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bioRxiv2016

Accounting for genetic interactions is necessary for accurate prediction of extreme phenotypic values of quantitative traits in yeast

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multi-locus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.

Genetics↗

bioRxiv2016

Genetic correlations with climate variables suggest Caenorhabditis elegans natural niche preferences

Species inhabit a variety of environmental niches, and the adaptation to a particular niche is often controlled by genetic factors, including gene-by-environment interactions. The genes that vary in order to regulate the ability to colonize a niche are often difficult to identify, especially in the context of complex ecological systems and in experimentally uncontrolled natural environments. Quantitative genetic approaches provide an opportunity to investigate correlations between genetic factors and environmental parameters that might define a niche. Previously, we have shown how a collection of 208 whole-genome sequenced wild Caenorhabditis elegans can facilitate association mapping approaches. To correlate climate parameters with the variation found in this collection of wild strains, we used geographic data to exhaustively curate daily weather measurements in short-term (three month), middle-term (one year), and long-term (three year) durations surrounding the data of strain isolation. These climate parameters were then used as quantitative traits in the mapping approaches. We identified 10 QTL underlying variation in three traits: elevation, relative humidity, and average temperature. We then performed statistical analyses to further narrow the genomic interval of interest to identify gene candidates with variants potentially underlying phenotypic differences. Additionally, we performed two-strain competition assays at high and low temperatures to validate a QTL for temperature preference and found suggestive evidence that genotypes might be adapted to particular temperatures.\n\n100-word summary for G3Quantitative genetic approaches provide an opportunity to investigate correlations between genetic factors and environmental parameters that might define a niche, but these genes are difficult to identify, especially in the context of complex ecological systems. Here, we used a collection of 152 sequenced wild Caenorhabditis elegans to correlate climate parameters with the variation found in this collection of wild strains. We identified 10 QTL in five traits, including elevation, relative humidity, and temperature. Additionally, we performed competition assays to validate a QTL for temperature preference and found suggestive evidence that genotypes might be adapted to particular temperatures.

Genetics↗

bioRxiv2016

SweGen: A whole-genome map of genetic variability in a cross-section of the Swedish population

Here we describe the SweGen dataset, a high-quality map of genetic variation in the Swedish population. This data represents a basic resource for clinical genetics laboratories as well as for sequencing-based association studies, by providing information on the frequencies of genetic variants in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide population based cohort of over 10,000 individuals. From this sample collection, 1,000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole genome sequencing (WGS). Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a whole-genome map of aggregated variant frequencies in the Swedish population that we hereby release to the scientific community.

genetics↗

bioRxiv2016

SOFIA: an R package for enhancing genetic visualization with Circos

Visualization of data from any stage of genetic and genomic research is one of the most useful approaches for detecting potential errors, ensuring accuracy and reproducibility, and presentation of the resulting data. Currently software such as Circos, ClicO FS, and RCircos, among others, provide tools for plotting a variety of genetic data types in a concise manner for data exploration and presentation. However, each of the programs have one or more disadvantages that limit their usability in data exploration or construction of publication quality figures, such as inflexibility in formatting and configuration, reduced image quality, lack of potential for automation, or requirements of high-level computational expertise. Therefore, we developed the R package SOFIA, which leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. The advantages of integrating both R and Circos into SOFIA are numerous. R is a very powerful, mid-level programming language widely used among the genetic and genomic research community, while Circos has proven to be a novel software for arranging genomic data to create aesthetical publication quality circular figures. Producing Circos figures in R with SOFIA is simple, requires minimal coding experience, even for complex figures that incorporate high-dimensional genetic information, and allows simultaneous analysis and visual exploration of genomic and genetic data in a single programming environment.

genetics↗

bioRxiv2016

A genetic risk score to guide age-specific, personalized prostate cancer screening

BackgroundProstate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patients risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa.\n\nMethodsGenotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given mans risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening.\n\nFindingsPHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set (p =10-53). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1st or 99th percentiles of genetic risk.\n\nInterpretationPolygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa.\n\nFundingDepartment of Defense #W81XWH-13-1-0391

genetics↗

bioRxiv2016

Genetic and transgenic reagents for Drosophila simulans, D.mauritiana, D. yakuba, D. santomea and D. virilis

