Extended Data Figure 6: Frequency distribution of the risk haplotype and dendrogram depicting clustering with Neanderthal haplotypes. | Nature

Extended Data Figure 6: Frequency distribution of the risk haplotype and dendrogram depicting clustering with Neanderthal haplotypes.

From: Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico

Extended Data Figure 6

a, Allele frequency of missense SNP rs117767867 (tag for risk haplotype) in the 1000 Genomes Phase I data set. b, Dendrogram generated from haplotypes across the 73-kb Neanderthal introgressed region. Nodes for modern human haplotypes are labelled in red or blue with the 1000 Genomes population in which the corresponding haplotype resides. Archaic Neanderthal sequences are labelled in black and include the low-coverage Neanderthal sequence14 (labelled Vindija), and the unpublished Neanderthal sequence that is homozygous for the 5 SNP risk haplotype17 (Altai). H1 includes haplotypes from MXL and FIN, and H2 and H3 both include haplotypes from CLM, MXL, CHB and ASW. Modern human sequences included are all 1000 Genomes Phase I samples that are homozygous for the 5 SNP risk haplotype (n = 15), and 16 non-risk haplotypes—four haplotypes (from two randomly selected individuals) from each of the CLM (Colombian in Medellin, Colombia), MXL (Mexican Ancestry in Los Angeles, California), CHB (Han Chinese in Beijing, China) and FIN (Finnish in Finland) 1000 Genomes populations (the populations with carriers of the 5 SNP haplotype). The red subtree depicts the Neanderthal clade, with all risk haplotypes clustering with the Altai and Vindija sequences. In blue are all other modern human haplotypes. The dendrogram was generated by the R function hclust using a complete linkage clustering algorithm on a distance matrix measuring the fraction of SNPs called in the 1000 Genomes project at which a pair of haplotypes differs (the y axis represents this distance). Because haplotypes are unavailable for the archaic samples, we picked a random allele to compute the distance matrix.

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