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Results for “genetics”

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bioRxiv2020

A genome-wide association study finds genetic variants associated with neck or shoulder pain in UK Biobank

BackgroundCommon types of musculoskeletal conditions include pain in the neck and shoulder areas. This study seeks to identify the genetic variants associated with neck or shoulder pain based on a genome-wide association approach using 203,309 subjects from the UK Biobank cohort and look for replication evidence from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and TwinsUK. MethodsCases in the UK Biobank were determined by a question which asked the participants if they had experienced pain in the neck or shoulder in the previous month influencing daily activities. Controls were the UK Biobank participants who reported no pain anywhere in the last month. A genome-wide association study was performed adjusting for age, sex, BMI and 9 population principal components. Significant and independent genetic variants were then sent to GS:SFHS and TwinsUK for replication. ResultsWe identified 3 genetic loci that were associated with neck or shoulder pain in the UK Biobank samples. The most significant locus was in an intergenic region in chromosome 17, rs12453010, having P = 1.66 x 10-11. The second most significant locus was located in the FOXP2 gene in chromosome 7 with P = 2.38 x 10-10 for rs34291892. The third locus was located in the LINC01572 gene in chromosome 16 with P = 4.50 x 10-8 for rs62053992. In the replication stage, among 4 significant and independent genetic variants, rs2049604 in the FOXP2 gene and rs62053992 in the LINC01572 gene were weakly replicated in GS:SFHS (P = 0.0240 and P = 0.0202, respectively). None of the single nucleotide polymorphisms (SNPs) were replicated in the TwinsUK cohort (P > 0.05). ConclusionsWe have identified 3 loci associated with neck or shoulder pain in the UK Biobank cohort, two of which were weakly supported in a replication cohort. Further evidence is needed to confirm their roles in neck or shoulder pain. SignificanceThis is the first genome-wide association study on neck or shoulder pain. We have identified 3 genetic loci (an intergenic region in chromosome 17, the FOXP2 gene in chromosome 7, and the LINC01572 gene in chromosome 16) that are associated with neck or shoulder pain using the UK Biobank cohort, among which the FOXP2 gene and the LINC01572 gene were weakly replicated by the Generation Scotland: Scottish Family Health Study (P < 0.05). The SNP heritability was 0.11, indicating neck or shoulder pain is a heritable trait. The tissue expression analysis suggested that neck or shoulder pain was related to multiple brain tissues, indicating the involvement of neuron function. The results will inform further research in the characterisation of the mechanisms of neck or shoulder pain.

genetics↗

bioRxiv2020

Transferability, development of Single Sequence Repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

In Sub-Saharan Africa, the fan palm Borassus aethiopum Mart. is an important non-timber forest product-providing palm that faces multiple anthropogenic threats to its genetic diversity. However, this species is so far under-studied, which prevents its sustainable development as a resource. The present work is a first attempt at characterizing the genetic diversity of this palm species as well as its spatial structuration in Benin, West Africa. During a first phase we implemented a microsatellite markers-based approach relying on the reported transferability of primers developed in other palm species and found that, in disagreement with previously published results, only 22.5% of the 80 markers tested enabled amplification of African fan palm DNA and polymorphism detection was insufficient. During a second phase, we therefore generated a B. aethiopum-specific genomic dataset through high-throughput sequencing and used it for the de novo detection of potential microsatellite markers. Among these, 11 enabled polymorphism detection and were further used for analyzing genetic diversity in nine B. aethiopum populations. Our results show that genetic diversity of Beninese fan palm populations is low, with an overall average expected heterozygosity (He) of 0.354. Moreover, the positive values of the fixation index (F) in populations from both the Central (Soudano-Guinean) and the Southern (Guinean) regions suggest limited gene flows. Our analysis show that sampled B. aethiopum populations are clustered into two groups, one spanning populations from both the Southern and most of the Central region, and the other including the Central population of Save (which also has the highest He) and populations from the North. In light of our results, we discuss the use of inter-species transfer vs. de novo development of microsatellite markers in genetic diversity analyses targeting under-studied species. We also suggest future applications for the molecular resources generated through the present study.

genetics↗

bioRxiv2020

The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?