Species of the Drosophila melanogaster species subgroup, including the species D. simulans, D. mauritiana, D. yakuba, and D. santomea, have long served as model systems for studying evolution. Studies in these species have been limited, however, by a paucity of genetic and transgenic reagents. Here we describe a collection of transgenic and genetic strains generated to facilitate genetic studies within and between these species. We have generated many strains of each species containing mapped piggyBac transposons including an enhanced yellow fluorescent protein gene expressed in the eyes and a phiC31 attP site-specific integration site. We have tested a subset of these lines for integration efficiency and reporter gene expression levels. We have also generated a smaller collection of other lines expressing other genetically encoded fluorescent molecules in the eyes and a number of other transgenic reagents that will be useful for functional studies in these species. In addition, we have mapped the insertion locations of 58 transposable elements in D. virilis that will be useful for genetic mapping studies.

genetics↗

bioRxiv2017

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes produces a substantially better accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected nine independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis, and Peripheral Vascular Disease. The GERA cohort analysis suggests that CPBayes is more powerful than ASSET with respect to detecting independent pleiotropic variants. We provide an R-package CPBayes implementing the proposed method.\n\nAuthor SummaryGenome-wide association studies (GWASs) have highlighted shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of aggregate-level pleiotropic association and selects an optimal subset of associated traits underlying a pleiotropic signal. CPBayes analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate GWASs with or without shared subjects, cohort study for multiple traits. It performs a fully Bayesian analysis and offers various flexibilities in the inference. In addition to parameters of primary interest (e.g., the measures of overall pleiotropic association, the optimal subset of associated traits), it provides additional interesting insights into a pleiotropic signal (e.g., the trait-specific posterior probability of association, the credible interval of unknown true genetic effects). Using computer simulations and a real data application to the large Kaiser GERA cohort, we demonstrate that CPBayes offers substantially better accuracy while selecting the non-null traits compared to a well known subset-based meta analysis ASSET. In the GERA cohort analysis, CPBayes detected a larger number of independent pleiotropic variants than ASSET. We provide a user-friendly R-package CPBayes for general use.

genetics↗

bioRxiv2017

Genetic contributions to trail making test performance in UK Biobank

The Trail Making Test is a widely used test of executive function and has been thought to be strongly associated with general cognitive function. We examined the genetic architecture of the trail making test and its shared genetic aetiology with other tests of cognitive function in 23 821 participants from UK Biobank. The SNP-based heritability estimates for trail-making measures were 7.9 % (part A), 22.4 % (part B), and 17.6 % (part B - part A). Significant genetic correlations were identified between trail-making measures and verbal-numerical reasoning (rg > 0.6), general cognitive function (rg > 0.6), processing speed (rg > 0.7), and memory (rg > 0.3). Polygenic profile analysis indicated considerable shared genetic aetiology between trail making, general cognitive function, processing speed, and memory (standardized {beta} between 0.03 and 0.08). These results suggest that trail making is both phenotypically and genetically strongly associated with general cognitive function and processing speed.

genetics↗

bioRxiv2017

Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits.

Heritability, h2, is a foundational concept in genetics, critical to understanding the genetic basis of complex traits. Recently-developed methods that estimate heritability from genotyped SNPs, h2 SNP, explain substantially more genetic variance than genome-wide significant loci, but less than classical estimates from twins and families. However, h2SNP estimates have yet to be comprehensively compared under a range of genetic architectures, making it difficult to draw conclusions from sometimes conflicting published estimates. Here, we used thousands of real whole genome sequences to simulate realistic phenotypes under a variety of genetic architectures, including those from very rare causal variants. We compared the performance of ten methods across different types of genotypic data (commercial SNP array positions, whole genome sequence variants, and imputed variants) and under differing causal variant frequencies, levels of stratification, and relatedness thresholds. These results provide guidance in interpreting past results and choosing optimal approaches for future studies. We then chose two methods (GREML-MS and GREML-LDMS) that best estimated overall h2SNP and the causal variant frequency spectra to six phenotypes in the UK Biobank using imputed genome-wide variants. Our results suggest that as imputation reference panels become larger and more diverse, estimates of the frequency distribution of causal variants will become increasingly unbiased and the vast majority of trait narrow-sense heritability will be accounted for.

