%0 Journal Article %J American Sociological Review %D 2015 %T Lifetime Socioeconomic Status, Historical Context, and Genetic Inheritance in Shaping Body Mass in Middle and Late Adulthood %A Hexuan Liu %A Guo, Guang %K Demographics %K Event History/Life Cycle %K Genetics %K Health Conditions and Status %X This study demonstrates that body mass in middle and late adulthood is a consequence of the complex interplay among individuals' genes, lifetime socioeconomic experiences, and the historical context in which they live. Drawing on approximately 9,000 genetic samples from the Health and Retirement Study, we first investigate how socioeconomic status (SES) over the life course moderates the impact of 32 established obesity-related genetic variants on body mass index (BMI) in middle and late adulthood. We then consider differences across birth cohorts in the genetic influence on BMI, and cohort variations in the moderating effects of life-course SES on the genetic influence. Our analyses suggest that persistently low SES over the life course or downward mobility (e.g., high SES in childhood but low SES in adulthood) amplify the genetic influence on BMI, and persistently high SES or upward mobility (e.g., low SES in childhood but high SES in adulthood) compensate for such influence. For more recent birth cohorts, the genetic influence on BMI becomes stronger, but the moderating effects of lifetime SES on the genetic influence are weaker compared to earlier cohorts. We discuss these findings in light of social changes during the obesity epidemic in the United States. %B American Sociological Review %I 80 %V 80 %P 705-737 %G eng %N 4 %4 body mass index/Socioeconomic Status/life course/genetic influence/genetics/genetics/obesity %$ 999999 %R 10.1177/0003122415590627 %0 Journal Article %J BMC Genomics %D 2015 %T Mixture SNPs effect on phenotype in genome-wide association studies. %A Wang, Ling %A Shen, Haipeng %A Hexuan Liu %A Guo, Guang %K Algorithms %K Alleles %K Alpha-Ketoglutarate-Dependent Dioxygenase FTO %K Bayes Theorem %K Body Mass Index %K Chromosomes, Human, Pair 16 %K Genome-Wide Association Study %K Humans %K Linkage Disequilibrium %K Phenotype %K Polymorphism, Single Nucleotide %K Principal Component Analysis %K Proteins %X

BACKGROUND: Recently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs. The HBM models the SNP effects using a mixture distribution of a point mass at zero and a normal distribution, where the point mass corresponds to those non-associative SNPs.

RESULTS: We estimate the HBM using Gibbs sampling. The estimation performance of our method is first demonstrated through two simulation studies. We make the simulation setups realistic by using parameters fitted on the Framingham Heart Study (FHS) data. The simulation studies show that our method can accurately estimate the proportion of SNPs associated with the simulated phenotype and identify these SNPs, as well as adapt to certain model mis-specification than the standard mixed models. In addition, we analyze data from the FHS and the Health and Retirement Study (HRS) to study the association between Body Mass Index (BMI) and SNPs on Chromosome 16, and replicate the identified genetic associations. The analysis of the FHS data identifies 0.3% SNPs on Chromosome 16 that affect BMI, including rs9939609 and rs9939973 on the FTO gene. These two SNPs are in strong linkage disequilibrium with rs1558902 (Rsq =0.901 for rs9939609 and Rsq =0.905 for rs9939973), which has been reported to be linked with obesity in previous GWAS. We then replicate the findings using the HRS data: the analysis finds 0.4% of SNPs associated with BMI on Chromosome 16. Furthermore, around 25% of the genes that are identified to be associated with BMI are common between the two studies.

CONCLUSIONS: The results demonstrate that the HBM and the associated estimation algorithm offer a powerful tool for identifying significant genetic associations with phenotypes of interest, among a large number of SNPs that are common in modern genetics studies.

%B BMC Genomics %I 16 %V 16 %P 3 %8 2015 Feb 03 %G eng %1 http://www.ncbi.nlm.nih.gov/pubmed/25649116?dopt=Abstract %2 PMC4417323 %4 genetics/genetics/Phenotypes/Phenotypes/Hierarchical Bayesian Model/Statistical analysis/Framingham Heart Study/Body Mass Index %$ 999999 %R 10.1186/1471-2164-16-3 %0 Journal Article %J PLoS One %D 2014 %T Genomic assortative mating in marriages in the United States. %A Guo, Guang %A Wang, Lin %A Hexuan Liu %A Randall, Thomas %K Data collection %K Female %K Genome, Human %K Genomics %K Genotype %K Humans %K Male %K Marriage %K Middle Aged %K Phenotype %K Polymorphism, Single Nucleotide %K Reproduction %K United States %X

Assortative mating in phenotype in human marriages has been widely observed. Using genome-wide genotype data from the Framingham Heart study (FHS; number of married couples = 989) and Health Retirement Survey (HRS; number of married couples = 3,474), this study investigates genomic assortative mating in human marriages. Two types of genomic marital correlations are calculated. The first is a correlation specific to a single married couple "averaged" over all available autosomal single-nucleotide polymorphism (SNPs). In FHS, the average married-couple correlation is 0.0018 with p = 3 × 10(-5); in HRS, it is 0.0017 with p = 7.13 × 10(-13). The marital correlation among the positively assorting SNPs is 0.001 (p = .0043) in FHS and 0.015 (p = 1.66 × 10(-24)) in HRS. The sizes of these estimates in FHS and HRS are consistent with what are suggested by the distribution of the allelic combination. The study also estimated SNP-specific correlation "averaged" over all married couples. Suggestive evidence is reported. Future studies need to consider a more general form of genomic assortment, in which different allelic forms in homologous genes and non-homologous genes result in the same phenotype.

%B PLoS One %V 9 %P e112322 %8 2014 %G eng %N 11 %1 http://www.ncbi.nlm.nih.gov/pubmed/25384046?dopt=Abstract %R 10.1371/journal.pone.0112322