Distribution of third generation siblings included in data by sibship size.

<p>Distribution of third generation siblings included in data by sibship size.</p

A sibling method for identifying vQTLs

<div><p>The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.</p></div

Outcomes versus coefficients.

<p>Outcomes versus coefficients.</p

Manhattan plot of sibling variation in BMI among FHS 3rd generation sibling pairs.

<p>Results for the pairwise sibling standard deviation in BMI regressed against the sibling minor allele count with controls for sex of sibship, mean age of siblings, age difference of siblings, sibling mean BMI, and parental genotype.</p

Raw means of sibling standard deviations (before age, sex, mean of trait, and parental genotype controls) by count of minor alleles.

<p>The graph shows that the sibling standard deviation increases with the count of minor allele snps that have effects on variance only, or effects on both the mean and variance of a trait, while stays flat for snp’s that only effect the mean or that not associated with the trait.</p

Results of sibling standard deviation method across 1000 replicates.

<p>The figure shows that both in the presence and absence of family-level confounding between the genotype and outcome variable, the method, which examines the effect of an additional minor allele in the sibling pair on the trait’s standard deviation, correctly estimates no variance effects (<i>β</i> = 0) when the outcome is simulated to have mean effects only, and correctly detects variance effects (<i>β</i> ≠ 0) when the outcome is simulated to have variance effects only.</p

Enriched canonical pathways for height and BMI sibling-pair standard deviations in FHS, estimated using i-GSEA4GWAS.

<p>Enriched canonical pathways for height and BMI sibling-pair standard deviations in FHS, estimated using i-GSEA4GWAS.</p

Test for spurious association with variance due to non-linear effects on mean levels.

<p>Mean and standard error for height (inches) and BMI among with two minor alleles is shown separately for homozygotes (one sibling with zero minor alleles and the other sibling with two) and heterozygotes (each sibling has one minor allele), for each genome-wide suggestively significant SNP for the respective trait (<b>A</b>. height; <b>B</b>. BMI). One significant SNP for height (rs8029740) is not depicted because there is only one sibling pair with the 1-1 allele combination and 0 sibling pairs with the 0-2 combination. A two-sample t-test for equality of means, estimated separately for each SNP, revealed no significant differences between the two groups for the top hits for each trait.</p

Marsupial family phylogneomics analyses results

Analyses results for each data set, including phylogenetic and dating analyses. For access to target sequences and code used to process the sequencing reads, please see the Bioinformatics directory at https://github.com/duchene/marsupial_family_phylogenomics (linked below)

Analysis of enriched monocyte purity comparing the two monocyte isolation methods conducted in parallel.

<p>(A) Representative forward and side scatter flow diagrams (n = 5) of PBMC (left), CD14 positively isolated monocyte (centre) and untouched monocyte (right) from the same donor. (B) Comparison of monocyte subsets (red) and lymphocyte (blue) in PBMCs and in enriched monocytes, identified using PE-conjugated anti-CD14 antibody and APC-conjugated anti-CD16 antibody. (C) Monocyte and lymphocyte gating strategy highlighting the monocyte singlet gates and doublets to emphasise the difference between the two methods. More extensive gating showing the hierarchical strategy for defining the singlet gates is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180267#pone.0180267.s004" target="_blank">S4 Fig</a>.</p