Background: PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format. Findings: To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O √ n -time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). Conclusions: The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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Second-generation PLINK: rising to the challenge of larger and richer datasets

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. 
To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. 
The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

https://oar.princeton.edu/bitstream/88435/pr1v99g/1/Genetic_effects_gene_expression_2017.pdf

Genetic effects on gene expression across human tissues.

Genetics and analysis of quantitative traits

A Genome-wide association study (GWAS) estimates size and significance of the effect of common genetic variants on a phenotype of interest. A Polygenic Score (PGS) is a score, computed for each individual, summarizing the expected value of a phenotype on the basis of the individual’s genotype. The PGS is computed as a weighted sum of the values of the individual’s genetic variants, using as weights the GWAS estimated coefficients from a training sample. Thus, PGS carries information on the genotype, and only on the genotype, of an individual. In our case phenotypes of interest are measures of educational achievement, such as having a college degree, or the education years, in a sample of approximately 2700 adult twins and their parents. We set up the analysis in a standard model of optimal parental investment and intergenerational mobility, extended to include a fully specified genetic analysis of skill transmission, and show that the model’s predictions on mobility differ substantially from those of the standard model. For instance, the coefficient of intergenerational income elasticity may be larger, and may differ across countries because the distribution of the genotype is different, completely independently of any difference in institution, technology or preferences. We then study how much of the educational achievement is explained by the PGS for education, thus estimating how much of the variance of education can be explained by genetic factors alone. We find a substantial effect of PGS on performance in school, years of education and college. Finally we study the channels between PGS and the educational achievement, distinguishing how much is due to cognitive skills and to personality traits. We show that the effect of PGS is substantially stronger on Intelligence than on other traits, like Constraint, which seem natural explanatory factors of educational success. For educational achievement, both cognitive and non cognitive skills are important, although the larger fraction of success is channeled by Intelligence. Date: September 17, 2018. We thank Aysu Okbay for generously running the meta-analysis (three times!) on the data, Peter Visscher for a clarification on Robinson et al. (2017), Philippe Köllinger for the help in the process, Joel Waldfogel, Tom Holmes, Giulio Zanella for very useful observations, criticisms and suggestions, and audiences in many seminars for very lively, illuminating discussions. Supported in part by grants from the National Science Foundation to AR (SES1728056), the National Institute on Alcohol Abuse and Alcoholism (AA09367) and the National Institute of Drug Abuse (DA05147).

Polygenic score analysis of educational achievement and intergenerational mobility

The target article touches upon some of the most difficult and essential questions in personality psychology. Questioning the notion of a common factor as an as–yet–unobserved common cause of a beh...

Common Factors and Causal Networks

Ree, Carretta, and Teachout's (2015) arguments for recognizing the importance of general factors are mostly on point, but they neglect two broad issues: (a) an important theoretical problem introduced by the presence of multiple factors (general, group, specific) and (b) the criterion validity of group factors in certain settings.

The determinacy and predictive power of common factors

563 Background: 5-FU continues to serve as the backbone of systemic combination chemotherapy for treatment of CRC in the adjuvant and metastatic disease settings. The dosing of 5-FU has traditionally been based on body surface area (BSA). Growing evidence suggests that BSA-based 5-FU dosing has several limitations, and that pharmacokinetic (PK)-guided dosing of 5-FU improves clinical efficacy with reduced toxicity. We conducted a QI study to evaluate the clinical feasibility of PK-guided 5-FU dose adjustment and its impact on clinical outcome. Methods: CRC patients treated with combination chemotherapy regimens containing infusional 5-FU who underwent PK-guided optimization of 5-FU dosing using an antibody-based immunoassay (OnDose or MyCare) were included in the analysis. Results: A total of 58 patients (34 males, 24 females) meeting the entry criteria have been identified to date. The median age was 61 years (range, 27-85). Fifteen patients had stage III disease, and the remaining 43 patients had stage ...

Therapeutic drug monitoring of 5-fluorouracil (5-FU) in the treatment of patients with colorectal cancer (CRC).

Heritable variation in fitness-survival and reproduction-is the fuel of evolution by natural selection. Many human societies have dramatically reduced mortality before and during the prime reproductive years, making fertility a reasonably good proxy for the whole of fitness in much of our species. For this reason, empirical knowledge regarding the genetics of fertility must be an essential part of any framework for understanding past and ongoing trends in human adaptive evolution. Here we use R.A. Fisher's analysis of human fertility as a starting point and find strong support from more recent research for his main contentions: fertility is a moderately heritable trait, where much of the genetic influences are shared with psychological characteristics.

James J. Lee

Papers

Polygenic score analysis of educational achievement and intergenerational mobility

Common Factors and Causal Networks

The determinacy and predictive power of common factors

Therapeutic drug monitoring of 5-fluorouracil (5-FU) in the treatment of patients with colorectal cancer (CRC).

The genetics of human fertility.