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Effect of population structure and migration when investigating genetic continuity using ancient DNA

Recent advances in sequencing techniques provide means to access direct genetic snapshots from the past with ancient DNA data (aDNA) from diverse periods of human prehistory. Comparing samples taken in the same region but at different time periods may indicate if there is continuity in the peopling history of that area or if a large genetic input, such as an immigration wave, has occurred. Here we propose a new modeling approach for investigating population continuity using aDNA, including two fundamental elements in human evolution that were absent from previous methods: population structure and migration. The method also considers the extensive temporal and geographic heterogeneity commonly found in aDNA datasets. We compare our spatially-explicit approach to the previous non-spatial method and show that it is more conservative and thus suitable for testing population continuity, especially when small, isolated populations, such as prehistoric ones, are considered. Moreover, our approach also allows investigating partial population continuity and we apply it to a real dataset of ancient mitochondrial DNA. We estimate that 91% of the current genetic pool in central Europe entered the area with immigrant Neolithic farmers, but a genetic contribution of local hunter-gatherers as large as 83% cannot be entirely ruled out.

Genetics

Genetic overlap between schizophrenia and developmental psychopathology: a longitudinal approach applied to common childhood disorders between age 7 and 15 years

Various non-psychotic psychiatric disorders in childhood and adolescence can precede the onset of schizophrenia, but the nature of this relationship remains unclear. We investigated to what extent the association between schizophrenia and psychiatric disorders in childhood is explained by shared genetic risk factors.\n\nPolygenic risk scores (PRS), reflecting an individuals genetic risk for schizophrenia, were constructed for participants in two birth cohorts (2,588 children from the Netherlands Twin Register (NTR) and 6,127 from the Avon Longitudinal Study of Parents And Children (ALSPAC)). The associations between schizophrenia PRS and measures of anxiety, depression, attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder/conduct disorder (ODD/CD) were estimated at age 7, 10, 12/13 and 15 years in the two cohorts. Results were then meta-analyzed, and age-effects and differences in the associations between disorders and PRS were formally tested in a meta-regression.\n\nThe schizophrenia PRS was associated with childhood and adolescent psychopathology Where the association was weaker for ODD/CD at age 7. The associations increased with age this increase was steepest for ADHD and ODD/CD. The results are consistent with a common genetic etiology of schizophrenia and developmental psychopathology as well as with a stronger shared genetic etiology between schizophrenia and adolescent onset psychopathology.\n\nA multivariate meta-analysis of multiple and repeated observations enabled to optimally use the longitudinal data across diagnoses in order to provide knowledge on how childhood disorders develop into severe adult psychiatric disorders.

Genetics

Host genetic variation in mucosal immunity pathways influences the upper airway microbiome

The degree to which host genetic variation can modulate microbial communities in humans remains an open question. Here we performed a genetic mapping study of the microbiome in two accessible upper airway sites, the nasopharynx and the nasal vestibule, during two seasons in 144 adult members of a founder population of European decent. We estimated the relative abundances (RAs) of genus level bacteria from 16S rRNA gene sequences and examined associations with 148,653 genetic variants (linkage disequilibrium [LD] r2 < 0.5) selected from among all common variants discovered in genome sequences in this population. We identified 37 microbiome quantitative trait loci (mbQTLs) that showed evidence of association with the RAs of 22 genera (q < 0.05), and were enriched for genes in mucosal immunity pathways. The most significant association was between the RA of Dermacoccus (phylum Actinobacteria) and a variant 8kb upstream of TINCR (rs117042385; p = 1.61[x]10-8; q = 0.002), a long non-coding RNA that binds to peptidoglycan recognition protein 3 (PGLYRP3) mRNA, a gene encoding a known antimicrobial protein. A second association was between a missense variant in PGLYRP4 (rs3006458) and the RA of an unclassified genus of family Micrococcaceae (phylum Actinobacteria) (p = 5.10[x]10-7; q = 0.032). Our findings provide evidence of host genetic influences on upper airway microbial composition in humans, and implicate mucosal immunity genes in this relationship.

