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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.

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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.

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bioRxiv2017

Evaluating genetic ancestry and self-reported ethnicity in the context of carrier screening

BackgroundCurrent professional society guidelines recommend genetic carrier screening be offered on the basis of ethnicity, or when using expanded carrier screening panels, they recommend to compute residual risk based on ethnicity. We investigated the reliability of self-reported ethnicity in 9138 subjects referred to carrier screening. Self-reported ethnicity gathered from test requisition forms and during post-test genetic counseling, and genetic ancestry predicted by a statistical model, were compared for concordance.\n\nResultsWe identified several discrepancies between the two sources of self-reported ethnicity and genetic ancestry. Only 30.3% of individuals who indicated Mediterranean ancestry during consultation self-reported this on requisition forms. Additionally, the proportion of individuals who reported Southeast Asian but were estimated to have a different genetic ancestry was found to depend on the source of self-report. Finally, individuals who reported Latin American demonstrated a high degree of ancestral admixture. As a result, carrier rates and residual risks provided for patient decision-making are impacted if using self-reported ethnicity.\n\nConclusionOur analysis highlights the unreliability of ethnicity classification based on patient self-reports. We recommend the routine use of pan-ethnic carrier screening panels in reproductive medicine. Furthermore, the use of an ancestry model would allow better estimation of carrier rates and residual risks.

genetics↗

bioRxiv2017

Genetics of trans-regulatory variation in gene expression

Heritable variation in gene expression provides a critical bridge between differences in genome sequence and the biology of many traits, including common human diseases. However, the sources of most regulatory genetic variation remain unknown. Here, we used transcriptome profiling in 1,012 yeast segregants to map the genetic basis of variation in gene expression with high statistical power. We identified expression quantitative trait loci (eQTL) that together account for over 70% of the total genetic contribution to variation in mRNA levels, allowing us to examine the sources of regulatory variation comprehensively. We found that variation in the expression of a typical gene has a complex genetic architecture involving multiple eQTL. We also detected hundreds of eQTL pairs with significant non-additive interactions in an unbiased genome-wide scan. Although most genes were influenced by a local eQTL located close to the gene, most expression variation arose from distant, trans-acting eQTL located far from their target genes. Nearly all distant eQTL clustered at 102 \"hotspot\" locations, some of which influenced the expression of thousands of genes. Hotspot regions were enriched for transcription factor genes and altered expression of their target genes though both direct and indirect mechanisms. Many local eQTL had no detectable effects on the expression of other genes in trans. These results reveal the complexity of genetic influences on transcriptome variation in unprecedented depth and detail.

genetics↗

bioRxiv2017

Multivariate analysis of the cotton seed ionome reveals integrated genetic signatures of abiotic stress response

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.\n\nOne sentence summaryThe cotton seed ionome has a shared genetic basis that provides insight into the physiological status of the plant.

genetics↗

bioRxiv2017

Systems genetics identifies modifiers of Alzheimer’s disease risk and resilience

Identifying genes that modify symptoms of Alzheimers disease (AD) will provide novel therapeutic strategies to prevent, cure or delay AD. To discover genetic modifiers of AD, we combined a mouse model of AD with a genetically diverse reference panel to generate F1 mice harboring identical high-risk human AD mutations but which differ across the remainder of their genome. We first show that genetic variation profoundly modifies the impact of causal human AD mutations and validate this panel as an AD model by demonstrating a high degree of phenotypic, transcriptomic, and genetic overlap with human AD. Genetic mapping was used to identify candidate modifiers of cognitive deficits and amyloid pathology, and viral-mediated knockdown was used to functionally validate Trpc3 as a modifier of AD. Overall, work here introduces a humanized mouse population as an innovative and reproducible resource for the study of AD and identifies Trpc3 as a novel therapeutic target.\n\nHighlightsO_LINew transgenic mouse population enables mapping of AD risk and resilience factors\nC_LIO_LITranscriptomic and phenotypic profiles in diverse AD mice parallel those in humans\nC_LIO_LIApoe genotype and expression correlate with cognitive symptoms in mice\nC_LIO_LITrpc3 is a novel target to reduce amyloid load and cognitive symptoms in AD\nC_LI

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bioRxiv2017

Genetic evidence for shared risks across psychiatric disorders and related traits in a Swedish population twin sample

Psychiatric traits related to categorically-defined psychiatric disorders are heritable and present to varying degrees in the general population. In this study, we test the hypothesis that genetic risk factors associated with psychiatric disorders are also associated with continuous variation in milder population traits. We combine a contemporary twin analytic approach with polygenic risk score (PRS) analyses in a large population-based twin sample. Questionnaires assessing traits of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning difficulties, tic disorders (TD), obsessive-compulsive disorder (OCD), anxiety, major depressive disorder (MDD), mania and psychotic experiences were administered to a large, Swedish twin sample. Individuals with clinical psychiatric diagnoses were identified using the Swedish National Patient Register. Joint categorical/continuous twin modeling was used to estimate genetic correlations between psychiatric diagnoses and continuous traits. PRS for psychiatric disorders were calculated based on independent discovery genetic data. The association between PRS for each disorder and related continuous traits was tested. We found mild to strong genetic correlations between psychiatric diagnoses and corresponding traits (ranging from .31-.69) in the twin analyses. There was also evidence of association between PRS for ASD, ADHD, TD, OCD, anxiety, MDD and schizophrenia with related population traits. These results indicate that genetic factors which predispose to psychiatric disorders are also associated with milder variation in characteristic traits throughout the general population, for many psychiatric phenotypes. This finding supports the conceptualization of psychiatric disorders as the extreme ends of continuous traits.

