Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data

Abstract

24The under-representation of several ethnic groups in existing genetic databases and studies 25 have undermined our understanding of the genetic variations and associated traits or diseases 26 in many populations. Cost and technology limitations remain the challenges in performing large-27 scale genome sequencing projects in many developing countries, including Vietnam. As one of 28 the most rapidly adopted genetic tests, non-invasive prenatal testing (NIPT) data offers an 29 alternative untapped resource for genetic studies. Here we performed a large-scale genomic 30 analysis of 2,683 pregnant Vietnamese women using their NIPT data and identified a 31 comprehensive set of 8,054,515 single-nucleotide polymorphisms, among which 8.2% were 32 new to the Vietnamese population. Our study also revealed 24,487 disease-associated genetic 33 variants and their allele frequency distribution, especially 5 pathogenic variants for prevalent 34 genetic disorders in Vietnam. We also observed major discrepancies in the allele frequency 35 distribution of disease-associated genetic variants between the Vietnamese and other 36 populations, thus highlighting a need for genome-wide association studies dedicated to the 37 Vietnamese population. The resulted database of Vietnamese genetic variants, their allele 38 frequency distribution, and their associated diseases presents a valuable resource for future 39 genetic studies. 40

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

“…The use of dense genotyping arrays followed by imputation to a haplotype reference panel has enabled population-scale genome wide associations studies to become routine in characterizing the genetic architecture of complex traits and disease. An alternative technology is low-pass whole genome sequencing followed by imputation, which has successfully used for a number of problems in statistical and population genetics (Tran et al, 2020;Pasaniuc et al, 2012;Gilly et al, 2019), and which was recently shown to recapitulate comparable disease risk stratification performance using a handful of polygenic risk scores derived from imputed array data from an independent cohort (Homburger et al, 2019). We introduced the notion of effective coverage, a quantity which describes the coverage of a sequenced sample under an ideal sampling process and which is more predictive of imputation accuracy than nominal coverage.…”

“…As genome sequencing costs have decreased over the past decade, sequencing-based alternatives to genotyping arrays have been the subject of growing interest (Wetterstrand, 2019). Specifically, low-coverage shotgun whole genome sequencing followed by imputation has been utilized for a number of problems in statistical and population genetics, from providing the backbone for graphbased pangenomes in sorghum to trait mapping in human pharmacogenetics (Tran et al, 2020;Gilly et al, 2019;Rubinacci et al, 2020;Homburger et al, 2019;Wasik et al, 2019;Jensen et al, 2020;Cai et al, 2015a;Liu et al, 2018). As an intuition for why this approach is useful, a sample sequenced at a target coverage of 0.5× is expected to have at least one read on 33 million of the 85 million sites in the 1000 Genomes Phase 3 release, whereas a genotyping array will probe a number of variants which is one to two orders of magnitude fewer, albeit with higher average accuracy (Consortium et al, 2015).…”

“…As an intuition for why this approach is useful, a sample sequenced at a target coverage of 0.5× is expected to have at least one read on 33 million of the 85 million sites in the 1000 Genomes Phase 3 release, whereas a genotyping array will probe a number of variants which is one to two orders of magnitude fewer, albeit with higher average accuracy (Consortium et al, 2015). For many use cases, there are a number of advantages to low-pass sequencing (lps) over genotyping arrays; for example, (1) there is a lack of ascertainment bias with regard to which variants/sites on the genome are assayed, (2) sequence data can be used to discover novel variation both at the sample or population level (such as in Tran et al (2020); Liu et al (2018)), (3) massively parallel low-pass sequencing can be achieved by multiplexing large numbers of samples to reduce cost, and (4) the fact that the average expected accuracy of a sample's imputed genotypes can be fine-tuned by adjusting the target coverage for the sample, something which is useful when designing experiments within real-world logistical or budgeting constraints (see Pasaniuc et al (2012) for a detailed simulation-based cost-benefit analysis of low-pass sequencing compared to genotyping arrays for GWAS study designs).…”

Genetic landscape of recessive diseases in the Vietnamese population from large-scale clinical exome sequencing

“…(1 in 20) of the Vietnamese population. populations, had been reported previously in [9]. Two other autosomal recessive diseases were found with relatively high carrier frequencies, including hemochromatosis type 1 (HFE, 9.4% or syndrome is a multisystem disorder characterized by many clinical features, including developmental delay, intellectual disability and facial dysmorphis.…”

“…Gurdasani et al found that nearly 78% of the participants in genetic studies had European ancestries, whereas the two major populations, Asian and African, only accounted for 11% and 2.4%, respectively [6]. Indeed, genetic research on the Vietnamese population still lags behind other Western and Asian populations, despite recent efforts of genome sequencing projects in the country [8, 9]. There has been no research to study the prevalence of inherited disorders in the Vietnamese population.…”