SEARCH · Search bioRxiv

Results for “Genetics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39Linked to original sources

bioRxiv2026

Systems genetics identifies ETS1 as a stress-dependent regulator of adipocyte insulin action and heme-iron homeostasis

White adipose tissue plays a central role in systemic energy homeostasis by buffering nutrient excess through insulin-stimulated glucose uptake and triglyceride storage. Despite its importance, the genetic and molecular mechanisms governing adipose tissue insulin action remain poorly defined because tissue-specific insulin responsiveness has been difficult to quantify at the scale required for genetic discovery. Here, we developed the first scalable platform for high-throughput genetic mapping of tissue-specific insulin action in adipose tissue, enabling systems genetic analysis across 559 genetically diverse Diversity Outbred Australia (DOz) mice. Genetic analysis accounting for adiposity identified 39 loci associated with adipose tissue insulin action, demonstrating that adipose insulin responsiveness is a genetically encoded trait that captures a dimension of metabolic health beyond adiposity. Among these, a strong diet-dependent locus on chromosome 9 encompassed the transcription factor Ets1. Functional studies demonstrated that Ets1 silencing selectively restored insulin-stimulated glucose uptake in insulin-resistant adipocytes. Proteomic profiling revealed that ETS1 orchestrates a stress-responsive program involving heme metabolism, iron handling and redox homeostasis. Consistent with this, ETS1 knockdown reduced cellular heme and labile iron levels and attenuated oxidative stress under insulin-resistant conditions. Collectively, these findings demonstrate the power of systems genetics to identify previously unrecognised regulators of adipose insulin action and establish the heme-iron axis as a critical determinant of adipocyte insulin responsiveness.

systems biology↗

bioRxiv2014

The Genetic Legacy of the Expansion of Turkic-Speaking Nomads Across Eurasia

The Turkic peoples represent a diverse collection of ethnic groups defined by the Turkic languages. These groups have dispersed across a vast area, including Siberia, Northwest China, Central Asia, East Europe, the Caucasus, Anatolia, the Middle East, and Afghanistan. The origin and early dispersal history of the Turkic peoples is disputed, with candidates for their ancient homeland ranging from the Transcaspian steppe to Manchuria in Northeast Asia. Previous genetic studies have not identified a clear-cut unifying genetic signal for the Turkic peoples, which lends support for language replacement rather than demic diffusion as the model for the Turkic languages expansion. We addressed the genetic origin of 373 individuals from 22 Turkic-speaking populations, representing their current geographic range, by analyzing genome-wide high-density genotype data. Most of the Turkic peoples studied, except those in Central Asia, genetically resembled their geographic neighbors, in agreement with the elite dominance model of language expansion. However, western Turkic peoples sampled across West Eurasia shared an excess of long chromosomal tracts that are identical by descent (IBD) with populations from present-day South Siberia and Mongolia (SSM), an area where historians center a series of early Turkic and non-Turkic steppe polities. The observed excess of long chromosomal tracts IBD (> 1cM) between populations from SSM and Turkic peoples across West Eurasia was statistically significant. Finally, we used the ALDER method and inferred admixture dates ([~]9th-17th centuries) that overlap with the Turkic migrations of the 5th-16th centuries. Thus, our results indicate historical admixture among Turkic peoples, and the recent shared ancestry with modern populations in SSM supports one of the hypothesized homelands for their nomadic Turkic and related Mongolic ancestors.\n\nAuthor SummaryCenturies of nomadic migrations have ultimately resulted in the distribution of Turkic languages over a large area ranging from Siberia, across Central Asia to Eastern Europe and the Middle East. Despite the profound cultural impact left by these nomadic peoples, little is known about their prehistoric origins. Moreover, because contemporary Turkic speakers tend to genetically resemble their geographic neighbors, it is not clear whether their nomadic ancestors left an identifiable genetic trace. In this study, we show that Turkic-speaking peoples sampled across the Middle East, Caucasus, East Europe, and Central Asia share varying proportions of Asian ancestry that originate in a single area, southern Siberia and Mongolia. Mongolic- and Turkic-speaking populations from this area bear an unusually high number of long chromosomal tracts that are identical by descent with Turkic peoples from across west Eurasia. Admixture induced linkage disequilibrium decay across chromosomes in these populations indicates that admixture occurred during the 9th-17th centuries, in agreement with the historically recorded Turkic nomadic migrations and later Mongol expansion. Thus, our findings reveal genetic traces of recent large-scale nomadic migrations and map their source to a previously hypothesized area of Mongolia and southern Siberia.