Genetics

Genetic Regulation of Phenotypic Plasticity and Canalisation in Yeast Growth

The ability of a genotype to show diverse phenotypes in different environments is called phenotypic plasticity. Phenotypic plasticity helps populations to evade extinctions in novel environments, facilitates adaptation and fuels evolution. However, most studies focus on understanding the genetic basis of phenotypic regulation in specific environments. As a result, while its evolutionary relevance is well established, genetic mechanisms regulating phenotypic plasticity and their overlap with the environment specific regulators is not well understood. Saccharomyces cerevisiae is highly sensitive to the environment, which acts as not just external stimulus but also as signalling cue for this unicellular, sessile organism. We used a previously published dataset of a biparental yeast population grown in 34 diverse environments and mapped genetic loci regulating variation in phenotypic plasticity, plasticity QTL, and compared them with environment-specific QTL. Plasticity QTL is one whose one allele exhibits high plasticity whereas the other shows a relatively canalised behaviour. We mapped phenotypic plasticity using two parameters - environmental variance, an environmental order-independent parameter and reaction norm (slope), an environmental order-dependent parameter. Our results show a partial overlap between pleiotropic QTL and plasticity QTL such that while some plasticity QTL are also pleiotropic, others have a significant effect on phenotypic plasticity without being significant in any environment independently. Furthermore, while some plasticity QTL are revealed only in specific environmental orders, we identify large effect plasticity QTL, which are order-independent such that whatever the order of the environments, one allele is always plastic and the other is canalised. Finally, we show that the environments can be divided into two categories based on the phenotypic diversity of the population within them and the two categories have differential regulators of phenotypic plasticity. Our results highlight the importance of identifying genetic regulators of phenotypic plasticity to comprehensively understand the genotype-phenotype map.

Genetics

Genetic Prediction of Male Pattern Baldness

Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40-69 years. We identified over 250 independent novel genetic loci associated with severe hair loss. By developing a prediction algorithm based entirely on common genetic variants, and applying it to an independent sample, we could discriminate accurately (AUC = 0.82) between those with no hair loss from those with severe hair loss. The results of this study might help identify those at the greatest risk of hair loss and also potential genetic targets for intervention.

Genetics

Dissecting the genetics of complex traits using summary association statistics

During the past decade, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced vast repositories of genetic variation and trait measurements across millions of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyze summary association statistics. Here we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Genetics

Integrating Molecular QTL Data into Genome-wide Genetic Association Analysis: Probabilistic Assessment of Enrichment and Colocalization

We propose a novel statistical framework for integrating genetic data from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs for complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and the analysis of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals, which is analogous to the power calculation in genetic association studies. Using this utility, we further illustrate the importance of enrichment analysis on the ability of discovering colocalized signals and the potential limitations of currently available molecular QTL data.

Genetics

Protected corridors preserve tiger genetic diversity and minimize extinction into the next century

Habitat loss is the greatest threat to large carnivores around the world. Maintenance of functional connectivity in fragmented landscapes will be important for long-term species persistence. Here, we merge landscape genetics analyses and spatially-explicit simulations to understand future persistence and extinction of tigers (Panthera tigris) in Central India. Tigers in this landscape are restricted to Protected Areas (PAs) and forest fragments embedded within a mosaic of agricultural fields and human settlements. We examined current population connectivity of tigers across nine reserves (using 116 non-invasively sampled individuals and 12 microsatellites). Genetic data was used to infer resistance-to-movement. Our results suggest that dense human settlements and roads with high traffic are detrimental to tiger movement. We used landscape genetic simulations to model 86 different scenarios that incorporated impacts of future land-use change on inferred population connectivity and extinction. Our results confirm that genetic variability (heterozygosity) will decrease in the future and small and/or isolated PAs will have a high risk of local extinction. The average extinction risk of small PAs reduced by 23-70% on adding a 5 km buffer around exiting boundaries. Unplanned development results in 35% lower heterozygosity and 56% higher average extinction probability for tigers even within protected areas. Increasing tiger numbers in such a scenario decreases extinction probability just by 12 % (from 56% to 44%). Scenarios where habitat connectivity was enhanced and maintained, stepping-stone populations were introduced/maintained, and tiger numbers were increased, led to low overall extinction probability (between 3-21%). Our simulations provide a means to quantitatively evaluate the effects of different land-use change scenarios on connectivity and extinction, linking basic science to land-use change policy and planned infrastructure development.

