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The Asian Diversity Project (ADP) assembled 37 cosmopolitan and ethnic minority populations in Asia that have been densely genotyped across over half a million markers to study patterns of genetic diversity and positive natural selection. We performed population structure analyses of the ADP populations and divided these populations into four major groups based on their genographic information. By applying a highly sensitive algorithm haploPS to locate genomic signatures of positive selection, 140 distinct genomic regions exhibiting evidence of positive selection in at least one population were identified. We examined the extent of signal sharing for regions that were selected in multiple populations and observed that populations clustered in a similar fashion to that of how the ancestry clades were phylogenetically defined. In particular, populations predominantly located in South Asia underwent considerably different adaptation as compared with populations from the other geographical regions. Signatures of positive selection present in multiple geographical regions were predicted to be older and have emerged prior to the separation of the populations in the different regions. In contrast, selection signals present in a single population group tended to be of lower frequencies and thus can be attributed to recent evolutionary events.

Pharmacogenomics has been lauded as an important innovation in clinical medicine as a result of advances in genomic science. As one of the cornerstones in precision medicine, the vision to determine the right medication in the right dosage for the right treatment with the use of genetic information has not exactly materialised, and few genetic tests have been implemented as the standard of care in health systems worldwide. Here we review the findings from a SWOT analysis to examine the strengths, weaknesses, opportunities and threats around the role of pharmacogenetics in public health and clinical health care, at the micro, meso and macro levels corresponding to the perspectives of the individuals (scientists, patients and physicians), the health-care institutions and the health systems, respectively.

Investigating how an ancestral population diverges to give rise to distinct subpopulations remains a fundamental pursuit in population genetics. There is broad consensus for the 'Out-of-Africa' hypothesis that states that modern humans arose ∼200 000 years ago in Africa and spread throughout the continent ∼100 000 years ago. This was followed by several waves of major population dispersals across the globe, although the exact nature of the population divergence remains debatable. Existing methods to estimate population divergence time differ in their methodological frameworks and demographic assumptions, and require different types of genetic data as input. These fundamental differences often result in the methods producing inconsistent estimates of the population divergence time, further confounding attempts to robustly uncover the history of human migration, especially when most population genetic studies do not employ multiple methods to estimate the time to the most recent common ancestor (TMRCA). Here, we chose eight popular methods for estimating TMRCA and evaluated their robustness and accuracy in correctly identifying the true TMRCA through a series of simulations that mimicked different evolutionary scenarios. We subsequently applied all eight methods to estimate the population divergence time between Southeast Asian Malays and South Asian Indians using deep whole-genome sequencing data.

There has been limited success in identifying causal variants underlying association signals observed in genome-wide association studies (GWAS). The use of 1000 Genomes Project (1KGP) allows the imputation to estimate the genetic information at untyped variants. However, long stretches of high linkage disequilibrium within the genome prevent us from differentiating between causal variants and perfect surrogates, thus limiting our ability to identify causal variants. Transethnic strategies have been proposed as a possible solution to mitigate this. However, these studies generally rely on imputing genotypes from multiple ancestries from 1KGP but not against population-specific reference panels. Here, we perform the first transethnic fine-mapping study across three Asian cohorts from diverse ancestries at the loci implicated with eye and blood lipid traits, using population-specific reference panels that have been generated by whole-genome sequencing samples from the same ancestry groups. Our study outlines several challenges faced in a fine-mapping exercise where one simply aims to meta-analyse existing GWAS that have been imputed against reference haplotypes from the 1KGP.

While genome-wide association studies have discovered numerous bona fide variants that are associated with common diseases and complex traits; these variants tend to be common in the population and explain only a small proportion of the phenotype variance. The search for the missing heritability has thus switched to rare and low-frequency variants, defined as <5% in the population, but which are expected to have a bigger impact on phenotypic outcomes. The rarer nature of these variants coupled with the curse of testing multiple variants across the genome meant that large sample sizes will still be required despite the assumption of bigger effect sizes. Combining data from multiple studies in a meta-analysis will continue to be the natural approach in boosting sample sizes. However, the population genetics of rare variants suggests that allelic and effect size heterogeneity across populations of different ancestries is likely to pose a greater challenge to trans-ethnic meta-analysis of rare variants than to similar analyses of common variants. Here, we introduce a novel method to perform trans-ethnic meta-analysis of rare and low-frequency variants. The approach is centered on partitioning the studies into distinct clusters using local inference of genomic similarity between population groups, with the aim to minimize both the number of clusters and between-study heterogeneity in each cluster. Through a series of simulations, we show that our approach either performs similarly to or outperforms conventional and recently introduced meta-analysis strategies, particularly in the presence of allelic heterogeneity.

