scholarly journals Extreme diversification of floral volatiles within and among species ofLithophragma(Saxifragaceae)

2019 ◽  
Vol 116 (10) ◽  
pp. 4406-4415 ◽  
Author(s):  
Magne Friberg ◽  
Christopher Schwind ◽  
Paulo R. Guimarães ◽  
Robert A. Raguso ◽  
John N. Thompson
Keyword(s):  
Complex Traits ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Floral Scent ◽  
Complex Trait ◽  
Interspecific Variation ◽  
Floral Volatiles ◽  
Geographic Ranges ◽  
Diverse Interactions ◽  
Ecological Convergence

A major challenge in evolutionary biology is to understand how complex traits of multiple functions have diversified and codiversified across interacting lineages and geographic ranges. We evaluate intra- and interspecific variation in floral scent, which is a complex trait of documented importance for mutualistic and antagonistic interactions between plants, pollinators, and herbivores. We performed a large-scale, phylogenetically structured study of an entire plant genus (Lithophragma, Saxifragaceae), of which several species are coevolving with specialized pollinating floral parasites of the moth genusGreya(Prodoxidae). We sampled 94Lithophragmapopulations distributed across all 12 recognizedLithophragmaspecies and subspecies, and four populations of related saxifragaceous species. Our results reveal an unusually high diversity of floral volatiles among populations, species, and clades within the genus. Moreover, we found unexpectedly major changes at each of these levels in the biosynthetic pathways used by local populations in their floral scents. Finally, we detected significant, but variable, genus- and species-level patterns of ecological convergence in the floral scent signal, including an impact of the presence and absence of two pollinatingGreyamoth species. We propose that one potential key to understanding floral scent variation in this hypervariable genus is its geographically diverse interactions with the obligate specializedGreyamoths and, in some species and sites, more generalized copollinators.

scholarly journals Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

2019 ◽  
Author(s):  
Yuhua Zhang ◽  
Corbin Quick ◽  
Ketian Yu ◽  
Alvaro Barbeira ◽  
Francesca Luca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Complex Trait ◽  
Causal Effects ◽  
Biological Mechanisms ◽  
Integrative Framework ◽  
Eqtl Data

AbstractTranscriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

scholarly journals Biobank-scale inference of ancestral recombination graphs enables genealogy-based mixed model association of complex traits

2021 ◽  
Author(s):  
Brian C Zhang ◽  
Arjun Biddanda ◽  
Pier Francesco Palamara
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Mixed Model ◽  
Large Population ◽  
Complex Trait ◽  
Genotype Imputation ◽  
Loss Of Function ◽  
Genome Wide ◽  
Wide Range ◽  
Ancestral Recombination Graphs

Accurate inference of gene genealogies from genetic data has the potential to facilitate a wide range of analyses. We introduce a method for accurately inferring biobank-scale genome-wide genealogies from sequencing or genotyping array data, as well as strategies to utilize genealogies within linear mixed models to perform association and other complex trait analyses. We use these new methods to build genome-wide genealogies using genotyping data for 337,464 UK Biobank individuals and to detect associations in 7 complex traits. Genealogy-based association detects more rare and ultra-rare signals (N = 133, frequency range 0.0004% - 0.1%) than genotype imputation from ~65,000 sequenced haplotypes (N = 65). In a subset of 138,039 exome sequencing samples, these associations strongly tag (average r = 0.72) underlying sequencing variants, which are enriched for missense (2.3×) and loss-of-function (4.5×) variation. Inferred genealogies also capture additional association signals in higher frequency variants. These results demonstrate that large-scale inference of gene genealogies may be leveraged in the analysis of complex traits, complementing approaches that require the availability of large, population-specific sequencing panels.

scholarly journals qgg: an R package for large-scale quantitative genetic analyses

2018 ◽  
Author(s):  
Palle Duun Rohde ◽  
Izel Fourie Sørensen ◽  
Peter Sørensen
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Complex Trait ◽  
R Package ◽  
Phenotypic Data ◽  
Genetic Analyses ◽  
Association Analyses ◽  
Main Page ◽  
Functional Features ◽  
The Uk