genetics↗

bioRxiv2017

Maternal and fetal genetic contribution to gestational weight gain

BackgroundClinical recommendations to limit gestational weight gain (GWG) imply high GWG is causally related to adverse outcomes in mother or offspring, but GWG is the sum of several inter-related complex phenotypes (maternal fat deposition and vascular expansion, placenta, amniotic fluid and fetal growth). Understanding the genetic contribution to GWG could help clarify the potential effect of its different components on maternal and offspring health. Here we explore the genetic contribution to total, early and late GWG.\n\nParticipants and MethodsA genome-wide association study was used to identify maternal and fetal variants contributing to GWG in up to 10,543 mothers and up to 16,317 offspring of European origin, with replication in 10,660 mothers and 7,561 offspring. Additional analyses determined the proportion of variability in GWG from maternal and fetal common genetic variants and the overlap of established genome-wide significant variants for phenotypes relevant to GWG (e.g. maternal BMI and glucose, birthweight).\n\nResultsWe found that approximately 20% of the variability in GWG was tagged by common maternal genetic variants, and that the fetal genome made a surprisingly minor contribution to explaining variation in GWG. We were unable to identify any genetic variants that reached genome-wide levels of significance (P<5x10-8) and replicated. Some established maternal variants associated with increased BMI, fasting glucose and type 2 diabetes were associated with lower early, and higher later GWG. Maternal variants related to higher systolic blood pressure were related to lower late GWG. Established maternal and fetal birthweight variants were largely unrelated to GWG.\n\nConclusionWe found a modest contribution of maternal common variants to GWG and some overlap of maternal BMI, glucose and type 2 diabetes variants with GWG. These findings suggest that associations between GWG and later offspring/maternal outcomes may be due to the relationship of maternal BMI and diabetes with GWG.

genetics↗

bioRxiv2017

On The Relationship Between Epistasis And Genetic Variance-Heterogeneity

Epistasis and genetic variance heterogeneity are two non-additive genetic inheritance patterns that are often, but not always, related. Here we use theoretical examples and empirical results from analyses of experimental data to illustrate the connection between the two. This includes an introduction to the relationship between epistatic gene-action, statistical epistasis and genetic variance heterogeneity and a brief discussion about how other genetic processes than epistasis can also give rise to genetic variance heterogeneity.\n\nHighlightGenetic effects on the trait variance, rather than the mean, have been found in several studies. Here we discuss how this sometimes, but not always, can be caused by epistasis.

genetics↗

bioRxiv2017

Differential Variant Calling In Mutants From Diverse Genetic Backgrounds: A Case Study In The Nematode Pristionchus pacificus

Genome sequencing of mutants is one of the most widely used techniques to identify genes that control traits of interest including human diseases. Traditionally, variants are called against reference genomes and various filtering techniques are applied to reduce the number of candidate mutations. However, if the genetic background of the mutant is different from the reference genome, the number of background variants may exceed the number of true mutations by several orders of magnitude resulting in candidate lists that cannot be effectively reduced.\n\nWe introduce the problem of differential variant calling in mutants from diverse genetic backgrounds. In the example of a mutant strain of the nematode Pristionchus pacificus, where the genetic background is not identical to the reference genome ({approx}1% genome-wide divergence), we show that simple intersection filtering does not effectively reduce the list of candidate mutations due to the combined effect of high number of background mutations, missing coverage in the wildtype sample, and problematic regions in the genome assembly. Although restriction to sites with coverage in mutant and wildtype sample greatly reduced the number of candidate sites, we further improved this candidate set by implementing a customized variant calling procedure. This takes the mutant sample and the control sample of same genetic background as input and calls variants exhibiting strong discriminative signals across the two samples. Intersecting the candidate mutations with an interval identfied from mapping by RAD-seq revealed a likely splice-site mutation in the P. pacificus dpy-1 gene, which has been previously shown to cause the associated morphological phenotype.\n\nOur study shows that combined analysis of mutant and wildtype samples drastically increases the potential to find true mutations. We hope that these results may be helpful for other model systems, where the identification of candidate mutants is complicated by assembly errors, lab-derived mutations, or different genetic backgrounds.

genetics↗

bioRxiv2017

How Well Do You Know Your Mutation? Complex Effects Of Genetic Background On Expressivity, Complementation, And Ordering Of Allelic Effects

For a given gene, different mutations influence organismal phenotypes to varying degrees. However, the expressivity of these variants not only depends on the DNA lesion associated with the mutation, but also on factors including the genetic background and rearing environment. The degree to which these factors influence related alleles, genes, or pathways similarly, and whether similar developmental mechanisms underlie variation in the expressivity of a single allele across conditions and variation across alleles is poorly understood. Besides their fundamental biological significance, these questions have important implications for the interpretation of functional genetic analyses, for example, if these factors alter the ordering of allelic series or patterns of complementation. We examined the impact of genetic background and rearing environment for a series of mutations spanning the range of phenotypic effects for both the scalloped and vestigial genes, which influence wing development in Drosophila melanogaster. Genetic background and rearing environment influenced the phenotypic outcome of mutations, including intra-genic interactions, particularly for mutations of moderate expressivity. We examined whether cellular correlates (such as cell proliferation during development) of these phenotypic effects matched the observed phenotypic outcome. While cell proliferation decreased with mutations of increasingly severe effects, surprisingly it did not co-vary strongly with the degree of background dependence. We discuss these findings and propose a phenomenological model to aid in understanding the biology of genes, and how this influences our interpretation of allelic effects in genetic analysis.

genetics↗

bioRxiv2017

Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360)

General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360; age range = 16 to 102). We found 9,714 genome-wide significant SNPs (P<5 x 10-8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.

genetics↗

bioRxiv2017

Genome-wide association study of habitual physical activity in over 277,000 UK Biobank participants identifies novel variants and genetic correlations with chronotype and obesity-related traits.

