RAPID REPORTS Population and social conditions. Pupils and students in the Community in 1990/91. 1993.9

<div><p>It is known that the macronutrient content of a meal has different impacts on the postprandial satiety and appetite hormonal responses. Whether obesity interacts with such nutrient-dependent responses is not well characterized. We examined the postprandial appetite and satiety hormonal responses after a high-protein (HP), high-carbohydrate (HC), or high-fat (HF) mixed meal. This was a randomized cross-over study of 9 lean insulin-sensitive (mean±SEM HOMA-IR 0.83±0.10) and 9 obese insulin-resistant (HOMA-IR 4.34±0.41) young (age 21–40 years), normoglycaemic Chinese men. We measured fasting and postprandial plasma concentration of glucose, insulin, active glucagon-like peptide-1 (GLP-1), total peptide-YY (PYY), and acyl-ghrelin in response to HP, HF, or HC meals. Overall postprandial plasma insulin response was more robust in the lean compared to obese subjects. The postprandial GLP-1 response after HF or HP meal was higher than HC meal in both lean and obese subjects. In obese subjects, HF meal induced higher response in postprandial PYY compared to HC meal. HP and HF meals also suppressed ghrelin greater compared to HC meal in the obese than lean subjects. In conclusion, a high-protein or high-fat meal induces a more favorable postprandial satiety and appetite hormonal response than a high-carbohydrate meal in obese insulin-resistant subjects.</p></div

PCS Interaction plot between ethnicity and stroke.

<p>PCS Interaction plot between ethnicity and stroke.</p

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data

<div><p>Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.</p></div

Socio-demographics of study participants.

†<p>ANOVA F statistics are significant at 5% level.</p>‡<p>Pearson Chi² statistics are significant at 5% level.</p

Population structure of 2,452 individuals in the Singapore Living Biobank Project.

<p>(A) Reference ancestry space derived from PCA on the genotypes of Chinese (CHS), Malays (MAS) and Indians (INS) from SGVP. (B) Estimated ancestry in the SGVP reference space based on LASER analysis. Colored symbols represent study individuals of self-reported Chinese and Malays. Grey symbols represent the SGVP reference individuals. (C) Estimated admixture proportion based on supervised ADMIXTURE analysis with the SGVP data as the reference. We specified K = 3 clusters in the ADMIXTURE analysis, which represent Chinese (blue), Malay (green), and Indian (orange) ancestry components.</p

Hierarchical Forward Regression for Main and Interaction Effects.

<p>Hierarchical Forward Regression for Main and Interaction Effects.</p

MCS Hierarchical Regression (Final).

*<p>Beta coefficients are significant at 5% level.</p

MCS Interaction plot between ethnicity and log Insulin.

<p>MCS Interaction plot between ethnicity and log Insulin.</p

PCS Interaction plot between ethnicity and musculoskeletal illness.

<p>PCS Interaction plot between ethnicity and musculoskeletal illness.</p

Clinical and lifestyle profile of study participants.

†<p>ANOVA F statistics are significant at 5% level.</p>‡<p>Pearson Chi² statistics are significant at 5% level.</p