Logistic models among 192 same sex twin pairs of questionnaire based information in predicting DNA-determined zygosity.

<p>a. PPZ was the question about previously perceived zygosity, CBS was the question whether strangers confused by twins’ appearance, Formal zygosity test-PPZ-CBS was a complex strategy for zygosity determination (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123992#pone.0123992.g001" target="_blank">Fig 1</a>).</p><p>b. p would be the probability of dizygotic (DZ) twins. I_PPZ1, I_PPZ2, I_S1, I_S2, I_F1, I_F1 referred to the dummy variables of question PPZ, CBS and Formal zygosity test-PPZ-CBS respectively, a subscript “1” indicated monozygotic (MZ) twins versus Do not know\Hard to say, subscript “2” indicated DZ versus Do not know\Hard to say. I_Age1-Age4 were the dummy variables of age group 30–39, 40–49, 50–59 and ≥60 versus ≤29. I_G was the dummy variable of Male versus Female. Bolds represented <i>P-value</i> <0.05.</p><p>c. Cut-off point would be the average point of probability (of DZ) maximizing both sensitivity and specificity. Consistency rate would be the proportion of correctly diagnosed MZ and DZ pairs by the logistic model among 192 pairs.</p><p>d. Prediction Errors were the raw estimates calculated by the Leave-One-Out Cross Validation (LOOCV) method.</p><p>Logistic models among 192 same sex twin pairs of questionnaire based information in predicting DNA-determined zygosity.</p

Determination of Zygosity in Adult Chinese Twins Using the 450K Methylation Array versus Questionnaire Data

<div><p>Previous studies have shown that both single nucleotide polymorphisms (SNPs) and questionnaires-based method can be used for twin zygosity determination, but few validation studies have been conducted using Chinese populations. In the current study, we recruited 192 same sex Chinese adult twin pairs to evaluate the validity of using genetic markers-based method and questionnaire-based method in zygosity determination. We considered the relatedness analysis based on more than 0.6 million SNPs genotyping as the golden standards for zygosity determination. After quality control, qualified twins were left for relatedness analysis based on identical by descent calculation. Then those same sex twin pairs were included in the zygosity questionnaire validation analysis. Logistic regression model was applied to assess the discriminant ability of age, sex and the three questions in zygosity determination. Leave one out cross-validation was used as a measurement of internal validation. The results of zygosity determination based on 65 SNPs in 450k methylation array were all consistent with genotyping. Age, gender, questions of appearance confused by strangers and previously perceived zygosity consisted of the most predictable model with a consistency rate of 0.8698, cross validation predictive error of 0.1347. For twin studies with genotyping and\or 450k methylation array, there would be no need to conduct other zygosity testing for the sake of costs consideration.</p></div

Complex strategy for zygosity determination based on the three question of formal zygosity test, previously perceived zygosity and confused by strangers.

<p>For example, if someone had “Yes” of Formal zygosity test, his zygosity would be the answer of question Previously perceived zygosity (PPZ) either “Monozygotic” or “Dizygotic” (but when he chose “Do not know” of question PPZ, his zygosity would be based on question Confused by strangers). On the contrary, when he chose “No” or “Do not know” in Formal zygosity test; his zygosity would be directly based on answers of Confused by strangers. In the question of Confused by strangers, “Yes” indicated to be identical twin, “No” referred to fraternal twin, “Hard to say” implied zygosity remained unclear.</p

Relatedness Analysis of Discriminating Monozygotic\Dizygotic Twin Pairs by Genotyping and Methylation Array.

<p>(a) was the plot based on Genotyping Array Data, (b) was the plot based on Methylation Array Data. Z₀, Z₁ referred to identity-by-descent (IBD) probabilities: those for the individuals having zero or one pairs of IBD alleles. The Blue Dots indicated Monozygotic Twins (MZ), which Both Z₀ and Z₁ Approximately equaled to 0, while the Red Dots indicated Dizygotic Twins (DZ), which Z₀ was close to 0.25 and Z₁ was close to 0.5 by Genotyping Array Data. (c) was an scatter plot of methylation 65 SNPs beta values in a representative DZ pair with calculating the intra pair correlation coefficient 0.7478, (d) was an example of MZ pair with correlation coefficient 0.9988, (e) was the boxplot of methylation 65 SNPs correlation coefficient for MZ versus DZ, the grey area indicated possible cut-off points (correlation coefficient ranged 0.84–0.90) for discriminating MZ and DZ twins.</p

Characteristics of the 192 same sex twin pairs who were analyzed for questionnaire based zygosity determination.

