Genetic Basis Of Complex Traits Research Articles

Strong temporal dynamics of QTL action on plant growth progression revealed through high-throughput phenotyping in canola.

SummaryA major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high‐throughput phenotyping in conjunction with genome‐wide association studies to elucidate genotype–phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non‐invasive phenotyping. Time‐resolved data for early growth‐related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome‐wide SNP array data provided the framework for genome‐wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker–trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time‐resolved analyses to effectively unravel the complex and stage‐specific contributions of genes affecting growth processes that operate at different developmental phases.

Evidence for the genetic basis and epistatic interactions underlying ocean- and river-maturing ecotypes of Pacific Lamprey (Entosphenus tridentatus) returning to the Klamath River, California.

Surveys of genomic variation have improved our understanding of the relationship between fitness-related phenotypes and their underlying genetic basis. In some cases, single large-effect genes have been found to underlie important traits; however, complex traits are expected to be under polygenic control and elucidation of multiple gene interactions may be required to fully understand the genetic basis of the trait. In this study, we investigated the genetic basis of the ocean- and river-maturing ecotypes in anadromous Pacific lamprey (Entosphenus tridentatus). In Pacific lamprey, the ocean-maturing ecotype is distinguished by advanced maturity of females (e.g., large egg mass) at the onset of freshwater migration relative to immature females of the river-maturing ecotype. We examined a total of 219 adult Pacific lamprey that were collected at-entry to the Klamath River over a 12-month period. Each individual was genotyped at 308 SNPs representing known neutral and adaptive loci and measured at morphological traits, including egg mass as an indicator of ocean- and river-maturing ecotype for females. The two ecotypes did not exhibit genetic structure at 148 neutral loci, indicating that ecotypic diversity exists within a single population. In contrast, we identified the genetic basis of maturation ecotypes in Pacific lamprey as polygenic, involving two unlinked gene regions that have a complex epistatic relationship. Importantly, these gene regions appear to show stronger effects when considered in gene interaction models than if just considered additive, illustrating the importance of considering epistatic effects and gene networks when researching the genetic basis of complex traits in Pacific lamprey and other species.

Genome-Wide Scan Identifies Selection Signatures in Chinese Wagyu Cattle Using a High-Density SNP Array.

Simple SummaryAnalysis of the genomic regions under consideration can provide important insights into the genetic basis of complex traits. In this study, we utilized a high-density SNP (Single nucleotide polymorphism) array for analysis of genome selection signatures in Chinese Wagyu cattle. In total, 239 selected regions responding to 162 candidate genes were identified, which related to economic traits, including fatty acid, meat quality, growth and developmental traits. Moreover, we found that these selected genes were enriched for the four Gene Ontology (GO) terms of biological regulation and metabolic process. These results also contribute to the understanding of genetic basis of these traits during the formation of this population.Selective breeding can lead to genetic diversity and diverse phenotypes in farm animals. Analysis of the genomic regions under selection can provide important insights into the genetic basis of complex traits. In this study, a high-density SNP array was used for analysis of genome selection signatures in Chinese Wagyu cattle. In total, we obtained 478,903 SNPs and 24,820 no-overlap regions for |iHS| (integrated haplotype score) estimations. Under the threshold of the top 1%, 239 regions were finally identified as candidate selected regions and 162 candidate genes were found based on the UMD3.1 genome assembly. These genes were reported to be associated with fatty acids, such as Bos taurus nitric oxide synthase 1 adaptor protein (NOS1AP), Bos taurus hydroxysteroid 17-beta dehydrogenase 7 (HSD17B7), Bos taurus WD repeat domain 7 (WDR7), Bos taurus ELOVL fatty acid elongase 2 (ELOVL2), Bos taurus calpain 1 (CAPN1), Bos taurus parkin RBR E3 ubiquitin protein ligase (PRKN, also known as PARK2), Bos taurus mitogen-activated protein kinase kinase 6 (MAP2K6), meat quality, including Bos taurus ADAM metallopeptidase domain 12 (ADAM12), Bos taurus 5′-aminolevulinate synthase 1 (ALAS1), Bos taurus small integral membrane protein 13 (SMIM13) and Bos taurus potassium two pore domain channel subfamily K member 2 (KCNK2), growth, and developmental traits, such as Bos taurus insulin like growth factor 2 receptor (IGF2R), Bos taurus RAR related orphan receptor A (RORA), Bos taurus fibroblast growth factor 14 (FGF14), Bos taurus paired box 6 (PAX6) and Bos taurus LIM homeobox 6 (LHX6). In addition, we identified several genes that are associated with body size and weight, including Bos taurus sorting nexin 29 (SNX29), Bos taurus zinc finger imprinted 2 (ZIM2), Bos taurus family with sequence similarity 110 member A (FAM110A), immune system, including Bos taurus toll like receptor 9 (TLR9), Bos taurus TAFA chemokine like family member 1 (TAFA1), Bos taurus glutathione peroxidase 8 (putative) (GPX8), Bos taurus interleukin 5 (IL5), Bos taurus PR domain containing 9 (PRDM9), Bos taurus glutamate ionotropic receptor kainate type subunit 2 (GRIK2) and feed intake efficiency, Bos taurus sodium voltage-gated channel alpha subunit 9 (SCN9A), Bos taurus relaxin family peptide/INSL5 receptor 4 (RXFP4), Bos taurus RNA polymerase II associated protein 3 (RPAP3). Moreover, four GO terms of biological regulation (GO:0009987, GO:0008152) and metabolic process (GO:0003824, GO:0005488) were found based on these genes. In addition, we found that 232 candidate regions (~18 Mb) overlapped with the Quantitative trait loci (QTL)regions extracted from cattle QTLdb. Our findings imply that many genes were selected for important traits in Chinese Wagyu cattle. Moreover, these results can contribute to the understanding of the genetic basis of the studied traits during the formation of this population.