genetics

Problems with Estimating Anthesis Phenology Parameters in Zea mays: Consequences for Combining Ecophysiological Models with Genetics

Ecophysiological crop models encode intra-species behaviors using constant parameters that are presumed to summarize genotypic properties. Accurate estimation of these parameters is crucial because much recent work has sought to link them to genotypes. The original goal of this study was to fit the anthesis date component of the CERES-Maize model to 5266 genetic lines grown at 11 site-years and genetically map the resulting parameter estimates. Although the resulting estimates had high predictive quality, numerous artifacts emerged during estimation. The first arose in situations where the model was unable to express the observed data for many lines, which ended up sharing the same parameter value. In the second (2254 lines), the model reproduced the data but there were often many parameter sets that did so equally well (equifinality). These artifacts made genetic mapping impossible, thus, revealing cautionary insights regarding a major current paradigm for linking process based models to genetics.

genetics

Natural Genetic Variation Modifies Gene Expression Dynamics at the Protein Level During Pheromone Response in Saccharomyces cerevisiae

Heritable variation in gene expression patterns plays a fundamental role in trait variation and evolution, making understanding the mechanisms by which genetic variation acts on gene expression patterns a major goal for biology. Both theoretical and empirical work have largely focused on variation in steady-state mRNA levels and mRNA synthesis rates, particularly of protein-coding genes. Yet in order for this variation to affect higher order traits it must lead to differences at the protein level. Variation in protein-specific processes including protein synthesis rates and protein decay rates could amplify, mask, or even reverse effects transmitted from the transcript level, but the extent to which this happens is unclear. Moreover, mechanisms that underlie protein expression variation under dynamic conditions have not been examined. To address this challenge, we analyzed how mRNA and protein expression dynamics covary between two strains of Saccharomyces cerevisiae during mating pheromone response. Although divergent steady-state mRNA expression levels explained divergent steady-state protein levels for four out of five genes in our study, the same was true for only one out of five genes for expression dynamics. By integrating decay rate and allele-specific protein expression analyses, we resolved that expression divergence for Fig1p was caused by genetic variation acting in trans on protein synthesis rate, expression divergence for Ina1p was caused by cis-by-trans epistatic effects on transcript level and protein synthesis rate, and expression divergence for Fus3p and Tos6p were caused by divergence in protein synthesis rates. Our study demonstrates that steady-state analysis of gene expression is insufficient to understand the impact of genetic variation on gene expression variation. An integrated and dynamic approach to gene expression analysis - comparing mRNA levels, protein levels, protein decay rates, and allele-specific protein expression - allows for a detailed analysis of the genetic mechanisms underlying protein expression divergences.

genetics

A complex multi-locus, multi-allelic genetic architecture underlying the long-term selection-response in the Virginia body weight line of chickens

The ability of a population to adapt to changes in their living conditions, whether in nature or captivity, often depends on polymorphisms in multiple genes across the genome. In-depth studies of such polygenic adaptations are difficult in natural populations, but can be approached using the resources provided by artificial selection experiments. Here, we dissect the genetic mechanisms involved in long-term selection responses of the Virginia chicken lines, populations that after 40 generations of divergent selection for 56-day body weight display a nine-fold difference in the selected trait. In the F15 generation of an intercross between the divergent lines, 20 loci explained more than 60% of the additive genetic variance for the selected trait. We focused particularly on seven major QTL and found that only two fine-mapped to single, bi-allelic loci; the other five contained linked loci, multiple alleles or were epistatic. This detailed dissection of the polygenic adaptations in the Virginia lines provides a deeper understanding of genome-wide mechanisms involved in the long-term selection responses. The results illustrate that long-term selection responses, even from populations with a limited genetic diversity, can be polygenic and influenced by a range of genetic mechanisms.