Genetics↗

bioRxiv2015

Two variance component model improves genetic prediction in family data sets

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively using Best Linear Unbiased Prediction (BLUP) methods. Such methods were pioneered in the plant and animal breeding literature and have since been applied to predict human traits with the aim of eventual clinical utility. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two variance component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated using genetic markers. In simulations using real genotypes from CARe and FHS family cohorts, we demonstrate that the two variance component model achieves gains in prediction r2 over standard BLUP at current sample sizes, and we project based on simulations that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two variance component model significantly improves prediction r2 in each case, with up to a 20% relative improvement. We also find that standard mixed model association tests can produce inflated test statistics in data sets with related individuals, whereas the two variance component model corrects for inflation.\n\nAuthor SummaryGenetic prediction has been well-studied in plant and animal breeding and has generated considerable recent interest in human genetics, both in family data sets and in population cohorts. Many prediction studies are based on the widely used Best Linear Unbiased Prediction (BLUP) approach, which performs a mixed model analysis using a genetic relationship matrix that is either estimated from genotype data--thus measuring identity-by-state (IBS) sharing--or obtained from family pedigree information. We show here that a substantial improvement in prediction accuracy in family data sets can be obtained by jointly modeling both IBS sharing and approximate pedigree structure, both estimated using genetic markers, using separate variance components within a two variance component mixed model. We demonstrate the performance of this model in simulations and real data sets. We also show that previous mixed model association methods suffer from inflated test statistics in family data sets due to their failure to account for the different heritability parameters corresponding to IBS sharing vs. pedigree relatedness. Our two variance component model provides a solution to this problem without compromising statistical power.

Genetics↗

bioRxiv2016

The genetic basis of host choice and resting behavior in the major African malaria vector, Anopheles arabiensis

Malaria transmission is dependent on the propensity of Anopheles mosquitoes to bitehumans (anthropophily) instead of other dead end hosts. Recent increases in the usage of Long Lasting Insecticide Treated Nets (LLINs) in Africa have been associated with reductions in highly anthropophilic vectors such as Anopheles gambiae s.s.,leaving less anthropophilic species such as Anopheles arabiensis as the most prominent remaining source of transmission in many settings. An.arabiensis is more of a generalist in terms of its host choice and resting behavior, which may be due to phenotypic plasticity and/or segregating allelic variation. To investigate the potential genetic basis of host choice and resting behavior in An. arabiensis we performed a genome-wide association study on host choice (human-or cattle-fed) and resting position (collected indoors or outdoors) in the Kilombero Valley, Tanzania. This represents the first genomic/molecular analysis of host choice and resting behavior in a malaria vector. We identified a total of 4,820,851 SNPs, which were used to conduct the first genome-wide estimates of 'SNP heritability' for host choice and resting behavior in this species. A genetic component was detected for host choice (human vs cow fed; permuted P = 0.002), but there was no evidence of a genetic component for resting behavior (indoors versus outside; permuted P = 0.465). A principal component analysis (PCA) segregated individuals based on genomic variation into three groups which are characterized by differences at the 2Rb and/or 3Ra paracentromeric chromosome inversions. There was a non-random distribution of cattle-fed mosquitoes between the PCA clusters, suggesting that alleles linked to the 2Rb and/or 3Ra inversions may influence host choice. Using a novel inversion genotyping assay, we detected a significant enrichment of the standard arrangement (non-inverted) of 3Ra among cattle-fed mosquitoes (N=129) versus all non-cattle-fed individuals (N=234; 2, p=0.007). Thus, tracking the frequency of the 3Ra in An. arabiensis populations is important, especially in relation to the emergence of behavioral avoidance(e.g. shifting toward cattle-feeding) in some populations. A better understanding of the genetic basis for host choice in An. arabiensis may also open avenues for novel vector control strategies based on introducing genes for zoophily into wild mosquito populations.\n\nAuthor summaryMalaria transmission is driven by the propensity for mosquito vectors to bite people, whilst its control depends on the tendency of mosquitoes to bite and rest in places where they will come into contact with insecticides. In many parts of Africa, Anopheles arabiensis is now the only remaining vec 63 tor in areas where coverage with Long Lasting Insecticide Treated Nets is high. We sought to assess the potential for An. arabiensis to adapt its behavior to avoid control measures by investigating the genetic basis for its host choice and resting behavior. Blood fed An. arabiensis were collected resting indoors and outdoors in the Kilombero Valley, Tanzania. We sequenced a total of 48 genomes representing 4 phenotypes (human or cow fed, resting in or outdoors) and tested for a genetic basis for each phenotype. Genomic analysis followed up by application of a novel molecular karyotyping assay revealed a relationship between An. arabiensis that fed on cattle and the standard arrangement of the 3Ra inversion. This indicates that the host choice behavior of An. arabiensis has has a substantial genetic component. Validation with controlled host preference assays comparing individuals with the standard and inverted arrangement of 3Ra is still needed.