ASSOCIATION MAPPING OF MORPHOLOGICAL AND PHYSIOLOGICAL TRAITS OF FLAG LEAF RELATED TO DROUGHT TOLERANCE IN BARLEY

Association mapping has proven to be a powerful approach for dissecting the genetic basis of complex traits. In this study, QTLs controlling flag leaf characteristics under drought stress were detected in a set of 148 modern spring barley cultivars using AM analysis. Flag leaf length (FLL), flag leaf width (FLW), relative water content (RWC), chlorophyll content, and maximum quantum efficiency of PSII (Fv/Fm) which are important in photosynthetic rate, were evaluated under normal irrigation and drought stress conditions at grain filling stage. Population structure was estimated using Structure 2.3 and linkage disequilibrium (LD) was estimated by the ‘Full Matrix LD’ using Tassel 5.0 . Significant marker/trait associations were investigated based on K-Q matrix using Tassel 3.0 . The analysis of population structure divided the cultivars into two sub-groups. Significant LD values (P < 0.01) between polymorphic sites with regions of high and low LD were observed. A total of 84 significant putative genomic regions were identified, which delineated into 37 QTLs under two water treatments. Two stable QTLs on 2H and 3H were detected for FLL in drought stress treatment. A QTL for FLL were detected on 2H in normal treatment, which alone explained around 11% of phenotypic variance of FLL. This QTL was also associated with the expression of FLW and explained around 7.5% of phenotypic variance. The results suggest that major loci are located on chromosomes 2H, 3H, 4H and 5H involved in the development of flag leaf characteristics and could be used as selection criteria in barley breeding for drought tolerance.

A rapid and efficient linear mixed model approach using the score test and its application to GWAS

Recent studies have demonstrated that Linear mixed model (LMM) successfully correct for population structure and genetic relatedness in genome-wide association studies (GWAS). However, currently available LMM methods usually suffer from computational inefficiency. Here, we propose a novel method, consisting of an LMM using the score test (LMM-Score). Our method takes advantage of score test that does not require estimation of parameters under the alternative hypothesis or the full model. We performed extensive simulation studies to estimate the statistical power of LMM-Score with various assumptions of sample size or marker size under different trait heritabilities. Although the limited power of simulation studies due to small sample size and conservativeness of Bonferroni correction, LMM-Score reduces the computing time greatly and maintained statistical power when compared with routine LMM method. Therefore, we believe that the LMM-Score is a valuable method for identifying the genetic basis of complex traits. We also applied our approach to real beef cattle data and suggested SOX17, RP1, LYN RPS20, snoU54, U1, MOS, PLAG1, CHCHD7, and SDR16C5 on BTA14 as the most promising candidate genes affecting pure meat weight (PMW), foreshank weight (FSW) and silverside weight (SSW) in Chinese Simmental beef cattle.

Mitonuclear epistasis, genotype-by-environment interactions, and personalized genomics of complex traits in Drosophila.

