Cognitive Ability, Wages, and Meritocracy

This paper presents new evidence from the NLSY on the importance of meritocracy in American society. In it, we find that general intelligence, or g -- a measure of cognitive ability--is dominant in explaining test score variance. The weights assigned to tests by g are similar for all major demographic groups. These results support Spearman's theory of g. We also find that g and other measures of ability are not rewarded equally across race and gender, evidence against the view that the labor market is organized on meritocratic principles. Additional factors beyond g are required to explain wages and occupational choice. However, both blue collar and white collar wages are poorly predicted by g or even multiple measures of ability. Observed cognitive ability is only a minor predictor of social performance. White collar wages are more g loaded than blue collar wages. Many noncognitive factors determine blue collar wages.

Methods for Large-Scale Genetic Association Studies.

With the increasing availability and decreasing costs of high-throughput genotyping, contemporary genetic association studies now incorporate vast quantities of information. Major advances in genotyping technology have led to higher throughput at lower costs, and greater accuracy and completeness. These advances bring with them new questions, including 1) how to best adjust for the multiple testing problem given the likely correlation between tests involving dense markers, and 2) what levels of genotyping quality can be expected, and what levels can be tolerated in an association testing framework. We first address the issue of adjustment for the many tests performed, given the high levels of correlation that are typical of association studies involving dense markers. We present PACT (P-value Adjusted for Correlated Tests), an estimator analogous to Bonferroni or Sidak adjustment which accounts for the correlation between tests. We show through simulation that PACT can attain the accuracy and power of permutation tests thousands of times faster. We next extend our work on PACT so that it may be applied to meta-analyses involving correlated tests. We describe extensions to four common study designs, and show through simulation that these methods provide valid tests with greater power than methods which do not account for correlation. Finally, we investigate the nature of genotyping error and missing data for a variety of common SNP genotyping platforms in two datasets where replicate genotyping has been performed. We find that the rates of error and missingness vary depending on an individual's true genotype, and that heterozygotes and minor allele homozygotes are more prone to errors and missingness on most platforms. We show that differential rates of genotype error and missing data can invalidate the commonly used test of equal allele frequencies. We use simulation to assess the impact of the observed distribution of errors and missing data on false-positive rates in a genome-wide association context. We find that the impact varies depending on 1) whether the underlying association test is an allele frequency test, a test for trend, or a family-based association test and 2) whether appropriate quality control measures are applied.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58390/1/conneely_1.pd

Autosomal Dominant Retinal Dystrophies Caused by a Founder Splice Site Mutation, c.828+3A>T, in PRPH2 and Protein Haplotypes in trans as Modifiers.

PurposeWe determined the phenotypic variation, disease progression, and potential modifiers of autosomal dominant retinal dystrophies caused by a splice site founder mutation, c.828+3A>T, in the PRPH2 gene.MethodsA total of 62 individuals (19 families) harboring the PRPH2 c.828+3A>T mutation, had phenotype analysis by fundus appearance, electrophysiology, and visual fields. The PRPH2 haplotypes in trans were sequenced for potential modifying variants and generalized estimating equations (GEE) used for statistical analysis.ResultsSeveral distinct phenotypes caused by the PRPH2 c.828+3A>T mutation were observed and fell into two clinical categories: Group I (N = 44) with mild pattern dystrophies (PD) and Group II (N = 18) with more severe cone-rod dystrophy (CRD), retinitis pigmentosa (RP), and central areolar chorioretinal dystrophy (CACD). The PRPH2 Gln304-Lys310-Asp338 protein haplotype in trans was found in Group I only (29.6% vs. 0%), whereas the Glu304-Lys310-Gly338 haplotype was predominant in Group II (94.4% vs. 70.4%). Generalized estimating equations analysis for PD versus the CRD/CACD/RP phenotypes in individuals over 43 years alone with the PRPH2 haplotypes in trans and age as predictors, adjusted for correlation within families, confirmed a significant effect of haplotype on severity (P = 0.03) with an estimated odds ratio of 7.16 (95% confidence interval [CI] = [2.8, 18.4]).ConclusionsThe PRPH2 c.828+3A>T mutation results in multiple distinct phenotypes likely modified by protein haplotypes in trans; the odds of having the CACD/RP-like phenotype (versus the PD phenotype) are 7.16 times greater with a Glu304-Lys310-Gly338 haplotype in trans. Further functional studies of the modifying haplotypes in trans and PRPH2 splice variants may offer therapeutic targets

