Two complementary depictions of the developmental architecture underlying the genotype–phenotype map for complex traits.

<p>A captures the idea that large amounts of genetic variation funnel to smaller numbers of pathways and processes. These processes then interact to produce structured and modulated phenotypic variation in a complex trait. B, which derives from Wagner <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004724#pgen.1004724-Wagner2" target="_blank">[20]</a>, shows the many-to-many mapping of genes to traits; while both Cs show the modular pattern of gene effects on processes and the effects of processes on sets of phenotypic traits. These depictions illustrate some of the complexity involved in constructing models of genotype–phenotype relations in complex traits.</p

Model images of patterned vegetation

This zip file contains a dataset of images of patterned vegetation produced by a computational model. These images are: (1) raw model output images, (2) images that have been cropped to 50x50 pixels, and (3) binary images derived from these cropped images. These images are all in .tif format. The images show patterns produced at 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3 and 1.4 mm per day

<i>SCHIP1</i> locus associated with centroid size.

<p><b>(A)</b> Regional association plot of centroid size at the <i>SCHIP1</i> locus. Association data are shown using GWAS P-values with the meta-analysis P-value for the lead SNP, rs79909949. The LD pattern is based on the 1000 Genomes Project 2012 African reference and GRCh37/hg19. The estimated recombination rate (cM/Mb) is from HapMap samples. <b>(B)</b> Relative facial size at the upper and lower 95% confidence intervals for centroid size after adjusting for sex and age.</p

Expression of <i>Schip1</i> and <i>Pde8a</i> during mouse embryonic development.

<p>Whole-mount <i>in situ</i> hybridization of <b>(A-D)</b> <i>Schip1</i> and <b>(E-H)</b> <i>Pde8a</i> expression in mouse embryos from E9.5 to E12.5. ba1, first branchial arch (future mandible); ba2, second branchial arch; fb, forebrain; fn, frontonasal process; fl, forelimb; hb, hindbrain; hl, hindlimb; ln, lateronasal process; mb, midbrain; md, mandible; mn, medionasal process; mx, maxilla; ov, otic vesicle.</p

GWAS, replication, and meta-analysis results of replicated association signals.

<p>GWAS, replication, and meta-analysis results of replicated association signals.</p

3D Facial scan with annotated landmarks.

<p>3D facial mesh obtained for each study individual with study landmarks obtained from automatic landmarking overlaid and labeled.</p

World Health Organization 2007 reference BMI Z-scores for the Tanzanian study population.

<p>Histogram of Tanzanian Z-scores calculated from WHO 2007 reference data by age and sex with WHO cutoffs for thinness and overweight classifications.</p

Facial size and shape phenotypes tested for genetic association.

<p>Facial size and shape phenotypes tested for genetic association.</p

<i>SCHIP1</i> locus associated with PC4.

<p><b>(A)</b> Regional association plot of PC4 at the <i>SCHIP1</i> locus. Association data are shown using GWAS P-values. The most associated SNP rs368386044 could not be displayed in the LocusZoom plot, but is in complete linkage disequilibrium with rs9868698. See <b><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006174#pgen.1006174.g003" target="_blank">Fig 3</a></b>legend for details. <b>(B)</b> Morphs showing the range of shape variation along PC4. The heatmap depicts the regions of the face that vary the most between the min and max morphs. Red shows the regions that project most beyond the mean mesh at the positive extreme while yellow is intermediate in that direction. Blue shows the areas that project most inwards from the mean mesh while light blue shows a lesser degree of inwards projection. Green shows those regions that align most closely to the mean mesh.</p

Genome-Wide Association Study Reveals Multiple Loci Influencing Normal Human Facial Morphology

<div><p>Numerous lines of evidence point to a genetic basis for facial morphology in humans, yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features. We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts. Analyses were performed on just under one million genotyped SNPs (Illumina OmniExpress+Exome v1.2 array) imputed to the 1000 Genomes reference panel (Phase 3). We observed genome-wide significant associations (p < 5 x 10⁻⁸) for cranial base width at 14q21.1 and 20q12, intercanthal width at 1p13.3 and Xq13.2, nasal width at 20p11.22, nasal ala length at 14q11.2, and upper facial depth at 11q22.1. Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: <i>MAFB</i>, <i>PAX9</i>, <i>MIPOL1</i>, <i>ALX3</i>, <i>HDAC8</i>, and <i>PAX1</i>. We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in <i>CACNA2D3</i> and <i>PRDM16</i>. These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features. Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis.</p></div