Mitochondrial function requires the coordinated expression of dozens of gene products from the mitochondrial genome and hundreds from the nuclear genomes. The systems that emerge from these interactions convert the food we eat and the oxygen we breathe into energy for life, while regulating a wide range of other cellular processes. These facts beg the question of whether the gene‐by‐gene interactions (G x G) that enable mitochondrial function are distinct from the gene‐by‐environment interactions (G x E) that fuel mitochondrial activity. We examine this question using a Drosophila model of mitonuclear interactions in which experimental combinations of mtDNA and nuclear chromosomes generate pairs of mitonuclear genotypes to test for epistatic interactions (G x G). These mitonuclear genotypes are then exposed to altered dietary or oxygen environments to test for G x E interactions. We use development time to assess dietary effects, and genome wide RNAseq analyses to assess hypoxic effects on transcription, which can be partitioned in to mito, nuclear, and environmental (G x G x E) contributions to these complex traits. We find that mitonuclear epistasis is universal, and that dietary and hypoxic treatments alter the epistatic interactions. We further show that the transcriptional response to alternative mitonuclear interactions has significant overlap with the transcriptional response to alternative oxygen environments. Gene coexpression analyses suggest that these shared genes are more central in networks of gene interactions, implying some functional overlap between epistasis and genotype by environment interactions. These results are discussed in the context of evolutionary fitness, the genetic basis of complex traits, and the challenge of achieving precision in personalized medicine. © 2018 The Authors. IUBMB Life published by Wiley Periodicals, Inc. on behalf of International Union of Biochemistry and Molecular Biology, 70(12):1275–1288, 2018

The UK Biobank resource with deep phenotyping and genomic data

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

Time‐specific and pleiotropic quantitative trait loci coordinately modulate stem growth in Populus

SummaryIn perennial woody plants, the coordinated increase of stem height and diameter during juvenile growth improves competitiveness (i.e. access to light); however, the factors underlying variation in stem growth remain unknown in trees. Here, we used linkage‐linkage disequilibrium (linkage‐LD) mapping to decipher the genetic architecture underlying three growth traits during juvenile stem growth. We used two Populus populations: a linkage mapping population comprising a full‐sib family of 1,200 progeny and an association mapping panel comprising 435 unrelated individuals from nearly the entire natural range of Populus tomentosa. We mapped 311 quantitative trait loci (QTL) for three growth traits at 12 timepoints to 42 regions in 17 linkage groups. Of these, 28 regions encompassing 233 QTL were annotated as 27 segmental homology regions (SHRs). Using SNPs identified by whole‐genome re‐sequencing of the 435‐member association mapping panel, we identified significant SNPs (P ≤ 9.4 × 10−7) within 27 SHRs that affect stem growth at nine timepoints with diverse additive and dominance patterns, and these SNPs exhibited complex allelic epistasis over the juvenile growth period. Nineteen genes linked to potential causative alleles that have time‐specific or pleiotropic effects, and mostly overlapped with significant signatures of selection within SHRs between climatic regions represented by the association mapping panel. Five genes with potential time‐specific effects showed species‐specific temporal expression profiles during the juvenile stages of stem growth in five representative Populus species. Our observations revealed the importance of considering temporal genetic basis of complex traits, which will facilitate the molecular design of tree ideotypes.

Inference of genetic architecture from chromosome partitioning analyses is sensitive to genome variation, sample size, heritability and effect size distribution.

Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so-called "chromosome partitioning" where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.

High density marker panels, SNPs prioritizing and accuracy of genomic selection

BackgroundThe availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance.ResultsIn this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers.ConclusionsThese results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications.

Polygenicity and Epistasis Underlie Fitness-Proximal Traits in the Caenorhabditis elegans Multiparental Experimental Evolution (CeMEE) Panel.

Understanding the genetic basis of complex traits remains a major challenge in biology. Polygenicity, phenotypic plasticity, and epistasis contribute to phenotypic variance in ways that are rarely clear. This uncertainty can be problematic for estimating heritability, for predicting individual phenotypes from genomic data, and for parameterizing models of phenotypic evolution. Here, we report an advanced recombinant inbred line (RIL) quantitative trait locus mapping panel for the hermaphroditic nematode Caenorhabditis elegans, the C. elegans multiparental experimental evolution (CeMEE) panel. The CeMEE panel, comprising 507 RILs at present, was created by hybridization of 16 wild isolates, experimental evolution for 140-190 generations, and inbreeding by selfing for 13-16 generations. The panel contains 22% of single-nucleotide polymorphisms known to segregate in natural populations, and complements existing C. elegans mapping resources by providing fine resolution and high nucleotide diversity across > 95% of the genome. We apply it to study the genetic basis of two fitness components, fertility and hermaphrodite body size at time of reproduction, with high broad-sense heritability in the CeMEE. While simulations show that we should detect common alleles with additive effects as small as 5%, at gene-level resolution, the genetic architectures of these traits do not feature such alleles. We instead find that a significant fraction of trait variance, approaching 40% for fertility, can be explained by sign epistasis with main effects below the detection limit. In congruence, phenotype prediction from genomic similarity, while generally poor ([Formula: see text]), requires modeling epistasis for optimal accuracy, with most variance attributed to the rapidly evolving chromosome arms.