genetics

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis acting genotype-environment interactions (GxE) - genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate interaction quantitative trait loci (iQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

genetics

Shared Genetic Architecture Of Asthma With Allergic Diseases: A Genome-wide Cross Trait Analysis Of 112,000 Individuals From UK Biobank

Clinical and epidemiological data suggest that asthma and allergic diseases are associated. And may share a common genetic etiology. We analyzed genome-wide single-nucleotide polymorphism (SNP) data for asthma and allergic diseases in 35,783 cases and 76,768 controls of European ancestry from the UK Biobank. Two publicly available independent genome wide association studies (GWAS) were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84x10-62). Cross trait analysis identified 38 genome-wide significant loci, including novel loci such as D2HGDH and GAL2ST2. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance our understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.

genetics

Genetic mapping of species differences via "in vitro crosses" in mouse embryonic stem cells

Discovering the genetic changes underlying species differences is a central goal in evolutionary genetics. However, hybrid crosses between species in mammals often suffer from hybrid sterility, greatly complicating genetic dissection of trait variation. Here we describe a simple, robust and transgene-free technique to make \"in vitro crosses\" in hybrid mouse embryonic stem cells by inducing random mitotic crossovers with the drug ML216, which inhibits Bloom syndrome (BLM). Starting with an interspecific hybrid (between Mus musculus and Mus spretus) embryonic stem cell line spanning 1.5 million years of divergence, we demonstrate the feasibility of mapping enzymatic differences across species within weeks and the possibility of re-deriving whole mice. Our work shows how in vitro crosses can overcome major bottlenecks like hybrid sterility in traditional mouse breeding to address fundamental questions in evolutionary biology.\n\nImpact StatementBy mixing hybrid mouse genomes in stem cells via mitotic recombination, genetic mapping and hybrid mosaic mice can be achieved in weeks, even across species barriers.

genetics

Non-Parametric Genetic Prediction of Complex Traits with Latent Dirichlet Process Regression Models

Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model (DPR). DPR is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare DPR with several commonly used prediction methods with simulations. We further apply DPR to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.

genetics

Genetic and Environmental Influences on Pubertal Hormones in Human Hair across Development

Background: Puberty is a complex biopsychosocial process that is linked to an array of psychiatric and medical disorders that emerge in adolescence and persist across the lifespan. Although the pubertal process is driven by neuroendocrine changes, few quantitative genetic studies have directly measured puberty-relevant hormones. Hair samples can now be assayed for accumulation of hormones over several months. In contrast to more conventional salivary measures, hair measures are not confounded by diurnal variation or hormonal reactivity.\n\nMethods: In an ethnically and socioeconomically diverse sample of 1,286 child and adolescent twins and multiples from 672 unique families, we estimated genetic and environmental influences on concentrations of testosterone, DHEA, and progesterone in hair across the period of 8 to 18 years of age.\n\nResults: We identified sex-specific developmental windows of maximal heritability in each hormone. Peak heritability for DHEA occurred at age 9.8 years for males and 10.0 years for females. Peak heritability for testosterone occurred at age 12.5 and 15.2 years for males and females, respectively. Peak heritability for male progesterone occurred at 11.2 years, while the heritability of female progesterone remained uniformly low.\n\nConclusion: This is the first study of genetic influences on pubertal hormone concentrations in human hair and is the largest of the heritability of pubertal hormones in any form. The identification of specific developmental windows when genetic signals for hormones are maximized has critical implications for well-informed models of hormone-behavior associations in childhood and adolescence.