Genetics↗

bioRxiv2017

Estimation of Pairwise Genetic Distances Under Independent Sampling of Segregating Sites vs. Haplotype Sampling

Genetic distance is a standard measure of variation in populations. When sequencing genomes individually, genetic distances are computed over all pairs of multilocus haplotypes in a sample. However, when next-generation sequencing methods obtain reads from heterogeneous assemblages of genomes (e.g. for microbial samples in a biofilm or cells from a tumor), individual reads are often drawn from different genomes. This means that pairwise genetic distances are calculated across independently sampled sites rather than across haplotype pairs. In this paper, we show that while the expected pairwise distance under whole haplotype sampling (WHS) is the same as with independent locus sampling (ILS), the sample variances of pairwise distance differ and depend on the direction and magnitude of linkage disequilibrium (LD) among polymorphic sites. We derive a weighted LD value that, when positive, predicts higher sample variance in estimated genetic distance for WHS. Weighted LD is positive when on average, the most common alleles at two loci are in positive LD. Using individual-based simulations of an infinite sites model under Fisher-Wright genetic drift, variances of estimated genetic distance are found to be almost always higher under WHS than under ILS, suggesting a reduction in estimation error when sites are sampled independently. We apply these results to haplotype frequencies from a lung cancer tumor to compute weighted LD and the variances in estimated genetic distance under ILS vs. WHS, and find that the the relative magnitudes of variances under WHS vs. ILS are sensitive to sampled allele frequencies.

genetics↗

bioRxiv2017

Genetic Analysis of a Metazoan Pathway using Transcriptomic Phenotypes

RNA-seq is commonly used to identify genetic modules that respond to perturbations. In single cells, transcriptomes have been used as phenotypes, but this concept has not been applied to whole-organism RNA-seq. Linear models can quantify expression effects of individual mutants and identify epistatic effects in double mutants. To make interpretation of these high-dimensional measurements intuitive, we developed a single coefficient to quantify transcriptome-wide epistasis that accurately reflects the underlying interactions. To demonstrate our approach, we sequenced four single and two double mutants of Caenorhabditis elegans. From these mutants, we reconstructed the known hypoxia pathway. In addition, we uncovered a class of 56 genes that have opposing changes in expression in egl-9(lf) compared to vhl-1(lf) but the egl-9(lf); vhl-1(lf) mutant has the same phenotype as egl-9(lf). This class violates the classical model of HIF-1 regulation, but can be explained by postulating a role of hydroxylated HIF-1 in transcriptional control.\n\nSignificance StatementTranscriptome profiling is a way to quickly and quantitatively measure gene expression level. Because of their quantitative nature, there is widespread interest in using transcriptomic profiles as a phenotype for genetic analysis. However, a source of major concern is that whole-animal transcriptomic profiles mix the expression signatures of multiple cellular states, making it hard to accurately reconstruct genetic interactions. Additionally, it has been difficult to quantify epistasis, the signature of genetic interaction between two genes, in these molecular phenotypes. Here, we show that it is possible to accurately reconstruct genetic interactions between genes using whole-animal RNA sequencing, and we demonstrate a powerful new way to measure and understand epistasis arising from these measurements. This suggests that whole-organism RNA-seq can be a powerful tool with which to understand genetic interactions in entire organisms and not only in isolated cells. With the advent of genome engineering tools, generating mutants has become easier and faster for many organisms. As mutants become easier to create, phenotyping them has become a major bottleneck in understanding the biological functions of the genes in question. Our work presents a possible solution to this problem, because transcriptome profiling is fast and sensitive to genetic perturbations regardless of the context they operate in.