Association mapping for seed cotton yield, yield components and fibre quality traits in upland cotton (Gossypium hirsutum L.) genotypes

AbstractAssociation mapping has revolutionized human genetics and is increasingly used in plant genetics. It is an efficient way of determining the genetic basis of complex traits. In cotton so far numerous association mapping studies are available as compared to many other important crops. In our study, mapping was performed using cotton 63K infinium beadchip with 201 germplasm lines. Through fast STRUCTURE analysis, lines were grouped into 12 subgroups and revealed genome sharing among the groups. The critical value (R2) was set to 0.243 as threshold to claim LD between two loci. About 3.13% marker pairs recorded significantly high LD (R2 = 1), and 8.19% of marker pairs were in the range of 0.3 to 0.99 R2. In MLM, 349 significant marker–trait associations were detected as against 642 in GLM because of effectiveness in eliminating false associations in MLM. A total of 68 markers explained &gt;10% phenotypic variation for yield and fibre quality traits. Phenotypic variation explained by markers was smaller, suggesting that they might be associated with minor QTLs.

LinkageMapView-rendering high-resolution linkage and QTL maps.

MotivationLinkage and quantitative trait loci (QTL) maps are critical tools for the study of the genetic basis of complex traits. With the advances in sequencing technology over the past decade, linkage map densities have been increasing dramatically, while the visualization tools have not kept pace. LinkageMapView is a free add-on package written in R that produces high resolution, publication-ready visualizations of linkage and QTL maps. While there is software available to generate linkage map graphics, none are freely available, produce publication quality figures, are open source and can run on all platforms. LinkageMapView can be integrated into map building pipelines as it seamlessly incorporates output from R/qtl and also accepts simple text or comma delimited files. There are numerous options within the package to build highly customizable maps, allow for linkage group comparisons, and annotate QTL regions.Availability and implementation https://cran.r-project.org/web/packages/LinkageMapView/

Statistical Methods for Latent Class Quantitative Trait Loci Mapping.

Identifying the genetic basis of complex traits is an important problem with the potential to impact a broad range of biological endeavors. A number of effective statistical methods are available for quantitative trait loci (QTL) mapping that allow for the efficient identification of multiple, potentially interacting, loci under a variety of experimental conditions. Although proven useful in hundreds of studies, the majority of these methods assumes a single model common to each subject, which may reduce power and accuracy when genetically distinct subclasses exist. To address this, we have developed an approach to enable latent class QTL mapping. The approach combines latent class regression with stepwise variable selection and traditional QTL mapping to estimate the number of subclasses in a population, and to identify the genetic model that best describes each subclass. Simulations demonstrate good performance of the method when latent classes are present as well as when they are not, with accurate estimation of QTL. Application of the method to case studies of obesity and diabetes in mouse gives insight into the genetic basis of related complex traits.

Development of a Medium Density Combined-Species SNP Array for Pacific and European Oysters (Crassostrea gigas and Ostrea edulis).

SNP arrays are enabling tools for high-resolution studies of the genetic basis of complex traits in farmed and wild animals. Oysters are of critical importance in many regions from both an ecological and economic perspective, and oyster aquaculture forms a key component of global food security. The aim of our study was to design a combined-species, medium density SNP array for Pacific oyster (Crassostrea gigas) and European flat oyster (Ostrea edulis), and to test the performance of this array on farmed and wild populations from multiple locations, with a focus on European populations. SNP discovery was carried out by whole-genome sequencing (WGS) of pooled genomic DNA samples from eight C. gigas populations, and restriction site-associated DNA sequencing (RAD-Seq) of 11 geographically diverse O. edulis populations. Nearly 12 million candidate SNPs were discovered and filtered based on several criteria, including preference for SNPs segregating in multiple populations and SNPs with monomorphic flanking regions. An Affymetrix Axiom Custom Array was created and tested on a diverse set of samples (n = 219) showing ∼27 K high quality SNPs for C. gigas and ∼11 K high quality SNPs for O. edulis segregating in these populations. A high proportion of SNPs were segregating in each of the populations, and the array was used to detect population structure and levels of linkage disequilibrium (LD). Further testing of the array on three C. gigas nuclear families (n = 165) revealed that the array can be used to clearly distinguish between both families based on identity-by-state (IBS) clustering parental assignment software. This medium density, combined-species array will be publicly available through Affymetrix, and will be applied for genome-wide association and evolutionary genetic studies, and for genomic selection in oyster breeding programs.

