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Title: Insights into the Genetic Structure and Diversity of 38 South Asian Indians from Deep Whole-Genome Sequencing}
Article:
Abstract:
South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language–speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project( SSIP). The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1, 000 Genomes Project( 1 KGP). SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins( Denisovan, Neanderthal) identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.
Introduction:
Next-generation sequencing( NGS) technologies have enabled an entire genome to be sequenced in a cost-effective manner[1], and this has allowed multiple individuals from a population to be surveyed in order to catalogue genetic variants that are present in the population. By adopting an unbiased approach to survey the whole genome, NGS presents a more comprehensive catalogue of different classes of genetic variants with a single assay: from changes that affect only a single base in the genome( single nucleotide polymorphisms, SNPs), to small-size additions and omissions( insertion-deletions, indels); and to larger contiguous changes in the genome that affect either the number of copies of a stretch of genome or differences in the genomic structure( structural variants, SVs). Prior to the advent of NGS, the knowledge of these different classes of variants at the population level has been derived primarily from surveys using pre-designed microarrays, and where the majority focused on SNP variations between populations. The 1000 Genomes Project( 1 KGP) intends to survey more than 2, 500 individuals from at least 20 populations around the world[2], and Phase 1 of the project has already offered valuable insights into the population genetics of 14 populations. While the 1 KGP adopted the approach of sequencing multiple individuals at a lower sequence depth of 2-6X, a recently concluded project in Southeast Asia sequenced 100 Austronesian Malays( the Singapore Sequencing Malay Project, SSMP) at a coverage of at least 30X examined the merits of deep sequencing for a more complete characterization of variants carried by the individuals[3]. The use of NGS has also been successfully extended to sequence ancient hominids such as the Neanderthals and the Denisovans[4],[5],[6], and from well-preserved human tissues from the Ötzi[7] and an aboriginal Australian[8]. South Asia comprises more than 20% of the total world population, of which the majority resides in India[8]. The contemporary demographic makeup of the Indian subcontinent is considerably heterogeneous and is the result of complex human migration and interaction since the first human dispersal out of Africa between 60, 000 to 75, 000 years ago. It was first proposed from the analysis of mitochondria sequences that a single rapid coastal dispersion happened from the Horn of Africa into Southeast Asia and Australasia through the Indian subcontinent[9], and which corroborated with additional analyses of mitochondria in the Andaman and Nicobar Islands[10]. Genome-wide genotyping surveys of geographically well-defined South Asians indicated the presence of complex admixture between populations in the Indian subcontinent[11],[12], which have been proposed to be attributed to the practice of the caste system which encouraged endogamous marriages and the presence of different ethno-linguistic groups – the Indo-Aryan language speaking groups that are primarily found in north India, and the Dravidian language speaking groups that are predominantly found in south India. Singapore is home to more than 350, 000 Indians, comprising 9. 2% of the residing population[13]. These people with ancestry originating from the Indian subcontinent has been designated officially and uniformly as “Indian”, although this can be inadequate given the heterogeneous background of the Indian populace in Singapore, which includes Tamils, Malayalee, Sikh, Hindustani, Punjabi, Sindhi, Hindi, Gujarati, Urdu and Sinhalese[14]. The majority of the Singapore Indians were descended from immigrants from south India who settled in the country after Singapore became a major entrepot trading center in the early 19th century[15],[16]. The SSIP aims to perform whole-genome sequencing of 38 healthy Singapore Indians, to provide insights into the genetic structure and diversity of Asian Indians in Singapore. With a target sequence depth of 30X, the SSIP presents another public resource of deep whole genome sequencing of multiple samples in a well-defined population, as with the SSMP. Here, we evaluated the genetic proximity of the SSIP to the SSMP and 14 populations from Phase 1 of the 1 KGP, and measured the degree of intra-population genetic diversity in each of the 16 populations. The ancestral origins of the SSIP samples were inferred by evaluating the mitochondria and chromosome Y haplogroup memberships of the relevant samples, and we performed an analysis of the relative relatedness between SSIP and two archaic hominins( Denisovan, Neanderthal). The genetic resource for the SSIP is publicly available at http://www. statgen. nus. edu. sg/~SSIP.
