Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples.

The distribution of fitness effects (DFE) has considerable importance in population genetics. To date, estimates of the DFE come from studies using a small number of individuals. Thus, estimates of the proportion of moderately to strongly deleterious new mutations may be unreliable because such variants are unlikely to be segregating in the data. Additionally, the true functional form of the DFE is unknown, and estimates of the DFE differ significantly between studies. Here we present a flexible and computationally tractable method, called Fit∂a∂i, to estimate the DFE of new mutations using the site frequency spectrum from a large number of individuals. We apply our approach to the frequency spectrum of 1300 Europeans from the Exome Sequencing Project ESP6400 data set, 1298 Danes from the LuCamp data set, and 432 Europeans from the 1000 Genomes Project to estimate the DFE of deleterious nonsynonymous mutations. We infer significantly fewer (0.38-0.84 fold) strongly deleterious mutations with selection coefficient |s| > 0.01 and more (1.24-1.43 fold) weakly deleterious mutations with selection coefficient |s| < 0.001 compared to previous estimates. Furthermore, a DFE that is a mixture distribution of a point mass at neutrality plus a gamma distribution fits better than a gamma distribution in two of the three data sets. Our results suggest that nearly neutral forces play a larger role in human evolution than previously thought

Natural selection reduced diversity on human Y chromosomes

The human Y chromosome exhibits surprisingly low levels of genetic diversity. This could result from neutral processes if the effective population size of males is reduced relative to females due to a higher variance in the number of offspring from males than from females. Alternatively, selection acting on new mutations, and affecting linked neutral sites, could reduce variability on the Y chromosome. Here, using genome-wide analyses of X, Y, autosomal and mitochondrial DNA, in combination with extensive population genetic simulations, we show that low observed Y chromosome variability is not consistent with a purely neutral model. Instead, we show that models of purifying selection are consistent with observed Y diversity. Further, the number of sites estimated to be under purifying selection greatly exceeds the number of Y-linked coding sites, suggesting the importance of the highly repetitive ampliconic regions. While we show that purifying selection removing deleterious mutations can explain the low diversity on the Y chromosome, we cannot exclude the possibility that positive selection acting on beneficial mutations could have also reduced diversity in linked neutral regions, and may have contributed to lowering human Y chromosome diversity. Because the functional significance of the ampliconic regions is poorly understood, our findings should motivate future research in this area.Comment: 43 pages, 11 figure

The impact of population demography and selection on the genetic architecture of complex traits

Population genetic studies have found evidence for dramatic population growth in recent human history. It is unclear how this recent population growth, combined with the effects of negative natural selection, has affected patterns of deleterious variation, as well as the number, frequencies, and effect sizes of mutations that contribute risk to complex traits. Here I use simulations under population genetic models where a proportion of the heritability of the trait is accounted for by mutations in a subset of the exome. I show that recent population growth increases the proportion of nonsynonymous variants segregating in the population, but does not affect the genetic load relative to that in a population that did not expand. Under a model where a mutation's effect on a trait is correlated with its effect on fitness, rare variants explain a greater portion of the additive genetic variance of the trait in a population that has recently expanded than in a population that did not recently expand. Further, when using a single-marker test, for a given false-positive rate and sample size, recent population growth decreases the expected number of significant association with the trait relative to the number detected in a population that did not expand. However, in a model where there is no correlation between a mutation's effect on fitness and the effect on the trait, common variants account for much of the additive genetic variance, regardless of demography. Moreover, here demography does not affect the number of significant association detected. These finding suggest recent population history may be an important factor influencing the power of association tests in accounting for the missing heritability of certain complex traits

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

DEVELOPMENT AND UTILIZATION OF BAYESIAN PROGNOSTIC MODELS IN A LEFT VENTRICULAR ASSIST DEVICE DECISION SUPPORT TOOL

Heart failure is a chronic, progressive condition that affects over 6 million Americans. The gold standard treatment for advanced heart failure is heart transplant. However, when a donor heart is not available, or the patient is not eligible, patients may receive a mechanical circulatory support device such as a left ventricular assist device (LVAD). LVADs can improve patient survival and increase patient quality of life but they also require significant changes in lifestyle and carry with them risks of adverse events, such as re-hospitalization, gastrointestinal bleeding (GI), stroke, or right heart failure. LVAD decision making for physicians and patients requires extensive discussion of the trade-off between benefits, risks, and associated lifestyle changes. Decision support tools for patients and their caregivers are in development but are not personalized and are limited to general educational information. Using Bayesian modeling, a machine learning method of data analysis, I developed novel predictive models for three sets of LVAD outcomes: all-cause mortality, recurrent gastrointestinal (GI) bleeding, and pump-dependent ischemic stroke. The mortality models performed better than current risk scores with receiver operating characteristic area under the curve (ROC AUC) of 70-71% in a multi-center validation cohort and 76-79% in a contemporary single-center study. The recurrent GI bleeding models performed with ROC AUCs of 68% and 60%, revealed the importance of hemoglobin/hematocrit levels and inflammation in driving risk, and are the first models for this outcome. The ischemic stroke models out-performed the current ischemic risk score with ROC AUCs of 64-66%. In addition to model development, I explored how to present prognostic information to decision making stakeholders: physicians, patients, and caregivers. I accomplished this with three studies: pilot testing the usability of an online application for physicians, surveying potential LVAD patients’ interest in healthcare engagement, and comparing the interpretation of prognostic information in different visual formats between patients and the general population. The results of these studies indicated that survival predictions are the most important outcome in decision making; patient numeracy is a key determinant of decision making engagement; and use of line graphs to present prognostic information is well-suited to all stakeholders