genetics↗

bioRxiv2017

A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics

Despite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses we demonstrate that our method provides accurate covariance estimates, thus enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal {approx} 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimers disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOADs correlation with cognitive traits and hints at an autoimmune component for ALS.

genetics↗

bioRxiv2017

Environmental factors dominate over host genetics in shaping human gut microbiota composition

Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the term biome-explainability as the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16-33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

genetics↗

bioRxiv2017

Immune-related genetic enrichment in frontotemporal dementia

BackgroundConverging evidence suggests that immune-mediated dysfunction plays an important role in the pathogenesis of frontotemporal dementia (FTD). Although genetic studies have shown that immune-associated loci are associated with increased FTD risk, a systematic investigation of genetic overlap between immune-mediated diseases and the spectrum of FTD-related disorders has not been performed.\n\nMethods and findingsUsing large genome-wide association studies (GWAS) (total n = 192,886 cases and controls) and recently developed tools to quantify genetic overlap/pleiotropy, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with FTD-related disorders namely FTD, corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS) - and one or more immune-mediated diseases including Crohns disease (CD), ulcerative colitis (UC), rheumatoid arthritis (RA), type 1 diabetes (T1D), celiac disease (CeD), and psoriasis (PSOR). We found up to 270-fold genetic enrichment between FTD and RA and comparable enrichment between FTD and UC, T1D, and CeD. In contrast, we found only modest genetic enrichment between any of the immune-mediated diseases and CBD, PSP or ALS. At a conjunction false discovery rate (FDR) < 0.05, we identified numerous FTD-immune pleiotropic SNPs within the human leukocyte antigen (HLA) region on chromosome 6. By leveraging the immune diseases, we also found novel FTD susceptibility loci within LRRK2 (Leucine Rich Repeat Kinase 2), TBKBP1 (TANK-binding kinase 1 Binding Protein 1), and PGBD5 (PiggyBac Transposable Element Derived 5). Functionally, we found that expression of FTD-immune pleiotropic genes (particularly within the HLA region) is altered in postmortem brain tissue from patients with frontotemporal dementia and is enriched in microglia compared to other central nervous system (CNS) cell types.\n\nConclusionsWe show considerable immune-mediated genetic enrichment specifically in FTD, particularly within the HLA region. Our genetic results suggest that for a subset of patients, immune dysfunction may contribute to risk for FTD. These findings have potential implications for clinical trials targeting immune dysfunction in patients with FTD.

genetics↗

bioRxiv2017

A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence

Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at rg =0.73 and rg =0.70 respectively. This allowed us to utilize a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al. 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al. 2017). This study had four goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample. We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system. We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests. Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.

genetics↗

bioRxiv2017

A set informative multiple autosomal markers for human identification: forensic research and population genetics analysis in a Chinese Xinjiang Hui group