Sorting Out Complex Thoughts and Messy Emotions

Cognition. Intelligence. Emotion. Sexuality. These are not merely complicated traits, invoking awed respect. These are aspects of animal life and human nature that are daunting in their biological complexity and in their existential importance. Curious biologists have been tackling animal behavior for centuries, but some topics and behaviors still remain opaque to biological understanding. While the demystification of human nature might give some unease, more of us are simply skeptical of any attempt to unravel the genetic underpinnings of such things.Image from iStock/Photo2008.View Large Image | View Hi-Res Image | Download PowerPoint SlideFor years now, genome-wide association studies (GWAS) have been mining ever-growing genetic datasets for clues to the genetic bases of complex traits and diseases that include behaviors and disabilities in cognition, intelligence, sexuality, social interaction, and emotion. Along with a few breakthroughs, there have been significant disappointments and legitimate questions about the limits of potential success (Visscher et al., 2012xVisscher, P.M., Brown, M.A., McCarthy, M.I., and Yang, J. Am. J. Hum. Genet. 2012; 90: 7–24Abstract | Full Text | Full Text PDF | PubMed | Scopus (904)See all ReferencesVisscher et al., 2012).Some geneticists don’t seem to have gotten the memo.Discussing the biology of human intelligence is a good way to start a scholarly brawl, and yet this complex trait is strongly heritable. Previous GWAS have found hints and candidates for causative genes, but the results are thought to be statistically underpowered. However, in May an international collaborative group published a large meta-analysis of data combined from these previous GWAS (and with new data), and reported 30 new and very promising candidate genes influencing human intelligence (Sniekers et al., 2017xSniekers, S., Stringer, S., Watanabe, K., Jansen, P.R., Coleman, J.R.I., Krapohl, E., Taskesen, E., Hammerschlag, A.R., Okbay, A., Zabaneh, D. et al. Nat. Genet. 2017; Crossref | Scopus (4)See all ReferencesSniekers et al., 2017). By increasing the cohort (nearly 80,000 people) and using some new tools (such as MAGMA), the work substantially expanded the list of genetic players. One new candidate is FOXO3, a transcription factor involved in insulin/IGF signaling.What of love? Shakespeare claimed that the course of true love never did run smooth, but geneticists recently claimed that assortative mating occurs in humans, meaning that humans tend to select mates that resemble themselves to some extent (Robinson et al., 2017xRobinson, M., Kleinman, A., Graff, M., Vinkhuyzen, A., Couper, D., Miller, M., Peyrot, W., Abdellaoui, A., Zietsch, B., Nolte, I. et al. Nat. Hum. Behav. 2017; 1Crossref | Scopus (5)See all ReferencesRobinson et al., 2017). This operates phenotypically, separate from confounding influences (such as socialization), and has implications for human population genetics and evolution. Another recent report found 12 genes associated with human reproductive behavior, specifically age at first birth and number of children (Barban et al., 2016xBarban, N., Jansen, R., de Vlaming, R., Vaez, A., Mandemakers, J.J., Tropf, F.C., Shen, X., Wilson, J.F., Chasman, D.I., Nolte, I.M...., BIOS Consortium, and LifeLines Cohort Study. Nat. Genet. 2016; 48: 1462–1472Crossref | PubMed | Scopus (11)See all ReferencesBarban et al., 2016). Romantic.Not even parenting practices are sacred. Hopi Hoekstra and her group study the evolution and genetics of behavior in closely related species of mice—the mice exhibit significant behavioral differences but can interbreed. This facilitates quantitative genetics and whole-genome analysis of behavioral traits. In a paper in April, the group reports on the genetics of parenting (Bendesky et al., 2017xBendesky, A., Kwon, Y.M., Lassance, J.M., Lewarch, C.L., Yao, S., Peterson, B.K., He, M.X., Dulac, C., and Hoekstra, H.E. Nature. 2017; 544: 434–439Crossref | PubMed | Scopus (12)See all ReferencesBendesky et al., 2017). Their tour de force showed heritability of a suite of parental behaviors (such as nest-making and baby-licking) and then dissected the genetic infrastructure. Even though the behaviors seem very similar in males and females, the underlying genetics can differ significantly. One behavior, nest-building, stands out, both because it seems genetically independent of other parenting tasks and because it has evolved through changes in the expression of vasopressin.Quantitative genetics is bringing powerful tools to old questions, including some deemed sacred or hopelessly complex. More drama is certain to come. Be sure to get a good seat.