Synthetic Biology Approaches to Engineering Human Cells

The field of synthetic biology seeks to revolutionize the scope and scale of what is currently feasible by genetic engineering. By focusing on engineering general signal processing platforms using modular genetic parts and devices rather than `one-off' systems, synthetic biologists aim to enable plug-and-play genetic circuits readily adaptable to different contexts. For mammalian systems, the goal of synthetic biology is to create sophisticated research tools and gene therapies. While several isolated parts and devices exist for mammalian systems there are few signal processing platforms available. We addressed this need by creating a transcriptional regulatory framework using programmable zinc finger (ZF) and TALE transcription factors and a conceptual framework for logical T-cell receptor signaling. We first engineered a large set of ZF activator and repressor transcription factors and response promoters. ZFs are scalable elements as they can be engineered to bind to given DNA sequences. We demonstrated that we could ‘tune’ the activity of the ZF transcription factors by fusing them to protein homo-dimerization domains and by modifying their response promoters. We also created OR and NOR logic gates using hybrid promoters and AND and NAND logic gates by reconstituting split zinc finger activators and repressors with split inteins. Next, using a computational algorithm we designed a series of TALE transcriptional activators and repressors predicted to be orthogonal to all 2kb human promoter regions and thus minimally interfere with endogenous gene expression. TALEs can be designed to bind to even longer DNA sequences than ZFs, however off-target binding is predicted to occur. We tested our computationally designed TALEs in human cells demonstrating that they activated their intended target genes, but not their likely endogenous off-target genes, nor synthetic promoters with binding site mismatches. Finally, we created a conceptual framework for logical T-cell-mediated killing of target cells expressing combinations of surface antigens. The systems consist of conventional and novel chimeric antigen receptors (CARs) containing inhibitory or co-stimulatory cytoplasmic signaling domains. In co-incubation assays of engineered T-cells with target cells we demonstrated a functioning OR-Gate system and progress toward development of a functional NOT-Gate system using the CD300a and CD45 inhibitory receptor domains

Engineering Synthetic TAL Effectors with Orthogonal Target Sites

The ability to engineer biological circuits that process and respond to complex cellular signals has the potential to impact many areas of biology and medicine. Transcriptional activator-like effectors (TALEs) have emerged as an attractive component for engineering these circuits, as TALEs can be designed de novo to target a given DNA sequence. Currently, however, the use of TALEs is limited by degeneracy in the site-specific manner by which they recognize DNA. Here, we propose an algorithm to computationally address this problem. We apply our algorithm to design 180 TALEs targeting 20 bp cognate binding sites that are at least 3 nt mismatches away from all 20 bp sequences in putative 2 kb human promoter regions. We generated eight of these synthetic TALE activators and showed that each is able to activate transcription from a targeted reporter. Importantly, we show that these proteins do not activate synthetic reporters containing mismatches similar to those present in the genome nor a set of endogenous genes predicted to be the most likely targets in vivo. Finally, we generated and characterized TALE repressors comprised of our orthogonal DNA binding domains and further combined them with shRNAs to accomplish near complete repression of target gene expression

Determining the effect of natural selection on linked neutral divergence across species

A major goal in evolutionary biology is to understand how natural selection has shaped patterns of genetic variation across genomes. Studies in a variety of species have shown that neutral genetic diversity (intra-species differences) has been reduced at sites linked to those under direct selection. However, the effect of linked selection on neutral sequence divergence (inter-species differences) remains ambiguous. While empirical studies have reported correlations between divergence and recombination, which is interpreted as evidence for natural selection reducing linked neutral divergence, theory argues otherwise, especially for species that have diverged long ago. Here we address these outstanding issues by examining whether natural selection can affect divergence between both closely and distantly related species. We show that neutral divergence between closely related species (e.g. human-primate) is negatively correlated with functional content and positively correlated with human recombination rate. We also find that neutral divergence between distantly related species (e.g. human-rodent) is negatively correlated with functional content and positively correlated with estimates of background selection from primates. These patterns persist after accounting for the confounding factors of hypermutable CpG sites, GC content, and biased gene conversion. Coalescent models indicate that even when the contribution of ancestral polymorphism to divergence is small, background selection in the ancestral population can still explain a large proportion of the variance in divergence across the genome, generating the observed correlations. Our findings reveal that, contrary to previous intuition, natural selection can indirectly affect linked neutral divergence between both closely and distantly related species. Though we cannot formally exclude the possibility that the direct effects of purifying selection drive some of these patterns, such a scenario would be possible only if more of the genome is under purifying selection than currently believed. Our work has implications for understanding the evolution of genomes and interpreting patterns of genetic variation.Tanya N. Phung, Christian D. Huber, Kirk E. Lohmuelle