In recent years, insertion/deletion (InDel) markers became a promising and useful supporting tool in forensic identification cases and biogeographic research field. In this study, 30 InDel loci were explored to reveal the genetic diversities and genetic relationships between Chinese Xinjiang Hui group and the 24 previously studied populations using varies methods such as forensic statistical parameter analysis, phylogenetic reconstruction, STRUCTURE analysis, multi-dimensional scaling, and principal component analysis. The observed heterozygosity and expected heterozygosity ranged from 0.1971 (HLD118) to 0.5092 (HLD 92), 0.2222 (HLD 114) to 0.5000 (HLD 6), respectively. Besides, after Bonferroni correction, no deviations from Hardy-Weinberg equilibrium tests were found at all 30 loci in Xinjiang Hui group. The cumulative probability of exclusion and combined discrimination power were 0.988849 and 0.99999999999378, respectively, which indicated that the 30 loci could be used as complementary genetic markers for paternity test and be qualified for personal identification in forensic cases. In this study, we found that Xinjiang Hui group had close relationships with most Chinese groups, especially Han populations, and all the results based on different genetic methods we used had a strong support for this finding. The 30 InDel loci has important significance in forensic identification research, in spite of this, for a better understanding of genetic background of the Chinese Xinjiang Hui group, molecular genetic genotyping at various genetic markers is necessary in future studies.\n\nSummary StatementWe report here, a promising Individual identification and population differentiation maker which could be used in forensic cases.

genetics↗

bioRxiv2017

Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

Study Objectives: Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects. Methods: Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a morning versus an evening person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods. Results: There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000, p=0.998), chronotype (rg=0.086, p=0.192) or insomnia (rg=-0.034, p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around. Conclusions: We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

genetics↗

bioRxiv2020

Spatiotemporal Genetic Diversity of Lions

The Scramble for Africa in the late 1800s marked the beginning of increased human population growth in Africa. Here, we determined the genetic architecture of both historical and modern lions to identify changes in genetic diversity that occurred during this period of landscape and anthropogenic change. We surveyed microsatellite and mitochondrial genetic variation from 143 high-quality museum specimens of known provenance and combined them with data from recently published nuclear and mitochondrial studies. Analysis of variation at 9 microsatellites and 280 polymorphic mitogenome SNPs indicate the presence of male-mediated gene flow and recent isolation of local subpopulations, likely due to habitat fragmentation. Nuclear markers showed a significant decrease in genetic diversity from the historical (HE=0.833) to the modern (HE=0.796) populations, while mitochondrial genetic diversity was maintained (Hd=0.98 for both). While the historical population appears to have been panmictic based on nDNA data, hierarchical structure analysis identified four tiers of fine structure in modern populations, able to detect most sampling locations. Mitochondrial analyses identified 4 clusters: Southern, Mixed, Eastern, and Western; and were consistent between modern and historically sampled haplotypes. Within the last century, habitat fragmentation caused lion subpopulations to become more isolated as human expansion changed the African landscape. This resulted in an increase in fine-scale nuclear genetic structure and loss of genetic diversity as subpopulations became more differentiated, while mitochondrial structure and diversity was maintained over time.

genetics↗

bioRxiv2020

Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling

The animals reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluators knowledge and interpretation of temperament, which may require substantial experience. Selection based on such subjective scores may not precisely change temperament preferences in cattle. The objectives of this study were to investigate the underlying genetic interrelationships among temperament measurements using genetic factor analytic modeling and validate a movement-based objective method (four-platform standing scale, FPSS) as a measure of temperament. Relationships among subjective methods of docility score (DS), temperament score (TS), 12 qualitative behavior assessment (QBA) attributes and objective FPSS including the standard deviation of total weight on FPSS over time (SSD) and coefficient of variation of SSD (CVSSD) were investigated using 1,528 calves at weaning age. An exploratory factor analysis (EFA) identified two latent variables account for TS and 12 QBA attributes, termed difficult and easy from their characteristics. Inclusion of DS in EFA was not a good fit because it was evaluated under restraint and other measures were not. A Bayesian confirmatory factor analysis inferred the difficult and easy scores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships among difficult, easy, DS, SSD, and CVSSD. Estimates of heritability ranged from 0.18 to 0.4 with the posterior standard deviation averaging 0.06. The factors of difficult and easy exhibited a large negative genetic correlation of -0.92. Moderate genetic correlation was found between DS and difficult (0.36), easy (-0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS with difficult (CVSSD: 0.35; SSD: 0.42) and easy (CVSSD: -0.35; SSD: -0.4). Correlation coefficients indicate selection could be performed with either and have similar outcomes. We contend that genetic factor analytic modeling provided a new approach to unravel the complexity of animal behaviors and FPSS-like measures could increase the efficiency of genetic selection by providing automatic, objective, and consistent phenotyping measures that could be an alternative of DS, which has been widely used in beef production.