A longitudinal study of DNA methylation as a potential mediator of age-related diabetes risk

DNA methylation (DNAm) has been found to show robust and widespread age-related changes across the genome. DNAm profiles from whole blood can be used to predict human aging rates with great accuracy. We sought to test whether DNAm-based predictions of age are related to phenotypes associated with type 2 diabetes (T2D), with the goal of identifying risk factors potentially mediated by DNAm. Our participants were 43 women enrolled in the Women's Health Initiative. We obtained methylation data via the Illumina 450K Methylation array on whole blood samples from participants at three timepoints, covering on average 16 years per participant. We employed the method and software of Horvath, which uses DNAm at 353 CpGs to form a DNAm-based estimate of chronological age. We then calculated the epigenetic age acceleration, or Δage, at each timepoint. We fit linear mixed models to characterize how Δage contributed to a longitudinal model of aging and diabetes-related phenotypes and risk factors. For most participants, Δage remained constant, indicating that age acceleration is generally stable over time. We found that Δage associated with body mass index (p = 0.0012), waist circumference (p = 0.033), and fasting glucose (p = 0.0073), with the relationship with BMI maintaining significance after correction for multiple testing. Replication in a larger cohort of 157 WHI participants spanning 3 years was unsuccessful, possibly due to the shorter time frame covered. Our results suggest that DNAm has the potential to act as a mediator between aging and diabetes-related phenotypes, or alternatively, may serve as a biomarker of these phenotypes

Prioritizing individual genetic variants after kernel machine testing using variable selection: He et al.

Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and do not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity By State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach

Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression: Cross-Platform Imputation of Methylation Profile

DNA methylation is a key epigenetic mark involved in both normal development and disease progression. Recent advances in high-throughput technologies have enabled genome-wide profiling of DNA methylation. However, DNA methylation profiling often employs different designs and platforms with varying resolution, which hinders joint analysis of methylation data from multiple platforms. In this study, we propose a penalized functional regression model to impute missing methylation data. By incorporating functional predictors, our model utilizes information from nonlocal probes to improve imputation quality. Here, we compared the performance of our functional model to linear regression and the best single probe surrogate in real data and via simulations. Specifically, we applied different imputation approaches to an acute myeloid leukemia dataset consisting of 194 samples and our method showed higher imputation accuracy, manifested, for example, by a 94% relative increase in information content and up to 86% more CpG sites passing post-imputation filtering. Our simulated association study further demonstrated that our method substantially improves the statistical power to identify trait-associated methylation loci. These findings indicate that the penalized functional regression model is a convenient and valuable imputation tool for methylation data, and it can boost statistical power in downstream epigenome-wide association study (EWAS)

Neonatal DNA methylation profile in human twins is specified by a complex interplay between intrauterine environmental and genetic factors, subject to tissue-specific influence