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bioRxiv2018

DNA methylation mediates genetic liability to non-syndromic cleft lip/palate

BackgroundNon-syndromic cleft lip/palate (nsCL/P) is a complex trait with genetic and environmental risk factors. Around 40 distinct genetic risk loci have been identified for nsCL/P, but many reside in non-protein-coding regions with an unclear function. We hypothesised that one possibility is that the genetic risk variants influence susceptibility to nsCL/P through gene regulation pathways, such as those involving DNA methylation.\n\nMethodsUsing nsCL/P Genome-wide association study summary data and methylation data from four studies, we used Mendelian randomization and joint likelihood mapping to identify putative loci where genetic liability to nsCL/P may be mediated by variation in DNA methylation in blood.\n\nResultsThere was evidence at three independent loci, VAX1 (10q25.3), LOC146880 (17q23.3) and NTN1 (17p13.1), that liability to nsCL/P and variation in DNA methylation might be driven by the same genetic variant. Follow up analyses using DNA methylation data, derived from lip and palate tissue, and gene expression catalogues provided further insight into possible biological mechanisms.\n\nConclusionsGenetic variation may increase liability to nsCL/P by influencing DNA methylation and gene expression at VAX1, LOC146880 and NTN1.

genetics↗

bioRxiv2018

Alzheimer’s environmental and genetic risk scores are differentially associated with ‘g’ and δ

IntroductionWe investigated the association of the Australian National University Alzheimers Disease Risk Index (ANU-ADRI) and an AD genetic risk score (GRS) with cognitive performance.\n\nMethodsThe ANU-ADRI (composed of 11 risk factors for AD) and GRS (composed of 25 AD risk loci) were computed in 1,061 community-dwelling older adults. Participants were assessed on 11 cognitive tests and activities of daily living. Structural equation modelling was used to evaluate the association of the ANU-ADRI and GRS with: 1) general cognitive ability (g) 2) dementia related variance in cognitive performance ({delta}) and 3) verbal ability, episodic memory, executive function and processing speed.\n\nResultsA worse ANU-ADRI score was associated with poorer performance in g, {delta}, and each cognitive domain. A worse GRS was associated with poorer performance in {delta} and episodic memory.\n\nDiscussionThe ANU-ADRI was broadly associated with worse cognitive performance, validating its further use in early dementia risk assessment.\n\nHighlightsO_LIAn environmental/lifestyle dementia risk index is broadly associated with cognitive performance\nC_LIO_LIAn Alzheimers genetic risk score is associated with dementia severity and episodic memory\nC_LIO_LIThe environmental risk index is more strongly associated with dementia severity than genetic risk\nC_LI\n\nResearch in ContextO_ST_ABSSystematic ReviewC_ST_ABSThe authors reviewed the literature using online databases (e.g. PubMed). Previous research has highlighted the need for dementia risk assessment tools to be evaluated on outcomes prior to dementia onset, such as cognitive performance. The relevant citations have been appropriately cited.\n\nInterpretationThe Australian National University Alzheimers Disease Risk Index (ANU-ADRI) was more broadly associated with cognitive performance than Alzheimers genetic risk. For the ANU-ADRI, stronger effects were observed for dementia-related variance in cognitive task performance that for variance in general cognitive function. This suggests that ANU-ADRI is more specifically associated with dementia-related processes and further validates its use in early risk assessment for dementia.\n\nFuture DirectionsAccordingly, future studies should seek to evaluate the association of the ANU-ADRI and genetic risk with AD biomarkers and longitudinal cognitive performance to evaluate differential trajectories in g and {delta}.

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bioRxiv2018

A genetic perspective on the relationship between eudaimonic -and hedonic well-being

Whether hedonism or eudaimonism are two distinguishable forms of well-being is a topic of ongoing debate. To shed light on the relation between the two, large-scale available molecular genetic data were leveraged to gain more insight into the genetic architecture of the overlap between hedonic and eudaimonic well-being. Hence, we conducted the first genome-wide association studies (GWAS) of eudaimonic well-being (N = [~]108K) and linked it to a GWAS of hedonic well-being (N = [~] 222K). We identified the first two genome-wide significant independent loci for eudaimonic well-being and 6 independent loci for hedonic well-being. Joint analyses revealed a moderate phenotypic correlation (r = 0.53), but a high genetic correlation (rg = 0.78) between eudaimonic and hedonic well-being. For both traits we identified enrichment in the frontal cortex -and cingulate cortex as well as the cerebellum to be top ranked. Bi-directional Mendelian Randomization analyses using two-sample MR indicated some evidence for a causal relationship from hedonic well-being to eudaimonic well-being whereas no evidence was found for the reverse. Additionally, genetic correlations patterns with a range of positive and negative related phenotypes were largely similar for hedonic -and eudaimonic well-being. Our results reveal a large genetic overlap between hedonism and eudaimonism.

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