Discussion:
The Singapore Sequencing Indian Project has produced a publicly available genomic resource by sequencing the genomes of 36 South Asian Indians from Singapore at a target coverage of 30X. This complements the existing resource of 96 Southeast Asian Malays from the SSMP, as well as 1, 092 samples from Phase 1 of the 1000 Genomes Project. By comparing against a panel of 132 South Asians with well-defined geographical origins, the 36 Singapore Indians were found to be genetically closer to the South Asians from south India. The assignment of Y chromosomes and mitochondria to known haplogroups identified 18% and 22% of the samples respectively to belong to haplogroups that are predominantly present in Europeans, and these memberships were not discernible in the PCA using autosomal SNPs of SSIP with other global populations, even though there were marginally higher degree of Caucasian admixture in these samples that were statistically significant. The PCA of the South Asian samples revealed greater genetic heterogeneity in Asian Indians that originated from northern parts of India, while those that originated from the south were considerably more homogeneous( see Figure 2C). In an independent analysis of HapMap Gujarati Indians with Asian Indians from the Singapore Genome Variation Project, around a third of the Gujarati Indians were found to cluster with the Singapore Indians( Figure S13). With an original sample size of 38, the SSIP is hardly representative of the complex genomic diversity that is present in the Indian subcontinent, nor does it contain geographically well-distributed samples to yield deeper insights into the migratory history of South Asians. Many theories have been proposed on the differentiation between Indo-Aryan and Dravidian-speaking Indians, and we emphasize the SSIP is not intended to investigate the different hypotheses, although it can serve as a potential population panel when more South Asian genetics data become available. Individuals in the SSIP have been sampled from Singapore, a location which is geographically distant from India, and migration and inter-marriages likely will have confounded genetic membership to specific geographical origins in south India, and thus the SSIP is likely to be more representative of cosmopolitan Indians in Singapore. While our assessment of intra-population diversity considers the extent of SNP sharing between pairs of samples in a population, the variance of this metric provides an effective measure of the genetic homogeneity of population labels. For example, the admixed Mexican population exhibited the largest spread in the pairwise distances, even though the median distance for Mexicans was in the same range as with the Caucasians( see Figure 5). This suggests that for subjects labeled as Mexicans, there are pairs of samples where the extent of SNP sharing was as similar as East Asians, while at the same time there are pairs within the population that were significantly more diverse than between pairs of SSIP samples. The downstream implication to such variable degree of intra-population diversity is the interpretation and relevance of population labels as surrogate for genomic information in the practice of public health. An example of this is in warfarin pharmacology. Although polymorphisms in VKORC1 and CYP2C9 can explain up to 70% of dosage variance between populations[25], it is still common to rely on race or ethnic labels to identify the loading dosages when initiating warfarin therapy in the absence of information from genetic screening[25],[26]. A population with a large variance for the intra-population diversity suggests that the adherence to self-reported population labels may serve as a poor surrogate for the underlying pharmacogenomics, which was evident in the larger range of warfarin international normalized ratio( INR) for African Americans and Caucasians, as compared to the Chinese and Japanese( see figure 1 of[26]). The SSIP resource is expected to be a timely complement to Phase 3 of the 1 KGP, which has sequenced the genomes of samples from three additional migrant South Asian groups in Houston( Gujarati Indians) and the United Kingdom( Sri Lankan Tamils, Indian Telegus), as well as two native South Asian groups in Bangladesh( Bengali) and Pakistan( Punjabi). Similar to the Phase 1 design, these five groups have been sequenced at a low coverage of between 2- to 6-fold, and it can be useful to evaluate whether the availability of the SSIP data will benefit the process of variant calling for these populations that have undergone low-pass sequencing. The variants for the 36 SSIP samples are publicly available in the variant call format( VCF), and these can be accessed along with phased haplotypes for the SSIP samples at http://www. statgen. nus. edu. sg/~SSIP.
Indians of South Asia has long been a population of interest to a wide audience , due to its unique diversity . We have deep-sequenced 38 individuals of Indian descent residing in Singapore ( SSIP ) in an effort to illustrate their diversity from a whole-genome standpoint . Indeed , among Asians in our population panel , SSIP was most diverse , followed by the Malays in Singapore ( SSMP ) . Their diversity is further observed in the population's chromosome Y haplogroup and mitochondria haplogroup profiles; individuals with European-dominant haplogroups had greater proportion of European admixture . Among variants ( single nucleotide polymorphism and small insertions/deletions ) discovered in SSIP , 21 . 69% were novel with respect to previous sequencing projects . In addition , some 14 loss-of-function variants ( LOFs ) were associated to cancer , Type II diabetes , and cholesterol levels . Finally , D statistic test with ancient hominids concurred that there was gene flow to East Asians compared to South Asians .