Additional file 1: of Gene-diet interaction effects on BMI levels in the Singapore Chinese population

Table S1. SNP information and Meta-analysis between 78 SNPs and BMI level. Table S2. Interaction between SNPs and AHEI-2010 dietary score on BMI. Table S3. Interaction between SNPs and total calories on BMI. Table S4. Interaction between SNPs and %protein on BMI. Table S5. Interaction between SNPs and út on BMI. Table S6. Interaction between SNPs and %SFA on BMI. Table S7. Interaction between SNPs and %MUFA on BMI. Table S8. Interaction between SNPs and %PUFA on BMI. Table S9. Interaction between SNPs and Êrbohydrate on BMI. Table S10. Interaction between SNPs and %starch on BMI. Table S11. Interaction between SNPs and fiber on BMI. Table S12. Interaction between SNPs and cholesterol on BMI. Table S13. Interaction between GRS and dietary factors on BMI in individual datasets used in the study. (PDF 1521 kb

Bivariate plots of the effect sizes in Asian cohorts and European ancestry cohorts.

<p>The effect sizes reported in the European ancestry were plotted against the ones discovered in our studies. The Y-axis corresponds to the European effect size, while the X-axis corresponds to the effect in Chinese (A), Indians (B), Malays (C) and Meta-analysis (D). The SNPs which are replicated in our studies at the P-value of 0.05 level were designated by red dots, while the SNPs failed to be replicated were designated by black dots. The SNPs with inconsistent effect directions between European and each population were labeled by the reported gene names.</p

Manhattan plot and Q-Q plot of genome-wide meta-analysis.

<p>(A) The Manhattan plot of the meta-analysis of Chinese, Malays and Indians. The minus log₁₀ of P-values (Y-axis) of inverse-variance meta-analysis across the 22 autosomal chromosomes after genomic control were plotted against the genomic coordinates. The horizontal line represents the genome-wide significant level of 5x10^-8. (B) Q-Q plot of the observed P-values (Y-axis) against the expected P-values (X-axis). The diagonal line and its 95% confidence envelop were also plotted.</p

Principal component analysis (PCA) of 1,224 samples from 16 global populations.

<p>PCA of 1,224 samples from SSIP, SSMP and 14 populations from Phase 1 of the 1-coded by continents (panel A). An analysis of admixture was also performed on the 16 populations with ADMIXTURE, where the number of distinct populations (<i>K</i>) was allowed to vary between 2 and 8 (panel B). The black window highlights the position of the SSIP samples on the admixture plot.</p

Insights into the Genetic Structure and Diversity of 38 South Asian Indians from Deep Whole-Genome Sequencing

<div><p>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.</p></div

Principal component analysis (PCA) of SSIP samples with 132 South Asians.

<p>PCA of 36 SSIP samples with 132 South Asian samples from 25 well-defined Indian groups by Reich and colleagues <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004377#pgen.1004377-Reich3" target="_blank">[44]</a> using 202,600 SNPs that were present in both databases (panel A). Five groups corresponding to Great Andamanese, Onge, Nyshi, Aonaga and Siddi were subsequently removed, leaving 104 samples from 20 Indian groups to be analyzed in a second PCA, where the samples were first assigned a color according to their group memberships (panel B), and second by the latitude of origin into North and South Indians (panel C, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004377#pgen.1004377.s018" target="_blank">Table S2</a> for the classification of North and South Indians). The color assignments in panels A and B are represented by the color legend on the bottom left of the figure.</p

Dstatistic analysis with ancient genomes for 5 randomly selected paired samples from each population from 1KGP and SSMP, anchored with a different SSIP sample (G1) and the chimpanzee genome in each of the 5 iterations.

<p>Dstatistic analysis with ancient genomes for 5 randomly selected paired samples from each population from 1KGP and SSMP, anchored with a different SSIP sample (G1) and the chimpanzee genome in each of the 5 iterations.</p

Size distribution and novelty of variants in SSIP.

<p>Autosomal variants identified in the 36 SSIP samples, which included single nucleotide polymorphisms (SNPs), small insertion/deletions (indels) between 2 bp to 50 bp, and large deletions between 51 bp to 1 Mb. The SSIP SNPs and indels are defined as novel if they are not present in SSMP and dbSNP137, whereas dbSNP132 was used for defining the novelty of the 1 KGP SNPs and indels. The novelty of large deletions in SSIP and 1 KGP is defined with respect to SSMP and DGV release 2013-07-23.</p

Assessing intra-population diversity between the samples.

<p>The extent of SNP sharing between every pair of samples in a population can be measured with a distance measure <i>D</i> that is scaled between 0 and 1 (vertical axis), where a higher value indicates a greater extent of heterogeneity in SNP content (or a lower degree of SNP sharing) between two samples. All possible pairwise measurements of <i>D</i> in each population are represented in a boxplot, where the ends of the whiskers indicate the minimum and maximum distances between specific pairs of samples in that population, the edges of the box indicates the 1^st and 3^rd quartiles, and the horizontal line in the box represents the median pairwise distance. The groups are colored with respect to the four continents (Americas – maroon; Africans – red; Asians – green; Europeans – blue). Each label on the horizontal axis indicates the continent label, population label, number of samples and total number of sample pairs of the population.</p

Chromosome Y haplogroup assignment for the 11 SSIP male samples.

<p>Chromosome Y haplogroup assignment for the 11 SSIP male samples.</p