Background/ObjectivesPhysical activity (PA) protects against a wide range of diseases. Engagement in habitual PA has been shown to be heritable, motivating the search for specific genetic variants that may ultimately inform efforts to promote PA and target the best type of PA for each individual.\n\nSubjects/MethodsWe used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (n=277,656) and two measures based on wrist-worn accelerometry data (n=67,808). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n=8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA).\n\nResultsWe identified 26 genome-wide loci across the five PA measures examined. Upon meta-analysis of the top hits for MVPA with results from the ARIC study, 8 of 10 remained significant at p<5x10-8. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA. Variants in CADM2, a gene recently implicated in risk-taking behavior and other personality and cognitive traits, were found to be associated with regular engagement in strenuous sports or other exercises. We also identified thirteen loci consistently associated (p<0.005) with each of the five PA measures. We find genetic correlations of PA with educational attainment traits, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions.\n\nConclusionsThese results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.

genetics↗

bioRxiv2017

Higher genetic risk for schizophrenia is associated with living in urban and populated areas

Social stress in urban life has been proposed as an environmental risk factor associated with the increased prevalence of schizophrenia in urban compared to rural areas. However, the potential genetic contributions to this relationship have been largely ignored.\n\nUsing a community-based sample of 15,544 adults living in Australia, we found higher genetic loading for schizophrenia in participants living in more densely populated (p-value=5.69*10-5) or less remote areas (p-value=0.003). Mendelian Randomization suggested that high schizophrenia genetic risk is a causal factor in chosing to live in denser (p-value=0.046) and less remote areas (p-value=0.044).\n\nOur results support the hypothesis of selective migration to more urban environments by people at higher genetic risk for schizophrenia and suggest a need to refine the social stress model for schizophrenia by including genetic influences on where people choose to live.

genetics↗

bioRxiv2017

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e. linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ~150,000 individuals give a higher accuracy than LDSC estimates based on ~400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-data sets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

genetics↗

bioRxiv2019

Gene drive and resilience through renewal with next generation Cleave and Rescue selfish genetic elements

Gene drive-based strategies for modifying populations face the problem that genes encoding cargo and the drive mechanism are subject to separation, mutational inactivation, and loss of efficacy. Resilience, an ability to respond to these eventualities in ways that restore population modification with functional genes is needed for long-term success. Here we show that resilience can be achieved through cycles of population modification with "Cleave and Rescue" (ClvR) selfish genetic elements. ClvR comprises a DNA sequence-modifying enzyme such as Cas9/gRNAs that disrupts endogenous versions of an essential gene, and a recoded version of the essential gene resistant to cleavage. ClvR spreads by creating conditions in which those lacking ClvR die because they lack functional versions of the essential gene. Cycles of modification can in principal be carried out if two ClvR elements targeting different essential genes are located at the same genomic position, and one of them, ClvRn+1, carries a Rescue transgene from an earlier element, ClvRn. ClvRn+1 should spread within a population of ClvRn, while also bringing about a decrease in its frequency. To test this hypothesis we first show that multiple ClvRs, each targeting a different essential gene, function when located at a common chromosomal position in Drosophila. We then show that when several of these also carry the Rescue from a different ClvR, they spread to transgene fixation in populations fixed for the latter, and at its expense. Therefore, genetic modifications of populations can be overwritten with new content, providing an ongoing point of control. SignificanceGene drive can spread beneficial traits through populations, but will never be a one-shot project in which one genetic element provides all desired modifications, for an indefinitely long time. Here we show that gene drive mediated population modification in Drosophila can be overwritten with new content while eliminating old, using Cleave and Rescue (ClvR) selfish genetic elements. The ability to carry out cycles of modification that create and then leave behind a minimal genetic footprint while entering and exiting a population provides important points of control. It makes possible the replacement of broken elements, upgrades with new elements that better carry out their tasks and/or provide new functions, all while promoting the removal of modifications no longer needed.

genetics↗