Neuropsychiatric phenotypes have been long known to be influenced by heritable risk factors. The past decade of genetic studies have confirmed this directly, revealing specific common and rare genetic variants enriched in disease cohorts. However, the early hope for these studies--that only a small set of genes would be responsible for a given disorder--proved false. The picture that has emerged is far more complex: a given disorder may be influenced by myriad coding and noncoding variants of small effect size, and/or by rare but severe variants of large effect size, many de novo. Noncoding genomic sequences harbor a large portion of these variants, the molecular functions of which cannot usually be inferred from sequence alone. This creates a substantial barrier to understanding the higher-order molecular and biological systems underlying disease risk. Fortunately, a proliferation of genetic technologies--namely, scalable oligonucleotide synthesis, high-throughput RNA sequencing, CRISPR, and CRISPR derivatives--have opened novel avenues to experimentally identify biologically significant variants en masse. These advances have yielded an especially versatile technique adaptable to large-scale functional assays of variation in both untranscribed and untranslated regulatory features: Massively Parallel Reporter Assays (MPRAs). MPRAs are powerful molecular genetic tools that can be used to screen tens of thousands of predefined sequences for functional effects in a single experiment. This approach has several ideal features for psychiatric genetics, but remains underutilized in the field to date. To emphasize the opportunities MPRA holds for dissecting psychiatric polygenicity, we review here its applications in the literature, discuss its ability to test several biological variables implicated in psychiatric disorders, illustrate this flexibility with a proof-of-principle, in vivo cell-type specific implementation of the assay, and envision future outcomes of applying MPRA to both computational and experimental neurogenetics.

genetics↗

bioRxiv2020

Rare Genetic Variants Underlie Outlying levels of DNA Methylation and Gene-Expression

Testing the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on low-level genomic traits, such as gene expression or DNA methylation (DNAm), as effect sizes are expected to be larger for low-level compared to higher-order complex traits. Here, we investigate DNAm in healthy ageing populations - the Lothian Birth cohorts of 1921 and 1936 and identify both transient and stable outlying DNAm levels across the genome. We find an enrichment of rare genetic variants within 1kb of DNAm sites in individuals with stable outlying DNAm, implying genetic control of this extreme variation. Using a family-based cohort, the Brisbane Systems Genetics Study, we observed increased sharing of DNAm outliers among more closely related individuals, consistent with these outliers being driven by rare genetic variation. We demonstrated that outlying DNAm levels have a functional consequence on gene expression levels, with extreme levels of DNAm being associated with gene expression levels towards the tails of the population distribution. Overall, this study demonstrates the role of rare variants in the phenotypic variation of low-level genomic traits, and the effect of extreme levels of DNAm on gene expression.

genetics↗

bioRxiv2020

A 3K Axiom(R) SNP array from a transcriptome-wide SNP resource sheds new light on the genetic diversity and structure of the iconic subtropical conifer tree Araucaria angustifolia (Bert.) Kuntze

High-throughput SNP genotyping has become a precondition to move to higher precision and wider genome coverage genetic analysis of natural and breeding populations of non-model species. We developed a 44,318 annotated SNP catalog for Araucaria angustifolia, a grandiose subtropical conifer tree, one of the only two native Brazilian gymnosperms, critically endangered due to its valuable wood and seeds. Following transcriptome assembly and annotation, SNPs were discovered from RNA-seq and pooled RAD-seq data. From the SNP catalog, an Axiom(R) SNP array with 3,038 validated SNPs was developed and used to provide a comprehensive look at the genetic diversity and structure of 15 populations across the natural range of the species. RNA-seq was a far superior source of SNPs when compared to RAD-seq in terms of conversion rate to polymorphic markers on the array, likely due to the more efficient complexity reduction of the huge conifer genome. By matching microsatellite and SNP data on the same set of A. angustifolia individuals, we show that SNPs reflect more precisely the actual genome-wide patterns of genetic diversity and structure, challenging previous microsatellite-based assessments. Moreover, SNPs corroborated the known major north-south genetic cline, but allowed a more accurate attribution to regional versus among-population differentiation, indicating the potential to select ancestry-informative markers. The availability of a public, user-friendly 3K SNP array for A. angustifolia and a catalog of 44,318 SNPs predicted to provide [~]29,000 informative SNPs across [~]20,000 loci across the genome, will allow tackling still unsettled questions on its evolutionary history, toward a more comprehensive picture of the origin, past dynamics and future trend of the species genetic resources. Additionally, but not less importantly, the SNP array described, unlocks the potential to adopt genomic prediction methods to accelerate the still very timid efforts of systematic tree breeding of A. angustifolia.

