Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the intermixing (admixture) of Native Americans, Europeans and Sub-Saharan Africans. Using novel haplotype-based methods, here we infer sub-continental ancestry in over 6,500 Latin Americans and evaluate the impact of regional ancestry variation on physical appearance. We find that Native American ancestry components in Latin Americans correspond geographically to the present-day genetic structure of Native groups, and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming mostly from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that ancestry related to highland (Central Andean) versus lowland (Mapuche) Natives is associated with variation in facial features, particularly nose morphology, and detect significant differences in allele frequencies between these groups at loci previously associated with nose morphology in this sample.

The human face represents a combined set of highly heritable phenotypes, but knowledge on its genetic architecture remains limited, despite the relevance for various fields. A series of genome-wide association studies on 78 facial shape phenotypes quantified from 3-dimensional facial images of 10,115 Europeans identified 24 genetic loci reaching study-wide suggestive association (p < 5 × 10⁻⁸), among which 17 were previously unreported. A follow-up multi-ethnic study in additional 7917 individuals confirmed 10 loci including six unreported ones (p_adjusted < 2.1 × 10⁻³). A global map of derived polygenic face scores assembled facial features in major continental groups consistent with anthropological knowledge. Analyses of epigenomic datasets from cranial neural crest cells revealed abundant cis-regulatory activities at the face-associated genetic loci. Luciferase reporter assays in neural crest progenitor cells highlighted enhancer activities of several face-associated DNA variants. These results substantially advance our understanding of the genetic basis underlying human facial variation and provide candidates for future in-vivo functional studies.

We carried out a genome-wide association study in Latin Americans and identified novel face morphology loci..

We report a genome-wide association study of facial features in >6000 Latin Americans based on automatic landmarking of 2D portraits and testing for association with inter-landmark distances. We detected significant associations (P-value <5 × 10⁻⁸) at 42 genome regions, nine of which have been previously reported. In follow-up analyses, 26 of the 33 novel regions replicate in East Asians, Europeans, or Africans, and one mouse homologous region influences craniofacial morphology in mice. The novel region in 1q32.3 shows introgression from Neanderthals and we find that the introgressed tract increases nasal height (consistent with the differentiation between Neanderthals and modern humans). Novel regions include candidate genes and genome regulatory elements previously implicated in craniofacial development, and show preferential transcription in cranial neural crest cells. The automated approach used here should simplify the collection of large study samples from across the world, facilitating a cosmopolitan characterization of the genetics of facial features.

Visualisation of GWAS summary statistics, specifically P-values, as Manhattan plots are widespread in GWAS publications, and many popular software tools are available, such as the R package qqman. However, there is a substantial need for further development, such as handling datasets from non-model organisms. We provide a new R package, fastman, with major additional capabilities compared to currently available packages. For example, it handles datasets involving genomes from non-model organisms, even at a draft stage consisting of numerous contigs and scaffolds (shorter stretches of assembled DNA sequences) that have not been compiled into chromosomes. Non-numeric chromosome IDs are also supported. Additionally, our package has the capability of plotting other genetic scores, such as other GWAS statistics (e.g. regression betas or odds ratios), fixation index( F _ST ), D-statistics, and various selection statistics, such as PBS. Importantly, negative or two-tailed values are supported in this package. In our package, we implement a heuristic algorithm that drastically reduces plotting time for huge datasets without losing visual precision, allowing for many different data types and missing data. We also provide substantial additional flexibility in highlighting and annotation. The package can produce plots directly from association outputs by PLINK . Alternatively, it can produce plots from any R data frame with custom columns and can handle large datasets to generate plots rapidly. It is available for public use at https://github.com/adhikari-statgen-lab/fastman .