Comparison between groups of monozygotic (MZ) and dizygotic (DZ) twins enables an estimation of the relative contribution of genetic and shared and nonshared environmental factors to phenotypic variability. Using DNA methylation profiling of ∼20,000 CpG sites as a phenotype, we have examined discordance levels in three neonatal tissues from 22 MZ and 12 DZ twin pairs. MZ twins exhibit a wide range of within-pair differences at birth, but show discordance levels generally lower than DZ pairs. Within-pair methylation discordance was lowest in CpG islands in all twins and increased as a function of distance from islands. Variance component decomposition analysis of DNA methylation in MZ and DZ pairs revealed a low mean heritability across all tissues, although a wide range of heritabilities was detected for specific genomic CpG sites. The largest component of variation was attributed to the combined effects of nonshared intrauterine environment and stochastic factors. Regression analysis of methylation on birth weight revealed a general association between methylation of genes involved in metabolism and biosynthesis, providing further support for epigenetic change in the previously described link between low birth weight and increasing risk for cardiovascular, metabolic, and other complex diseases. Finally, comparison of our data with that of several older twins revealed little evidence for genome-wide epigenetic drift with increasing age. This is the first study to analyze DNA methylation on a genome scale in twins at birth, further highlighting the importance of the intrauterine environment on shaping the neonatal epigenome

Mitochondrial polymorphisms and susceptibility to type 2 diabetes-related traits in Finns

Mitochondria play an integral role in ATP production in cells and are involved in glucose metabolism and insulin secretion, suggesting that variants in the mitochondrial genome may contribute to diabetes susceptibility. In a study of Finnish families ascertained for type 2 diabetes mellitus (T2DM), we genotyped single nucleotide polymorphisms (SNPs) based on phylogenetic networks. These SNPs defined eight major haplogroups and subdivided groups H and U, which are common in Finns. We evaluated association with both diabetes disease status and up to 14 diabetes-related traits for 762 cases, 402 non-diabetic controls, and 465 offspring of genotyped females. Haplogroup J showed a trend toward association with T2DM affected status (OR 1.69, P =0.056) that became slightly more significant after excluding cases with affected fathers (OR 1.77, P =0.045). We also genotyped non-haplogroup-tagging SNPs previously reported to show evidence for association with diabetes or related traits. Our data support previous evidence for association of T16189C with reduced ponderal index at birth and also show evidence for association with reduced birthweight but not with diabetes status. Given the multiple tests performed and the significance levels obtained, this study suggests that mitochondrial genome variants may play at most a modest role in glucose metabolism in the Finnish population. Furthermore, our data do not support a reported maternal inheritance pattern of T2DM but instead show a strong effect of recall bias.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47596/1/439_2005_Article_46.pd

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ing glucose, insulin, and C-peptide, and more favorable cardiovascular risk profile compared to the complement set of subjects with T2DM. OSA also revealed 33 families with the lowest average fasting insulin that had increased evidence for linkage at a second locus (MLS = 3.45 at 128 cM; uncorrected p = 0.017) coincident with quantitative trait locus linkage analysis results for fasting and 2-hour insulin in subjects without T2DM. Conclusions: These results suggest two diabetes susceptibility loci on chromosome 6q that may affect subsets of individuals with a milder form of T2DM

DNA Methylation Signatures of Chronic Low-Grade Inflammation Are Associated with Complex Diseases

Background: Chronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation. Results: We performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n = 8863) and trans-ethnic replication in African Americans (n = 4111). We found differential methylation at 218 CpG sites to be associated with CRP (P \u3c 1.15 × 10–7) in the discovery panel of European ancestry and replicated (P \u3c 2.29 × 10–4) 58 CpG sites (45 unique loci) among African Americans. To further characterize the molecular and clinical relevance of the findings, we examined the association with gene expression, genetic sequence variants, and clinical outcomes. DNA methylation at nine (16%) CpG sites was associated with whole blood gene expression in cis (P \u3c 8.47 × 10–5), ten (17%) CpG sites were associated with a nearby genetic variant (P \u3c 2.50 × 10–3), and 51 (88%) were also associated with at least one related cardiometabolic entity (P \u3c 9.58 × 10–5). An additive weighted score of replicated CpG sites accounted for up to 6% inter-individual variation (R2) of age-adjusted and sex-adjusted CRP, independent of known CRP-related genetic variants. Conclusion: We have completed an EWAS of chronic low-grade inflammation and identified many novel genetic loci underlying inflammation that may serve as targets for the development of novel therapeutic interventions for inflammation