genetics↗

bioRxiv2020

Combinatorial analysis of gene regulatory network reveals the causal genetic basis of breast cancer and gene-specific personalized drug treatments

Cancer is the major burden of diseases around the world. The incidence and mortality rate of cancers is mounting up with the passage of days. Breast cancer is the most demoralizing cause of death, where both diseases interlocked with each other due to some genetic, biological and behavioral motives. The molecular mechanism of breast cancer through which they crop up and manifest together remains questionable. The genetic basis of protein-protein interactions and gene networks has elucidated a group of gene regulatory systems in Breast cancer. Thus, the extraction of all genomic and proteomic data has enabled unprecedented views of gene-protein co-expression, co-regulation, and interactions in the biological system. This study explored the biological system to develop a gene-disease interaction model by implementing the extracted genomic and proteomic data of Breast cancer. The disease-specific and correlated genes were pulled out and their cabling studied by PPI, disease pathway and drug-disease interaction data to articulate their role in disease development. By analyzing mined genes that are related to breast cancer, a network model is also proposed. Exploration of all the correlated genes, Hub and common genes have given some promising pieces of evidence surrounding the genetic networking models. The result of this prospective study disclosed breast cancer mediated crosslinking or possible metastatic relation on a genetic basis. Moreover, other diseases like prostate, colorectal and ovarian cancers are at the same risk and might count into consideration. The finding provides a narrative broad approach for understanding the genetic basis of these fatal diseases by the pathway analysis with gene regulatory network evaluation.

genetics↗

bioRxiv2020

Mapping the genetic and environmental aetiology of autistic traits in Sweden and the UK

BackgroundAutistic traits are influenced by both genetic and environmental factors, and are known to vary geographically in prevalence. But to what extent does their aetiology also vary from place to place? MethodsWe applied a novel spatial approach to data on autistic traits from two large twin studies, the Child and Adolescent Twin Study in Sweden (CATSS; N=16,677, including 8,307 twin pairs) and the Twins Early Development Study in the UK (TEDS; N=11,594, including 5,796 twin pairs), to explore how the influence of nature and nurture on autistic traits varies from place to place. ResultsWe present maps of gene- and environment-by geography interactions in Sweden and the United Kingdom (UK), showing geographical variation in both genetic and environmental influences across the two countries. In Sweden genetic influences appear higher in the far south and in a band running across the centre of the country. Environmental influences appear greatest in the south and north, with reduced environmental influence across the central band. In the UK genetic influences appear greater in the south, particularly in more central southern areas and the southeast, the Midlands and the north of England. Environmental influences appear greatest in the south and east of the UK, with less influence in the north and the west. ConclusionsWe hope this systematic approach to identifying aetiological interactions will inspire research to examine a wider range of previously unknown environmental influences on the aetiology of autistic traits. By doing so, we will gain greater understanding of how these environments draw out or mask genetic predisposition and interact with other environmental influences in the development of autistic traits.

genetics↗

bioRxiv2020

Exploring the causal effects of genetic liability to ADHD and Autism on Alzheimer's disease

