0.4 families are randomly sampled with μ = 3.75, σ = 1, and ρ = 0.2. Diagnostics combined with formal tests are often used to determine the presence of a mixture.

Cynthia M. Bulik, et al. Am J Med Genet B Neuropsychiatr Genet. ;139B(1):81-87.

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