genetics↗

bioRxiv2020

Genetic predictors of participation in optional components of UK Biobank

Large studies (e.g. UK Biobank) are increasingly used for GWAS and Mendelian randomization (MR) studies. Selection into and dropout from studies may bias genetic and phenotypic associations. We examine genetic factors affecting participation in four optional components in up to 451,306 UK Biobank participants. We used GWAS to identify genetic variants associated with participation, MR to estimate effects of phenotypes on participation, and genetic correlations to compare participation bias across different studies. 32 variants were associated with participation in one of the optional components (P<6x10-9), including loci with known links to intelligence and Alzheimers disease. Genetic correlations demonstrated that participation bias was common across studies. MR showed that longer educational duration, older menarche and taller stature increased participation, whilst higher levels of adiposity, dyslipidaemia, neuroticism, Alzheimers and schizophrenia reduced participation. Our effect estimates can be used for sensitivity analysis to account for selective participation biases in genetic or non-genetic analyses.

genetics↗

bioRxiv2020

Is genetic liability to ADHD and ASD causally linked to educational attainment?

BackgroundIndividuals with Attention Deficit Hyperactivity Disorder (ADHD) or Autism Spectrum Disorder (ASD) are at risk of poor educational outcomes. Parental educational attainment has also been associated with risk of ADHD/ASD in the offspring. Despite evidence that ADHD and ASD show genetic links to educational attainment, less is known on the causal nature of the associations and the possible role of IQ. MethodsWe assessed the total causal effects of genetic liability to ADHD/ASD on educational attainment using two-sample Mendelian randomization (MR). We assessed the possible contribution of IQ to the identified causal effects by estimating the "direct" effects of ADHD/ASD on educational attainment, independent of IQ, using Multivariable MR (MVMR). Reverse direction analyses were performed. The latest GWAS meta-analyses of ADHD, ASD, educational attainment and IQ were used. Causal effect estimates were generated using inverse variance weighted models (IVW). Sensitivity analyses were performed to assess the robustness of the estimates and the presence of pleiotropy. ResultsGenetic liability to ADHD had a total (MRIVW:-3.3 months per doubling of liability to ADHD; 95%CI: -4.8 to -1.9; pval= 5*10-6) and direct negative causal effect on educational attainment (MVMRIVW:-1.6 months per doubling of liability to ADHD; 95%CI: -2.5 to -0.6; pval= 4*10-4). There was little evidence of a total causal effect of genetic liability to ASD on educational attainment (MRIVW: 4 days, per doubling of liability to ASD; 95%CI: -4.9 months to 5.6 months; pval= 0.9) but some evidence of a direct effect not via IQ (MVMRIVW:29 days per doubling the genetic liability to ASD; 95%CI: 2 to 48; pval= 0.03). Reverse direction analyses suggested that genetic liability to higher educational attainment was associated with lower risk of ADHD (MRIVWOR: 0.3 per standard deviation (SD) increase; 95%CI: 0.26 to 0.36; pval= 6*10-51), even after IQ was entered in the models (MVMRIVWOR: 0.33 per SD increase; 95%CI: 0.26 to 0.43; pval= 6*10-17). On the contrary, there was evidence consistent with a positive causal effect of genetic liability to higher educational attainment on risk of ASD (MRIVWOR: 1.51 per SD increase; 95%CI: 1.29 to 1.77; pval= 4*10-7), which was found to be largely explained by IQ (MVMRIVWOR per SD increase: 1.24; 95%CI: 0.96 to 1.60; pval= 0.09). ConclusionsOur findings suggest that despite the genetic and phenotypic overlap between ADHD and ASD, they present highly differentiated causal associations with educational attainment. This highlights the necessity for specialized educational interventions for children with ADHD and ASD. Further research is needed in order to decipher whether the identified causal effects reflect parentally transmitted effects, diagnostic masking, or selection bias.

genetics↗

bioRxiv2020

Identification of novel mitochondrial and mitochondrial related genetic loci associated with exercise response in the Gene SMART study