AbstractSummaryStudies of complex traits and diseases are strongly dependent on the availability of user-friendly software designed to handle large-scale genetic and phenotypic data. Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and disease phenotypes. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, performing single and multiple marker association analyses, and genomic-based predictions of phenotypes. In particular, we have developed novel predictive models that use information on functional features of the genome that enables more accurate predictions of complex trait phenotypes. We illustrates core facilities of the qgg package by analysing human standing height from the UK Biobank.Availability and implementationThe R package qgg is freely available. For latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg/.

scholarly journals Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution?

2021 ◽  
Vol 12 ◽  
Author(s):  
Evan K. Irving-Pease ◽  
Rasa Muktupavela ◽  
Michael Dannemann ◽  
Fernando Racimo
Keyword(s):  
Ancient Dna ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Complex Trait ◽  
New Wave ◽  
Trait Evolution ◽  
The Past ◽  
Different Populations ◽  
Association Data

Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations.

Modeling Extinction

2003 ◽  
Author(s):  
M. E. J. Newman ◽  
R. G. Palmer
Keyword(s):  
Evolutionary Biology ◽  
Large Scale ◽  
Competitive Exclusion ◽  
Applied Mathematics ◽  
Santa Fe ◽  
New Approach ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Extinction Events ◽  
Mass Extinction Events

Developed after a meeting at the Santa Fe Institute on extinction modeling, this book comments critically on the various modeling approaches. In the last decade or so, scientists have started to examine a new approach to the patterns of evolution and extinction in the fossil record. This approach may be called "statistical paleontology," since it looks at large-scale patterns in the record and attempts to understand and model their average statistical features, rather than their detailed structure. Examples of the patterns these studies examine are the distribution of the sizes of mass extinction events over time, the distribution of species lifetimes, or the apparent increase in the number of species alive over the last half a billion years. In attempting to model these patterns, researchers have drawn on ideas not only from paleontology, but from evolutionary biology, ecology, physics, and applied mathematics, including fitness landscapes, competitive exclusion, interaction matrices, and self-organized criticality. A self-contained review of work in this field.

Evolution and Selection of Quantitative Traits

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Mathematical Models ◽  
Quantitative Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Quantitative Traits ◽  
Specific Gene ◽  
Real World Data ◽  
Holistic Treatment ◽  
Genetics And Genomics ◽  
Selection Of

Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

A Primer of Population Genetics and Genomics

2020 ◽  
Author(s):  
Daniel L. Hartl
Keyword(s):  
Population Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Knowledge Retention ◽  
Learning By Doing ◽  
Human Populations ◽  
Gene Drive ◽  
Molecular Population Genetics ◽  
Genetics And Genomics ◽  
And Mathematics

A Primer of Population Genetics and Genomics, 4th edition, has been completely revised and updated to provide a concise but comprehensive introduction to the basic concepts of population genetics and genomics. Recent textbooks have tended to focus on such specialized topics as the coalescent, molecular evolution, human population genetics, or genomics. This primer bucks that trend by encouraging a broader familiarity with, and understanding of, population genetics and genomics as a whole. The overview ranges from mating systems through the causes of evolution, molecular population genetics, and the genomics of complex traits. Interwoven are discussions of ancient DNA, gene drive, landscape genetics, identifying risk factors for complex diseases, the genomics of adaptation and speciation, and other active areas of research. The principles are illuminated by numerous examples from a wide variety of animals, plants, microbes, and human populations. The approach also emphasizes learning by doing, which in this case means solving numerical or conceptual problems. The rationale behind this is that the use of concepts in problem-solving lead to deeper understanding and longer knowledge retention. This accessible, introductory textbook is aimed principally at students of various levels and abilities (from senior undergraduate to postgraduate) as well as practising scientists in the fields of population genetics, ecology, evolutionary biology, computational biology, bioinformatics, biostatistics, physics, and mathematics.

