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.

/pdf/second-generation-plink-rising-to-the-challenge-of-larger-2i1coy4h8p.pdf

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

Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring.

In order to infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with some environmental variable that also affects the trait. In this work we study the properties of LD Score regression, a recently developed method for using summary statistics from genome-wide association studies (GWAS) to ensure that confounding does not inflate the number of false positives. We do not treat the effects of genetic variation as a random variable and thus are able to obtain results about the unbiasedness of this method. We demonstrate that LD Score regression can produce estimates of confounding at null SNPs that are nearly unbiased under fairly general conditions. This robustness holds in the case of the parent genotype affecting the offspring phenotype through some environmental mechanism, despite the resulting correlation over SNPs between LD Scores and the degree of confounding. LD Score regression is thus an even stronger technique for causal inference than foreseen by its developers. Additionally, we demonstrate that LD Score regression can produce reasonably robust estimates of the genetic correlation, even when its estimates of the genetic covariance and the two univariate heritabilities are substantially biased.

/pdf/the-accuracy-of-ld-score-regression-as-an-estimator-of-2wugbjduhm.pdf

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies

Erratum: Why it is hard to find genes associated with social science traits: Theoretical and empirical considerations (American Journal of Public Health (2013) 103 (S152-S166))

Circulating endothelial cells and circulating endothelial cell progenitors as surrogate markers for determining response to antiangiogenic agents.

An alternating tangential flow (ATF) perfusion-based transient gene expression (TGE) bioprocess has been developed using human embryonic kidney (HEK) 293 cells to produce H1-ss-np, a promising candidate for a universal influenza vaccine. Two major adjustments were taken to improve the process: (1) eliminate the interference of microbubbles during gene transfection; and (2) utilize an ATF perfusion system for a prolonged culture period. As a result, a closed-operation 9-days ATF perfusion-based TGE bioprocess was developed. The TGE bioprocess showed continuous cell growth with high cell viability and prolonged cellular productivity that achieved recombinant product level of ~270 mg/L which was more than two times that of 4-days base-line TGE bioprocess. In addition, the consumables cost per milligram for ATF perfusion-based TGE bioprocess was ~70% lower than that of the base-line TGE bioprocess suggesting high cost savings potential in vaccine manufacturing. Based on the lower contamination risk, higher productivity, and cost efficiency, the ATF perfusion-based TGE bioprocess can likely provide potential benefits to many future applications in vaccine and drug manufacturing.

James J. Lee

Papers

Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring.

The accuracy of LD Score regression as an estimator of confounding and genetic correlations in genome-wide association studies

Erratum: Why it is hard to find genes associated with social science traits: Theoretical and empirical considerations (American Journal of Public Health (2013) 103 (S152-S166))

Circulating endothelial cells and circulating endothelial cell progenitors as surrogate markers for determining response to antiangiogenic agents.

Development of an alternating tangential flow (ATF) perfusion-based transient gene expression (TGE) bioprocess for universal influenza vaccine