BackgroundThere are very few studies investigating possible links between Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD) and Alzheimers disease and these have been limited by small sample sizes, diagnostic and recall bias. However, neurocognitive deficits affecting educational attainment in individuals with ADHD could be risk factors for Alzheimers later in life while hyper plasticity of the brain in ASD and strong positive genetic correlations of ASD with IQ and educational attainment could be protective against Alzheimers. MethodsWe estimated the bidirectional total causal effects of genetic liability to ADHD and ASD on Alzheimers disease through two-sample Mendelian randomization. We investigated their direct effects, independent of educational attainment and IQ, through Multivariable Mendelian randomization. ResultsThere was limited evidence to suggest that genetic liability to ADHD (OR=1.00, 95% CI: 0.98 to 1.02, p=0.39) or ASD (OR=0.99, 95% CI: 0.97 to 1.01, p=0.70) was associated with risk of Alzheimers disease. Similar causal effect estimates were identified when the direct effects, independent of educational attainment (ADHD: OR=1.00, 95% CI: 0.99 to 1.01, p=0.07; ASD: OR=0.99, 95% CI: 0.98 to 1.00, p=0.28) and IQ (ADHD: OR=1.00, 95% CI: 0.99 to 1.02. p=0.29; ASD: OR=0.99, 95% CI: 0.98 to 1.01, p=0.99), were assessed. Finally, genetic liability to Alzheimers disease was not found to have a causal effect on risk of ADHD or ASD (ADHD: OR=1.12, 95% CI: 0.86 to 1.44, p=0.37; ASD: OR=1.19, 95% CI: 0.94 to 1.51, p=0.14). ConclusionsIn the first study to date investigating the causal associations between genetic liability to ADHD, ASD and Alzheimers, within an MR framework, we found limited evidence to suggest a causal effect. It is important to encourage future research using ADHD and ASD specific subtype data, as well as longitudinal data in order to further elucidate any associations between these conditions.

genetics↗

bioRxiv2020

Unique genetic signatures of local adaptation over space and time for diapause, an ecologically relevant complex trait, in Drosophila melanogaster

Organisms living in seasonally variable environments utilize cues such as light and temperature to induce plastic responses, enabling them to exploit favorable seasons and avoid unfavorable ones. Local adapation can result in variation in seasonal responses, but the genetic basis and evolutionary history of this variation remains elusive. Many insects, including Drosophila melanogaster, are able to undergo an arrest of reproductive development (diapause) in response to unfavorable conditions. In D. melanogaster, the ability to diapause is more common in high latitude populations, where flies endure harsher winters, and in the spring, reflecting differential survivorship of overwintering populations. Using a novel hybrid swarm-based genome wide association study, we examined the genetic basis and evolutionary history of ovarian diapause. We exposed outbred females to different temperatures and day lengths, characterized ovarian development for over 2800 flies, and reconstructed their complete, phased genomes. We found that diapause, scored at two different developmental cutoffs, has modest heritability, and we identified hundreds of SNPs associated with each of the two phenotypes. Alleles associated with one of the diapause phenotypes tend to be more common at higher latitudes, but these alleles do not show predictable seasonal variation. The collective signal of many small-effect, clinally varying SNPs can plausibly explain latitudinal variation in diapause seen in North America. Alleles associated with diapause are segregating at relatively high frequencies in Zambia, suggesting that variation in diapause relies on ancestral polymorphisms, and both pro- and anti-diapause alleles have experienced selection in North America. Finally, we utilized outdoor mesocosms to track diapause under natural conditions. We found that hybrid swarms reared outdoors evolved increased propensity for diapause in late fall, whereas indoor control populations experienced no such change. Our results indicate that diapause is a complex, quantitative trait with different evolutionary patterns across time and space. Author SummaryAnimals exhibit diverse strategies to cope with unfavorable conditions in temperate, seasonally varying environments. The model fly, Drosophila melanogaster, can enter a physiological state known as diapause under winter-like conditions. Diapause is characterized by an absence of egg maturation in females and is thought to conserve energy for survival during stressful times. The ability to diapause is more common in flies from higher latitudes and in offspring from flies that have recently overwintered. Therefore, diapause has been thought to be a recent adaptation to temperate climates. We identified hundreds of genetic variants that affect diapause and found that some vary predictably across latitudes in North America. We found little signal of repeated seasonality in diapause-associated genetic variants, but our populations evolved an increased ability to diapause in the winter when they were exposed to natural conditions. Combined, our results suggest that diapause-associated variants evolve differently across space and time. We find little evidence that diapause evolved recently in temperate environments; rather, SNPs associated with diapause tend to be quite common in Zambia, suggesting that diapause may promote survival under stresses other than cold. Our results provide future targets for research into the genetic underpinnings of this complex, ecologically relevant trait.

genetics↗

bioRxiv2020

Quantitative genetic architecture of adaptive phenology traits in the deciduous tree, Populus trichocarpa (Torr. & Gray).

