BackgroundAustronesian is a linguistic family spread in most areas of the Southeast Asia, the Pacific Ocean, and the Indian Ocean. Based on their linguistic similarity, this linguistic family included Malayo-Polynesians and Taiwan aborigines. The linguistic similarity also led to the controversial hypothesis that Taiwan is the homeland of all the Malayo-Polynesians, a hypothesis that has been debated by ethnologists, linguists, archaeologists, and geneticists. It is well accepted that the Eastern Austronesians (Micronesians and Polynesians) derived from the Western Austronesians (Island Southeast Asians and Taiwanese), and that the Daic populations on the mainland are supposed to be the headstream of all the Austronesian populations.ResultsIn this report, we studied 20 SNPs and 7 STRs in the non-recombining region of the 1,509 Y chromosomes from 30 China Daic populations, 23 Indonesian and Vietnam Malayo-Polynesian populations, and 11 Taiwan aboriginal populations. These three groups show many resemblances in paternal lineages. Admixture analyses demonstrated that the Daic populations are hardly influenced by Han Chinese genetically, and that they make up the largest proportion of Indonesians. Most of the population samples contain a high frequency of haplogroup O1a-M119, which is nearly absent in other ethnic families. The STR network of haplogroup O1a* illustrated that Indonesian lineages did not derive from Taiwan aborigines as linguistic studies suggest, but from Daic populations.ConclusionWe show that, in contrast to the Taiwan homeland hypothesis, the Island Southeast Asians do not have a Taiwan origin based on their paternal lineages. Furthermore, we show that both Taiwan aborigines and Indonesians likely derived from the Daic populations based on their paternal lineages. These two populations seem to have evolved independently of each other. Our results indicate that a super-phylum, which includes Taiwan aborigines, Daic, and Malayo-Polynesians, is genetically educible.

Mitochondrial DNA (mtDNA) polymorphism has been studied systematically in the Han, Tibeto-Buman, and Hmong-Mien ethnic families of southern East Asia. Only two families in this region, Daic and Austro-Asiatic, were still uninvestigated. Daic is a major ethnic family in South China and Southeast Asia and has a long history. To study mtDNA polymorphism within this family, all the Daic populations of China and some of Vietnam (774 individuals from 30 populations) were typed by HVS-1 region sequencing and by PCR-RFLP assays. The observed high Southern type frequencies (B, F, M7, R) confirmed Daic as a typical Southern group. mtDNAs of other populations (126 individuals from 14 populations) from Austro-Asiatic ethnic families neighboring the Daic were also typed. Networks of mtDNA haplogroups in South China were traced from these new data and those from the literature. Ethnic families share many haplogroups, indicating their common origin. However, the two largest families in South China, Daic, and Hmong-Mien, polarized into several ethnic family specific haplogroups. Haplogroup ages were estimated in the networks of high-frequency haplogroups (B, F, M7, R), and they were found to originate about 50,000 years ago. In contrast, ethnic family specific haplogroups all originated around 20,000 years ago. We therefore conclude that modern humans have lived in South China for a long time, inside-ethnogenesis was a rather late event, and frequent inmixing was taking place throughout. MtDNA data of Daic, Austro-Asiatic and other populations in South China has therefore proven pivotal for studying the human history of East Asia.

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications.

Background: Allergic diseases often occur in combination (multimorbidity). Human blood transcriptome studies have not addressed multimorbidity. Large-scale gene expression data were combined to retrieve biomarkers and signaling pathways to disentangle allergic multimorbidity phenotypes. Methods: Integrated transcriptomic analysis was conducted in 1233 participants with a discovery phase using gene expression data (Human Transcriptome Array 2.0) from whole blood of 786 children from three European birth cohorts (MeDALL), and a replication phase using RNA Sequencing data from an independent cohort (EVA-PR, n = 447). Allergic diseases (asthma, atopic dermatitis, rhinitis) were considered as single disease or multimorbidity (at least two diseases), and compared with no disease. Results: Fifty genes were differentially expressed in allergic diseases. Thirty-two were not previously described in allergy. Eight genes were consistently overexpressed in all types of multimorbidity for asthma, dermatitis, and rhinitis (CLC, EMR4P, IL5RA, FRRS1, HRH4, SLC29A1, SIGLEC8, IL1RL1). All genes were replicated the in EVA-PR cohort. RT-qPCR validated the overexpression of selected genes. In MeDALL, 27 genes were differentially expressed in rhinitis alone, but none was significant for asthma or dermatitis alone. The multimorbidity signature was enriched in eosinophil-associated immune response and signal transduction. Protein-protein interaction network analysis identified IL5/JAK/STAT and IL33/ST2/IRAK/TRAF as key signaling pathways in multimorbid diseases. Synergistic effect of multimorbidity on gene expression levels was found. Conclusion: A signature of eight genes identifies multimorbidity for asthma, rhinitis, and dermatitis. Our results have clinical and mechanistic implications, and suggest that multimorbidity should be considered differently than allergic diseases occurring alone.