scholarly journals Phylogeography in an “oyster” shell provides first insights into the genetic structure of an extinct Ostrea edulis population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarah Hayer ◽  
Dirk Brandis ◽  
Alexander Immel ◽  
Julian Susat ◽  
Montserrat Torres-Oliva ◽  
...  
Keyword(s):  
Large Scale ◽  
Wadden Sea ◽  
Atlantic Coast ◽  
Phylogenetic Reconstruction ◽  
Oyster Shell ◽  
Glacial Refugia ◽  
Late Nineteenth Century ◽  
Ostrea Edulis ◽  
Geographic Ranges ◽  
The North

AbstractThe historical phylogeography of Ostrea edulis was successfully depicted in its native range for the first time using ancient DNA methods on dry shells from museum collections. This research reconstructed the historical population structure of the European flat oyster across Europe in the 1870s—including the now extinct population in the Wadden Sea. In total, four haplogroups were identified with one haplogroup having a patchy distribution from the North Sea to the Atlantic coast of France. This irregular distribution could be the result of translocations. The other three haplogroups are restricted to narrow geographic ranges, which may indicate adaptation to local environmental conditions or geographical barriers to gene flow. The phylogenetic reconstruction of the four haplogroups suggests the signatures of glacial refugia and postglacial expansion. The comparison with present-day O. edulis populations revealed a temporally stable population genetic pattern over the past 150 years despite large-scale translocations. This historical phylogeographic reconstruction was able to discover an autochthonous population in the German and Danish Wadden Sea in the late nineteenth century, where O. edulis is extinct today. The genetic distinctiveness of a now-extinct population hints at a connection between the genetic background of O. edulis in the Wadden Sea and for its absence until today.

Deconstructing and reconstructing joint hypermobility on an evo-devo perspective

Rheumatology ◽  
2021 ◽  
Author(s):  
Marco Castori
Keyword(s):  
Evolutionary Biology ◽  
Phenotypic Variability ◽  
Morphological Variability ◽  
Complex Trait ◽  
Joint Hypermobility ◽  
Integration Model ◽  
Ehlers Danlos Syndrome ◽  
Evo Devo ◽  
Derivatives Of ◽  
Continuous Traits

Abstract Joint hypermobility is a common characteristic in humans. Its non-casual association with various musculoskeletal complaints is known and currently defined “the spectrum”. It includes hypermobile Ehlers–Danlos syndrome (hEDS) and hypermobility spectrum disorders (HSD). hEDS is recognized by a set of descriptive criteria, while HSD is the background diagnosis for individuals not fulfilling these criteria. Little is known about the aetiopathogenesis of the spectrum. It may be interpreted as a complex trait according to the integration model. Particularly, the spectrum is common in the general population, affects morphology, presents extreme clinical variability and is characterized by marked sex bias without a clear Mendelian or hormonal explanation. Joint hypermobility and the other hEDS systemic criteria are intended as qualitative derivatives of continuous traits of normal morphological variability. The need for a minimum set of criteria for hEDS diagnosis implies a tendency to co-vary of these underlying continuous traits. In evolutionary biology, such a co-variation (i.e. integration) is driven by multiple forces, including genetic, developmental, functional and environmental/acquired interactors. The aetiopathogenesis of the spectrum may be resolved by a deeper understanding of phenotypic variability, which superimposes on normal morphological variability.

scholarly journals Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

2020 ◽  
Vol 10 (12) ◽  
pp. 4599-4613
Author(s):  
Fabio Morgante ◽  
Wen Huang ◽  
Peter Sørensen ◽  
Christian Maltecca ◽  
Trudy F. C. Mackay
Keyword(s):  
Precision Agriculture ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Cellular Response ◽  
Complex Trait ◽  
Sources Of Information ◽  
Starvation Resistance ◽  
Expression Levels ◽  
Chill Coma ◽  
Additional Layer

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

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