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Fulltext A genome wide pattern of population structure and admixture in peninsular Malaysia Malays

Hatin WI, Nur-Shafawati AR, Etemad A, Jin W, Qin P, Xu S, et al.

Hugo J, 2014 Dec;8(1):5.
PMID: 27090253 DOI: 10.1186/s11568-014-0005-z

BACKGROUND: The Malays consist of various sub-ethnic groups which are believed to have different ancestral origins based on their migrations centuries ago. The sub-ethnic groups can be divided based on the region they inhabit; the northern (Melayu Kedah and Melayu Kelantan), western (Melayu Minang) and southern parts (Melayu Bugis and Melayu Jawa) of Peninsular Malaysia. We analyzed 54,794 autosomal single nucleotide polymorphisms (SNPs) which were shared by 472 unrelated individuals from 17 populations to determine the genetic structure and distributions of the ancestral genetic components in five Malay sub-ethnic groups namely Melayu Bugis, Melayu Jawa, Melayu Minang, Melayu Kedah, and Melayu Kelantan. We also have included in the analysis 12 other study populations from Thailand, Indonesia, China, India, Africa and Orang Asli sub-groups in Malay Peninsula, obtained from the Pan Asian SNP Initiative (PASNPI) Consortium and International HapMap project database.
RESULTS: We found evidence of genetic influx from Indians to Malays, more in Melayu Kedah and Melayu Kelantan which are genetically different from the other Malay sub-ethnic groups, but similar to Thai Pattani. More than 98% of these northern Malays haplotypes could be found in either Indians or Chinese populations, indicating a highly admixture pattern among populations. Nevertheless, the ancestry lines of Malays, Indonesians and Thais were traced back to have shared a common ancestor with the Proto-Malays and Chinese.
CONCLUSIONS: These results support genetic admixtures in the Peninsular Malaysia Malay populations and provided valuable information on the enigmatic demographical history as well as shed some insights into the origins of the Malays in the Malay Peninsula.

Matched MeSH terms: HapMap Project
Fulltext Dissecting the genetic structure and admixture of four geographical Malay populations

Deng L, Hoh BP, Lu D, Saw WY, Twee-Hee Ong R, Kasturiratne A, et al.

Sci Rep, 2015 Sep 23;5:14375.
PMID: 26395220 DOI: 10.1038/srep14375

The Malay people are an important ethnic composition in Southeast Asia, but their genetic make-up and population structure remain poorly studied. Here we conducted a genome-wide study of four geographical Malay populations: Peninsular Malaysian Malay (PMM), Singaporean Malay (SGM), Indonesian Malay (IDM) and Sri Lankan Malay (SLM). All the four Malay populations showed substantial admixture with multiple ancestries. We identified four major ancestral components in Malay populations: Austronesian (17%-62%), Proto-Malay (15%-31%), East Asian (4%-16%) and South Asian (3%-34%). Approximately 34% of the genetic makeup of SLM is of South Asian ancestry, resulting in its distinct genetic pattern compared with the other three Malay populations. Besides, substantial differentiation was observed between the Malay populations from the north and the south, and between those from the west and the east. In summary, this study revealed that the genetic identity of the Malays comprises a mixed entity of multiple ancestries represented by Austronesian, Proto-Malay, East Asian and South Asian, with most of the admixture events estimated to have occurred 175 to 1,500 years ago, which in turn suggests that geographical isolation and independent admixture have significantly shaped the genetic architectures and the diversity of the Malay populations.

Matched MeSH terms: HapMap Project
Ancestry-informative marker (AIM) SNP panel for the Malay population

Yahya P, Sulong S, Harun A, Wangkumhang P, Wilantho A, Ngamphiw C, et al.

Int J Legal Med, 2020 Jan;134(1):123-134.
PMID: 31760471 DOI: 10.1007/s00414-019-02184-0

Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.

Matched MeSH terms: HapMap Project
Copy number polymorphisms in new HapMap III and Singapore populations

Ku CS, Teo SM, Naidoo N, Sim X, Teo YY, Pawitan Y, et al.

J Hum Genet, 2011 Aug;56(8):552-60.
PMID: 21677662 DOI: 10.1038/jhg.2011.54

Copy number variations can be identified using newer genotyping arrays with higher single nucleotide polymorphisms (SNPs) density and copy number probes accompanied by newer algorithms. McCarroll et al. (2008) applied these to the HapMap II samples and identified 1316 copy number polymorphisms (CNPs). In our study, we applied the same approach to 859 samples from three Singapore populations and seven HapMap III populations. Approximately 50% of the 1291 autosomal CNPs were found to be polymorphic only in populations of non-African ancestry. Pairwise comparisons among the 10 populations showed substantial differences in the CNPs frequencies. Additionally, 698 CNPs showed significant differences with false discovery rate (FDR)<0.01 among the 10 populations and these loci overlap with known disease-associated or pharmacogenetic-related genes such as CFHR3 and CFHR1 (age related macular degeneration), GSTTI (metabolism of various carcinogenic compounds and cancers) and UGT2B17 (prostate cancer and graft-versus-host disease). The correlations between CNPs and genome-wide association studies-SNPs were investigated and several loci, which were previously unreported, that may potentially be implicated in complex diseases and traits were found; for example, childhood acute lymphoblastic leukaemia, age-related macular degeneration, breast cancer, response to antipsychotic treatment, rheumatoid arthritis and type-1 diabetes. Additionally, we also found 5014 novel copy number loci that have not been reported previously by McCarroll et al. (2008) in the 10 populations.

Matched MeSH terms: HapMap Project