Mitochondria supply intracellular energy requirements during exercise. Specific mitochondrial haplogroups and mitochondrial genetic variants have been associated with athletic performance, and exercise responses. However, these associations were discovered using underpowered, candidate gene approaches, and consequently have not been replicated. Here, we used whole-mitochondrial genome sequencing, in conjunction with high-throughput genotyping arrays, to discover novel genetic variants associated with exercise responses in the Gene SMART (Skeletal Muscle Adaptive Response to Training) cohort (n=62 completed). We performed a Principal Component Analysis of cohort aerobic fitness measures to build composite traits and test for variants associated with exercise outcomes. None of the mitochondrial genetic variants but nine nuclear encoded variants in eight separate genes were found to be associated with exercise responses (FDR<0.05) (rs11061368: DIABLO, rs113400963: FAM185A, rs6062129 and rs6121949: MTG2, rs7231304: AFG3L2, rs2041840: NDUFAF7, rs7085433: TIMM23, rs1063271: SPTLC2, rs2275273: ALDH18A1). Additionally, we outline potential mechanisms by which these variants may be contributing to exercise phenotypes. Our data suggest novel nuclear-encoded SNPs and mitochondrial pathways associated with exercise response phenotypes. Future studies should focus on validating these variants across different cohorts and ethnicities. AUTHOR SUMMARYPrevious exercise genetic studies contain many flaws that impede the growth in knowledge surrounding change in exercise outcomes. In particular, exercise studies looking at mtDNA variants have looked at very small portions of the mitochondrial genome. Mitochondria are the power house of the cell and therefore understanding the mitochondrial genetics behind adaptations to training can help us fill knowledge gaps in current research. Here, we utilised a new mitochondrial genetic sequencing technique to examine all mitochondrial and mitochondrial related genetic variations. We have shown that there were no mitochondrial specific variants that influenced exercise training however there were 9 related variants that were significantly associated with exercise phenotypes. Additionally, we have shown that building composite traits increased the significance of our association testing and lead to novel findings. We will be able to understand why response to training is so varied and increase the effectiveness of exercise training on a host of metabolic disorders.

genetics↗

bioRxiv2020

Risk of preeclampsia in patients with genetic predisposition to common medical conditions: a case-control study

ObjectiveTo assess whether women with a genetic predisposition to medical conditions known to increase preeclampsia risk have an increased risk of preeclampsia in pregnancy. DesignCase-control study. Setting and populationPreeclampsia cases (n=498) and controls (n=1864) of European ancestry from 5 US sites genotyped on a cardiovascular gene-centric array. MethodsSignificant single nucleotide polymorphisms (SNPs) from 21 traits in 7 disease categories (cardiovascular, inflammatory/autoimmune, insulin resistance, liver, obesity, renal, thrombophilia) with published genome-wide association studies (GWAS) were used to create a genetic instrument for each trait. Multivariable logistic regression was used to test the association of each continuous, scaled genetic instrument with preeclampsia. Odds of preeclampsia were compared across quartiles of the genetic instrument and evaluated for significance using a test for trend. Main Outcome Measurespreeclampsia. ResultsAn increasing burden of risk alleles for elevated diastolic blood pressure (DBP) and increased body mass index (BMI) were associated with an increased risk of preeclampsia (DBP: overall OR 1.11 (1.01-1.21), p=0.025; BMI: OR 1.10 (1.00-1.20), p=0.042), while risk alleles associated with elevated alkaline phosphatase (ALP) were protective (OR 0.89 (0.82-0.97), p=0.008), driven primarily by pleiotropic effects of variants in the FADS gene region. The effect of DBP genetic loci was even greater in early-onset (<34 weeks) preeclampsia cases (OR 1.30 (1.08-1.56), p=0.005). For all other traits, the genetic instrument was not robustly associated with preeclampsia risk. ConclusionsThese results suggest that the underlying genetic architecture of preeclampsia is shared with other disorders, specifically hypertension and obesity. TWEETABLE ABSTRACTGenetic predisposition to increased diastolic blood pressure and obesity increases the risk of preeclampsia.

genetics↗