In a warming climate, the ability to accurately predict and track shifting environmental conditions will be fundamental for plant survival. Environmental cues define the transitions between growth and dormancy as plants synchronise development with favourable environmental conditions, however these cues are predicted to change under future climate projections which may have profound impacts on tree survival and growth. Here, we use a quantitative genetic approach to estimate the genetic basis of spring and autumn phenology in Populus trichocarpa to determine this species capacity for climate adaptation. We measured bud burst, leaf coloration, and leaf senescence traits across two years (2017-2018) and combine these observations with measures of lifetime growth to determine how genetic correlations between phenology and growth may facilitate or constrain adaptation. Timing of transitions differed between years, although we found strong cross year genetic correlations in all traits, suggesting that genotypes respond in consistent ways to seasonal cues. Spring and autumn phenology were correlated with lifetime growth, where genotypes that burst leaves early and shed them late had the highest lifetime growth. We also identified substantial heritable variation in the timing of all phenological transitions (h2 = 0.5-0.8) and in lifetime growth (h2 = 0.8). The combination of abundant additive variation and favourable genetic correlations in phenology traits suggests that cultivated varieties of P. trichocarpa have the capability to create populations which may adapt their phenology to climatic changes without negative impacts on growth.

genetics↗

bioRxiv2020

Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms

BackgroundGenome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. ResultsWe conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. ConclusionsMultitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

genetics↗

bioRxiv2020

Mapping genetic effects on cellular phenotypes with "cell villages"

Tens of thousands of genetic variants shape human phenotypes, mostly by unknown cellular mechanisms. Here we describe Census-seq, a way to measure cellular phenotypes in cells from many people simultaneously. Analogous to pooled CRISPR screens but for natural variation, Census-seq associates cellular phenotypes to donors genotypes by quantifying the presence of each donors DNA in cell "villages" before and after sorting or selection for cellular traits of interest. Census-seq enables population-scale cell-biological phenotyping with low cost and high internal control. We demonstrate Census-seq through investigation of genetic effects on the SMN protein whose deficiency underlies spinal muscular atrophy (SMA). Census-seq quantified and mapped effects of many common alleles on SMN protein levels and response to SMN-targeted therapeutics, including a common, cryptic non-responder allele. We provide tools enabling population-scale cell experiments and explain how Census-seq can be used to map genetic effects on diverse cell phenotypes. O_FIG O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/174383v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@169bf4borg.highwire.dtl.DTLVardef@18dd9c0org.highwire.dtl.DTLVardef@1d30ca5org.highwire.dtl.DTLVardef@e35bf0_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICensus-seq reveals how inherited genetic variation affects cell phenotypes C_LIO_LIGenetic analysis of cellular traits in cell villages of >100 donors C_LIO_LICharacterizing human alleles that shape SMN protein expression and drug responses C_LIO_LIDevelopment of protocols and software to enable cellular population genetics C_LI

genetics↗

bioRxiv2020

SambaR: an R package for fast, easy and reproducible population-genetic analyses of biallelic SNP datasets

BackgroundSNP datasets can be used to infer a wealth of information about natural populations, including information about their structure, genetic diversity, and the presence of loci under selection. However, SNP data analysis can be a time-consuming and challenging process, not in the least because at present many different software packages are needed to execute and depict the wide variety of mainstream population-genetic analyses. Here we present SambaR, an integrative and user-friendly R package which automates and simplifies quality control and population-genetic analyses of biallelic SNP datasets. SambaR allows users to perform mainstream population-genetic analyses and to generate a wide variety of ready to publish graphs with a minimum number of commands (less than ten). These wrapper commands call functions of existing packages (including adegenet, ape, LEA, poppr, pcadapt and StAMPP) as well as new tools uniquely implemented in SambaR. ResultsWe tested SambaR on online available SNP datasets and found that SambaR can process datasets of millions of SNPs and hundreds of individuals within hours, given sufficient computing power. Newly developed tools implemented in SambaR facilitate optimization of filter settings, objective interpretation of ordination analyses, enhance comparability of diversity estimates from reduced representation library SNP datasets, and generate reduced SNP panels and structure-like plots with Bayesian population assignment probabilities. ConclusionSambaR facilitates rapid population genetic analyses on biallelic SNP datasets by removing three major time sinks: file handling, software learning, and data plotting. In addition, SambaR provides a convenient platform for SNP data storage and management, as well as several new utilities, including guidance in setting appropriate data filters. Availability and implementationThe SambaR source script, manual and example datasets are distributed through GitHub: https://github.com/mennodejong1986/SambaR