Genome-wide association studies have seen unprecedented success in identifying genetic loci that correlate with disease susceptibility and severity. Early phases of these studies have predominantly been performed in the Caucasian populations. The next phase in medical genetics is to extend the exploration across genetically diverse populations to leverage on larger sample sizes for locating smaller effects that may be present in most human populations. However, discoveries from these studies do not actually reveal the underlying functional changes to the human genome, but only point to broad regions stipulated by the extent of linkage disequilibrium (LD). Fine-mapping the functional variants can, however, be hampered by extensive LD, which can yield multiple perfect surrogates that are not distinguishable from the underlying causal variants, although several studies have illustrated the value of relying on multiple genetically diverse populations to narrow the candidate regions where the functional variants can be found in. Here, we explore the efficiency of trans-ethnic meta-analysis in discovering genetic association and in fine-mapping the causal variants by asking: are there any population diversity metrics that will be useful for: (i) identifying the populations or genomic regions where meta-analysis are likely to be more successful for discovering associations?; (ii) identifying the populations or loci to perform deep targeted sequencing for the purpose of fine-mapping causal variants? Our results indicate that simple metrics like the F(ST) or the population specificity of haplotypes are useful in trans-ethnic meta-analyses, while the degree of haplotype sharing and LD variation are informative of the efficiency in trans-ethnic fine-mapping.

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.

Recent reports have identified a north-south cline in genetic variation in East and South-East Asia, but these studies have not formally explored the basis of these clinical differences. Understanding the origins of these variations may provide valuable insights in tracking down the functional variants in genomic regions identified by genetic association studies. Here we investigate the genetic basis of these differences with genome-wide data from the HapMap, the Human Genome Diversity Project and the Singapore Genome Variation Project. We implemented four bioinformatic measures to discover genomic regions that are considerably differentiated either between two Han Chinese populations in the north and south of China, or across 22 populations in East and South-East Asia. These measures prioritized genomic stretches with: (i) regional differences in the allelic spectrum for SNPs common to the two Han Chinese populations; (ii) differential evidence of positive selection between the two populations as quantified by integrated haplotype score (iHS) and cross-population extended haplotype homozygosity (XP-EHH); (iii) significant correlation between allele frequencies and geographical latitudes of the 22 populations. We also explored the extent of linkage disequilibrium variations in these regions, which is important in combining genetic association studies from North and South Chinese. Two of the regions that emerged are found in HLA class I and II, suggesting that the HLA imputation panel from the HapMap may not be directly applicable to every Chinese sample. This has important implications to autoimmune studies that plan to impute the classical HLA alleles to fine map the SNP association signals.

Several lines of evidence link glucose-6-phosphate dehydrogenase (G6PD) deficiency to protection from severe malaria. Early reports suggested most G6PD deficiency in sub-Saharan Africa was because of the 202A/376G G6PD A- allele, and recent association studies of G6PD deficiency have employed genotyping as a convenient way to determine enzyme status. However, further work has suggested that other G6PD deficiency alleles are relatively common in some regions of West Africa. To investigate the consequences of unrecognized allelic heterogeneity on association studies, in particular studies of G6PD deficiency and malaria, we carried out a case-control analysis of 2488 Gambian children with severe malaria and 3875 controls. No significant association was found between severe malaria and the 202A/376G G6PD A- allele when analyzed alone, but pooling 202A/376G with other deficiency alleles revealed the signal of protection (male odds ratio (OR) 0.77, 95% CI 0.62-0.95, P=0.016; female OR 0.71, 95% CI 0.56-0.89, P=0.004). We have identified the 968C mutation as the most common G6PD A- allele in The Gambia. Our results highlight some of the consequences of allelic heterogeneity, particularly the increased type I error. They also suggest that G6PD-deficient male hemizygotes and female heterozygotes are protected from severe malaria.