<p>MZ: Monozygotic, DZ: Dizygotic, P-values were calculated from χ² test for categorical variables</p><p>Characteristics of the 192 same sex twin pairs who were analyzed for questionnaire based zygosity determination.</p

Associations of Body Composition Measurements with Serum Lipid, Glucose and Insulin Profile: A Chinese Twin Study

<div><p>Objectives</p><p>To quantitate and compare the associations of various body composition measurements with serum metabolites and to what degree genetic or environmental factors affect obesity-metabolite relation.</p><p>Methods</p><p>Body mass index (BMI), waist circumference (WC), lean body mass (LBM), percent body fat (PBF), fasting serum high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), glucose, insulin and lifestyle factors were assessed in 903 twins from Chinese National Twin Registry (CNTR). Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated from fasting serum glucose and insulin. Linear regression models and bivariate structural equation models were used to examine the relation of various body composition measurements with serum metabolite levels and genetic/environmental influences on these associations, respectively.</p><p>Results</p><p>At individual level, adiposity measurements (BMI, WC and PBF) showed significant associations with serum metabolite concentrations in both sexes and the associations still existed in male twins when using within-MZ twin pair comparison analyses. Associations of BMI with TG, insulin and HOMA-IR were significantly stronger in male twins compared to female twins (BMI-by-sex interaction p = 0.043, 0.020 and 0.019, respectively). Comparison of various adiposity measurements with levels of serum metabolites revealed that WC explained the largest fraction of variance in serum LDL-C, TG, TC and glucose concentrations while BMI performed best in explaining variance in serum HDL-C, insulin and HOMA-IR levels. Of these phenotypic correlations, 64–81% were attributed to genetic factors, whereas 19–36% were attributed to unique environmental factors.</p><p>Conclusions</p><p>We observed different associations between adiposity and serum metabolite profile and demonstrated that WC and BMI explained the largest fraction of variance in serum lipid profile and insulin resistance, respectively. To a large degree, shared genetic factors contributed to these associations with the remaining explained by twin-specific environmental factors.</p></div

Bivariate genetic analyses of the estimated genetic and environmental correlation coefficients for phenotype pairs.

<p>r_G = genetic correlation between 2 phenotypes; r_E = unique environmental correlation between 2 phenotypes; c_G and c_E = genetic and unique environmental contribution to the correlation between 2 phenotypes, respectively; c_{G = <math><msub><mrow><mi>r</mi></mrow><mrow><mi>G</mi></mrow></msub><mo>*</mo><msqrt><msub><mrow><mi>A</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>*</mo><msub><mrow><mi>A</mi></mrow><mrow><mn>2</mn></mrow></msub></msqrt></math>}; c_{E = <math><msub><mrow><mi>r</mi></mrow><mrow><mi>E</mi></mrow></msub><mo>*</mo><msqrt><msub><mrow><mi>E</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>*</mo><msub><mrow><mi>E</mi></mrow><mrow><mn>2</mn></mrow></msub></msqrt></math>}; %_G and %_E = percentage of genetic and unique environmental contribution to the correlation between 2 phenotypes.</p><p>Abbreviations are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140595#pone.0140595.t002" target="_blank">Table 2</a>.</p><p>Models were adjusted for age, sex, region, social economic status, smoking status, drinking status and physical activity.</p><p>Bivariate genetic analyses of the estimated genetic and environmental correlation coefficients for phenotype pairs.</p

Random-intercept regression analyses of body composition measurements and serum metabolites in 903 Chinese adult twins stratified by gender, treating twins as individuals.

<p>Abbreviations are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0140595#pone.0140595.t002" target="_blank">Table 2</a>.</p><p>All regression models were adjusted for age, zygosity, region, social economic status, smoking status, drinking status and physical activity.</p><p>*p<0.05</p><p>**p<0.01</p><p>***p<0.001</p><p>Random-intercept regression analyses of body composition measurements and serum metabolites in 903 Chinese adult twins stratified by gender, treating twins as individuals.</p

Epidemiological characteristics of the 903 Chinese adult twins.

<p>SD, standard deviation; MZ, monozygotic; SES, social economic status.</p><p>n = 903 individuals (385 twin pairs and 133 individuals).</p><p>^a P values were corrected for the correlation between co-twins using multinomial logistic regression for categorical variables and mixed-effects models for continuous variables.</p><p>Epidemiological characteristics of the 903 Chinese adult twins.</p

Body composition and biochemical characteristics of the 903 Chinese adult twins.

<p>BMI, body mass index; WC, waist circumference; PBF, percentage body fat; LBM, lean body mass; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; TG, triglycerides; TC, total cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance.</p><p>n = 903 individuals (385 twin pairs and 133 individuals) and sample size vary due to missing values.</p><p>^a Data are reported as mean (95%CI) for body composition measurements and median (interquartile range) for biochemical measures.</p><p>^b<i>P</i> values were corrected for the correlation between co-twins using mixed-effects models for continuous variables.</p><p>Body composition and biochemical characteristics of the 903 Chinese adult twins.</p