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bioRxiv2020

Statistical model integrating interactions into genotype-phenotype association mapping: an application to reveal 3D-genetic basis underlying Autism

Biological interactions are prevalent in the functioning organisms. Correspondingly, statistical geneticists developed various models to identify genetic interactions through genotype-phenotype association mapping. The current standard protocols in practice test single variants or single regions (that contain multiple local variants) sequentially along the genome, followed by functional annotations that involve various aspects including interactions. The testing of genetic interactions upfront is rare in practice due to the burden of testing a huge number of combinations, which lead to the multiple-test problem and the risk of overfitting. In this work, we developed interaction-integrated linear mixed model (ILMM), a novel model that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems associated with combination searching. Three dimensional (3D) genomic interactions assessed by Hi-C experiments have led to unprecedented biological discoveries. However, the contribution of 3D genomic interactions to the genetic basis of complex diseases has yet to be quantified. Using 3D interacting regions as a priori information, we conducted both simulations and real data analysis to test ILMM. By applying ILMM to whole genome sequencing data for Autism Spectrum Disorders, or ASD (MSSNG) and transcriptome sequencing data (GTEx), we revealed the 3D-genetic basis of ASD and 3D-eQTLs for a substantial proportion of gene expression in brain tissues. Moreover, we have revealed a potential mechanism involving distal regulation between FOXP2 and DNMT3A conferring the risk of ASD. Software is freely available in our GitHub: https://github.com/theLongLab/Jawamix5

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bioRxiv2020

Genetic analysis of obstructive sleep apnoea discovers a strong association with cardiometabolic health

There is currently only limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). The aim of our study is to identify genetic loci associated with OSA risk and to test if OSA and its comorbidities share a common genetic background. We conducted the first large-scale genome-wide association study of OSA using FinnGen Study (217,955 individuals) with 16,761 OSA patients identified using nationwide health registries. We estimated 8.3% [0.06-0.11] heritability and identified five loci associated with OSA (P < 5.0 x 10-8): rs4837016 near GTPase activating protein and VPS9 domains 1 (GAPVD1), rs10928560 near C-X-C motif chemokine receptor 4 (CXCR4), rs185932673 near Calcium/calmodulin-dependent protein kinase ID (CAMK1D) and rs9937053 near Fat mass and obesity-associated protein (FTO) - a variant previously associated with body mass index (BMI). In a BMI-adjusted analysis, an association was observed for rs10507084 near Rhabdomyosarcoma 2 associated transcript (RMST)/NEDD1 gamma-tubulin ring complex targeting factor (NEDD1). We found genetic correlations between OSA and BMI (rg=0.72 [0.62-0.83]) and with comorbidities including hypertension, type 2 diabetes (T2D), coronary heart disease (CHD), stroke, depression, hypothyroidism, asthma and inflammatory rheumatic diseases (IRD) (rg > 0.30). Polygenic risk score (PRS) for BMI showed 1.98-fold increased OSA risk between the highest and the lowest quintile and Mendelian randomization supported a causal relationship between BMI and OSA. Our findings support the causal link between obesity and OSA and joint genetic basis between OSA and comorbidities.