Genetic Dissection of Heritable Traits in Yeast Using Bulk Segregant Analysis.

Bulk segregant analysis (BSA) is commonly used to determine the genetic basis of complex traits in yeast. This technique involves phenotyping progeny from a cross and then selectively genotyping pooled subsets of offspring with extreme phenotypes. Analysis of these genotype data can identify loci that show skewed allele frequencies in a group of phenotypically extreme individuals and that are likely to contribute to a trait. BSA can be applied to diverse strain crosses, including ones involving nonreference isolates. Further, given the high throughput of next-generation sequencing, it is possible to conduct many BSA experiments in parallel. Here, we present a BSA protocol for the generation of recombinant cross progeny. We then describe general BSA strategies for conducting phenotyping, causal loci detection, and candidate gene identification in a statistically powerful manner.

Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data.

MotivationThe identification of genetic variants influencing gene expression (known as expression quantitative trait loci or eQTLs) is important in unravelling the genetic basis of complex traits. Detecting multiple eQTLs simultaneously in a population based on paired DNA-seq and RNA-seq assays employs two competing types of models: models which rely on appropriate transformations of RNA-seq data (and are powered by a mature mathematical theory), or count-based models, which represent digital gene expression explicitly, thus rendering such transformations unnecessary. The latter constitutes an immensely popular methodology, which is however plagued by mathematical intractability.ResultsWe develop tractable count-based models, which are amenable to efficient estimation through the introduction of latent variables and the appropriate application of recent statistical theory in a sparse Bayesian modelling framework. Furthermore, we examine several transformation methods for RNA-seq read counts and we introduce arcsin, logit and Laplace smoothing as preprocessing steps for transformation-based models. Using natural and carefully simulated data from the 1000 Genomes and gEUVADIS projects, we benchmark both approaches under a variety of scenarios, including the presence of noise and violation of basic model assumptions. We demonstrate that an arcsin transformation of Laplace-smoothed data is at least as good as state-of-the-art models, particularly at small samples. Furthermore, we show that an over-dispersed Poisson model is comparable to the celebrated Negative Binomial, but much easier to estimate. These results provide strong support for transformation-based versus count-based (particularly Negative-Binomial-based) models for eQTL mapping.Availability and implementationAll methods are implemented in the free software eQTLseq: https://github.com/dvav/eQTLseqSupplementary information Supplementary data are available at Bioinformatics online.

Fast diffusion of domesticated maize to temperate zones

Adaptation to a temperate climate was a prerequisite for the spread of maize across a broad geographical range. To explicitly explore the demographic process underlying maize adaptation, we used a diffusion-based method to model the differentiation between temperate and tropical populations using the Non-Stiff Stalk group as a proxy for temperate maize. Based on multiple sequential Markovian coalescent approaches, we estimate that tropical and temperate maize diverged approximately 3‚000 to 5‚000 years ago and the population size shrank after the split. Using composite likelihood approaches, we identified a distinct tropical-temperate divergence event initiated 4‚958 years ago (95% confidence interval (CI): 4‚877–5‚039) from an ancestral population whose effective size was 24,162 (95% CI: 23,914–24,409). We found that continuous gene flow between tropical and temperate maize accompanied the differentiation of temperate maize. Long identical-by-descent tracts shared by tropical and temperate inbred lines have been identified, which might be the result of gene flow between tropical and temperate maize or artificial selection during domestication and crop improvement. Understanding the demographic history of maize diffusion not only provides evidence for population dynamics of maize, but will also assist the identification of regions under selection and the genetic basis of complex traits of agronomic importance.

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits.

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k1 (DNA accretion rate) ranged from 0.005±0.003 and for α (constant for energy for maintenance requirement) 0.134±0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k1 and α. QTLs within genomic regions mapped for k1 are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k1 were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k1 did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.