genetics

A set of Columbia-0-specific single nucleotide polymorphism markers for the genetic analysis of natural variation in Arabidopsis thaliana

Genetic markers are important tools for the study of natural and induced genetic variation. Due to the strong increase in the number of genome sequences, an overview of the genomic diversity of many natural strains from individual species could be gained. This allows for the design of markers for flexible use and cost-efficient small scale genetic studies requiring minimal laboratory and bioinformatics effort. Here, we describe 140 single nucleotide polymorphism (SNP) markers with genome-wide distribution that discriminate between the genotype of the common Arabidopsis thaliana reference accession Columbia-0 (Col-0) and the majority of Arabidopsis thaliana accessions that have been sequenced to date. We designed, generated and validated the markers using the kompetitive allele-specific PCR (KASP) technology and made all 140SNPvCol marker assays publicly available through a service provider. Through the integration of available genomic SNP allele information of 1,135 accessions, we found that 120 of these 140 markers could detect non-reference alleles in 647 accessions and more than 100 markers showed non-reference alleles in 1,094 accessions. We further show that the marker set can be used for the verification or identification of accessions of unknown identity. As the KASP methodology is very flexible, an optimal marker subset can be easily selected among the available 140SNPvCol markers presented here to analyze genetic combinations of Col-0 with any other accession.

genetics

The Genetic Epidemiology of Developmental Dysplasia of the Hip: A Genome-Wide Association Study Harnessing National Clinical Audit Data

BackgroundDevelopmental dysplasia of the hip (DDH) is a common, heritable condition characterised by abnormal formation of the hip joint, but has a poorly understood genetic architecture due to small sample sizes. We apply a novel case-ascertainment approach using national clinical audit (NCA) data to conduct the largest DDH genome-wide association study (GWAS) to date, and replicate our findings in independent cohorts.\n\nMethodsWe used the English National Joint Registry (NJR) dataset to collect DNA and conducted a GWAS in 770 DDH cases and 3364 controls. We tested the variant most strongly associated with DDH in independent replication cohorts comprising 1129 patients and 4652 controls.\n\nResultsThe heritable component of DDH attributable to common variants was 55% and distributed similarly across autosomal and the X-chromosomes. Variation within the GDF5 gene promoter was strongly and reproducibly associated with DDH (rs143384, OR 1.44 [95% CI 1.34-1.56], p=3.55x10-22). Two further replicating loci showed suggestive association with DDH near NFIB (rs4740554, OR 1.30 [95% CI 1.16-1.45], p=4.44x10-6) and LOXL4 (rs4919218, 1.19 [1.10-1.28] p=4.38x10-6). Through gene-based enrichment we identify GDF5, UQCC1, MMP24, RETSAT and PDRG1 association with DDH (p<1.2x10-7). Using the UK Biobank and arcOGEN cohorts to generate polygenic risk scores we find that risk alleles for hip osteoarthritis explain <0.5% of the variance in DDH susceptibility.\n\nConclusionUsing the NJR as a proof-of-principle, we describe the genetic architecture of DDH and identify several candidate intervention loci and demonstrate a scalable recruitment strategy for genetic studies that is transferrable to other complex diseases.\n\nKey MessagesO_LIWe report the first genome-wide scan for DDH in a European population, and the first to use national clinical audit data for case-ascertainment in complex disease.\nC_LIO_LIThe heritable component of DDH attributable to common variants is 55% and is distributed similarly across autosomal and the X-chromosomes.\nC_LIO_LIVariation within the GDF5 gene promoter is strongly and reproducibly associated with DDH, with fine-mapping indicating rs143384 as the likely casual variant.\nC_LIO_LIEnrichment analyses implicate GDF5, UQCC1, MMP24, RETSAT and PDRG1 as candidate targets for intervention in DDH.\nC_LIO_LIDDH shares little common genetic aetiology with idiopathic osteoarthritis of the hip, despite sharing variation within the GDF5 promoter as a common risk factor.\nC_LI

genetics