genetics↗

bioRxiv2020

Genetic diversity of maize landraces from the South-West of France

From the 17th century until the arrival of hybrids in 1960s, maize landraces were cultivated in the South-West of France, a traditional region for maize cultivation. A set of landraces were collected in this region between the 1950s and 1980s and were then conserved ex situ in a germplam collection. Previous studies using molecular markers on approx. twenty landraces fo this region showed that they belonged to a Pyrenees-Galicia Flint genetic group and originated from hybridization between Caribbean and Northern Flint germplasms introduced in Europe. In this study, we assessed the structure and genetic diversity of 194 SWF maize landraces to elucidate their origin, using a 50K SNP array and a bulk DNA approach. We identified two weakly differentiated genetic groups, one in the Western part and the other in the Eastern part. We highlighted the existence of a longitudinal gradient along the SWF area that was probably maintained through the interplay between genetic drifts and restricted gene flows, rather than through differential climatic adaptation. The contact zone between the two groups observed near the Garonne valley may be the result of these evolutionnary forces. We found only few significant cases of hybridization between Caribbean and Northern Flint germplasms in the region. We also found gene flows from various maize genetic groups to SWF landraces. Thus, we assumed that SWF landraces had a multiple origin with a slightly higher influence of Tropical germplasm in the West and preponderance of Northern Flint germplasm in the East.

genetics↗

bioRxiv2020

Twin studies with unmet assumptions are biased towards genetic heritability

For a century [1,2], studies of monozygotic and dizygotic twins have yielded estimates of trait heritability. The clever logic behind them is that while both types of twins share environments, their genetic overlap is different. Hence, larger trait correlations between monozygotic compared to dizygotic twins indicate heritability (nature), whereas similar correlations indicate shared environmental influences (nurture), and low correlations indicate shaping through non-shared environments (external influences and measurement error). While many have written on the assumptions that both types of twins share equal environments [3-5], and that parental genetics and environment are independent [6,7]; fewer have put their data where their mouth is. Here, the impacts of unmet assumptions were investigated using a generative mixture model of twin phenotypes. The results indicated that violations of the equal environments assumption yielded large overestimations of heritability and underestimations of shared environmental influences. On the other hand, when parental genetics shaped twins shared environments, only minor non-linear biases against heritability emerged. Finally, realistic levels of measurement error uniformly depressed estimates for genetic and shared environmental factors. In sum, twin studies are particularly susceptible to overestimation of genetic and non-shared environmental influences. This bias could explain why some traits, such as attitudes towards property taxes [8], show suspiciously high heritability without a biologically plausible mechanism. Particularly in the context of traits with convincing mechanisms of cultural transmission [9-11] and complex gene-environment interactions [6], researchers should not allow biases in twin studies to overestimate heritability.

genetics↗

bioRxiv2020

Heritability of ethanol consumption and pharmacokinetics in a genetically diverse panel of Collaborative Cross mouse strains and their inbred founder lines

BackgroundInter-individual variation in voluntary ethanol consumption and ethanol response is partially influenced by genetic variation. Discovery of the genes and allelic variants that affect these phenotypes may clarify the etiology and pathophysiology of problematic alcohol use, including alcohol use disorder. Genetically diverse mouse populations also demonstrate heritable variation in ethanol consumption and can be utilized to discover the genes and gene networks that influence this trait. The Collaborative Cross (CC) recombinant inbred strains, Diversity Outbred (DO) population and their eight founder strains are complementary mouse resources that capture substantial genetic diversity and can demonstrate expansive phenotypic variation in heritable traits. These populations may be utilized to discover candidate genes and gene networks that moderate ethanol consumption and other ethanol-related traits. MethodsWe characterized ethanol consumption, preference and pharmacokinetics in the eight founder strains and ten CC strains in 12-hour drinking sessions during the dark phase of the circadian cycle. ResultsEthanol consumption was found to be substantially heritable, both early in ethanol access and over a chronic intermittent access schedule. Ethanol pharmacokinetics were also found to be heritable; however, no association between strain-level ethanol consumption and pharmacokinetics was detected. The PWK/PhJ strain was found to be the highest drinking strain, with consumption substantially exceeding C57BL/6J, a strain commonly used as a model of "high" or "binge" drinking. Notably, we found strong evidence that sex moderated genetic effects on voluntary ethanol drinking. ConclusionsCollectively, this research may serve as a foundation for expanded genetic study of ethanol consumption in the CC/DO and related populations; moreover, we have identified reference strains with extreme consumption phenotypes that effectively represent polygenic models of hazardous ethanol use.

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