A dynamic model for functional mapping of biological rhythms

Functional mapping is a statistical method for mapping quantitative trait loci (QTLs) that regulate the dynamic pattern of a biological trait. This method integrates mathematical aspects of biological complexity into a mixture model for genetic mapping and tests the genetic effects of QTLs by comparing genotype-specific curve parameters. As a way of quantitatively specifying the dynamic behaviour of a system, differential equations have proved to be powerful for modelling and unravelling the biochemical, molecular, and cellular mechanisms of a biological process, such as biological rhythms. The equipment of functional mapping with biologically meaningful differential equations provides new insights into the genetic control of any dynamic processes. We formulate a new functional mapping framework for a dynamic biological rhythm by incorporating a group of ordinary differential equations (ODE). The Runge–Kutta fourth-order algorithm was implemented to estimate the parameters that define the system of ODE. The new model will find its implications for understanding the interplay between gene interactions and developmental pathways in complex biological rhythms.     

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.     

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.     

Functional mapping of growth and development

Understanding how an organism develops into a fully functioning adult from a mass of undifferentiated cells may reveal different strategies that allow the organism to survive under limiting conditions. Here, we review an analytical model for characterizing quantitative trait loci (QTLs) that underlie variation in growth trajectories and developmental timing. This model, called functional mapping, incorporates fundamental principles behind biological processes or networks that are bridged with mathematical functions into a statistical mapping framework. Functional mapping estimates parameters that determine the shape and function of a particular biological process, thus providing a flexible platform to test biologically meaningful hypotheses regarding the complex relationships between gene action and development.     

Evolution of brain–body allometry in Lake Tanganyika cichlids

Brain size is strongly associated with body size in all vertebrates. This relationship has been hypothesized to be an important constraint on adaptive brain size evolution. The essential assumption behind this idea is that static (i.e., within species) brain–body allometry has low ability to evolve. However, recent studies have reported mixed support for this view. Here, we examine brain–body static allometry in Lake Tanganyika cichlids using a phylogenetic comparative framework. We found considerable variation in the static allometric intercept, which explained the majority of variation in absolute and relative brain size. In contrast, the slope of the brain–body static allometry had relatively low variation, which explained less variation in absolute and relative brain size compared to the intercept and body size. Further examination of the tempo and mode of evolution of static allometric parameters confirmed these observations. Moreover, the estimated evolutionary parameters indicate that the limited observed variation in the static allometric slope could be a result of strong stabilizing selection. Overall, our findings suggest that the brain–body static allometric slope may represent an evolutionary constraint in Lake Tanganyika cichlids.     

A common genetic basis to the origin of the leaf economics spectrum and metabolic scaling allometry

Many facets of plant form and function are reflected in general cross‐taxa scaling relationships. Metabolic scaling theory (MST) and the leaf economics spectrum (LES) have each proposed unifying frameworks and organisational principles to understand the origin of botanical diversity. Here, we test the evolutionary assumptions of MST and the LES using a cross of two genetic variants of Arabidopsis thaliana. We show that there is enough genetic variation to generate a large fraction of variation in the LES and MST scaling functions. The progeny sharing the parental, naturally occurring, allelic combinations at two pleiotropic genes exhibited the theorised optimum ¾ allometric scaling of growth rate and intermediate leaf economics. Our findings: (1) imply that a few pleiotropic genes underlie many plant functional traits and life histories; (2) unify MST and LES within a common genetic framework and (3) suggest that observed intermediate size and longevity in natural populations originate from stabilising selection to optimise physiological trade‐offs.     

A genome‐wide association study to detect genetic variation for postpartum dysgalactia syndrome in five commercial pig breeding lines

Postpartum dysgalactia syndrome (PDS) in sows is an important disease after parturition with a relevant economic impact, affecting the health and welfare of both sows and piglets. The genetic background of this disease has been discussed and its heritability estimated, but further genetic analyses are lacking in detail. The aim of the current study was to detect loci affecting the susceptibility to PDS through a genome‐wide association approach. The study was designed as a family‐based association study with matched sampling of affected sows and healthy half‐ or full‐sib control sows on six farms. For the study, 597 sows (322 affected vs. 275 healthy control sows) were genotyped on 62 163 single nucleotide polymorphisms (SNPs) using the Illumina PorcineSNP60 BeadChip. After quality control, 585 sows (314 affected vs. 271 healthy control sows) and 49 740 SNPs remained for further analysis. Statistics were performed mainly with the r package genabel and included a principal component analysis. A statistically significant genome‐wide associated SNP was identified on porcine chromosome (SSC) 17. Further promising results with moderate significance were detected on SSC 13 and on an unplaced scaffold with an older annotation on SSC 15. The PRICKLE2 and NRP2 genes were identified as candidate genes near associated SNPs. Several quantitative trait loci (QTL) have been previously described in these genomic regions, including QTL for mammary gland condition, as teat number and non‐functional nipples QTL, as well as QTL for body temperature and gestation length.     

Genome-wide identification of quantitative trait loci for carcass composition and meat quality in a large-scale White Duroc × Chinese Erhualian resource population

Carcass and meat quality traits are economically important in pigs. In this study, 17 carcass composition traits and 23 meat quality traits were recorded in 1028 F₂ animals from a White Duroc × Erhualian resource population. All pigs in this experimental population were genotyped for 194 informative markers covering the entire porcine genome. Seventy-seven genome-wide significant quantitative trait loci (QTL) for carcass traits and 68 for meat quality were mapped to 34 genomic regions. These results not only confirmed many previously reported QTL but also revealed novel regions associated with the measured traits. For carcass traits, the most prominent QTL was identified for carcass length and head weight at 57 cM on SSC7, which explained up to 50% of the phenotypic variance and had a 95% confidence interval of only 3 cM. Moreover, QTL for kidney and spleen weight and lengths of cervical vertebrae were reported for the first time in pigs. For meat quality traits, two significant QTL on SSC5 and X were identified for both intramuscular fat content and marbling score in the longissimus muscle, while three significant QTL on SSC1 and SSC9 were found exclusively for IMF. Both LM and the semimembranous muscle showed common QTL for colour score on SSC4, 5, 7, 8, 13 and X and discordant QTL on other chromosomes. White Duroc alleles at a majority of QTL detected were favourable for carcass composition, while favourable QTL alleles for meat quality originated from both White Duroc and Erhualian.     

Genome‐wide identification and analysis of heterotic loci in three maize hybrids

Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large‐scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield‐related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete–incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow‐up studies for 17 heterotic loci in an independent experiment using 2225 F₂ individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants.     

SNP detection and genetic mapping of porcine genes encoding enzymes in hepatic metabolic pathways and evaluation of linkage with carcass traits

We have previously identified and mapped porcine expressed sequence tags (ESTs) derived from genes that are preferentially expressed in liver. The aim of the present study was to identify single nucleotide polymorphisms (SNPs) in porcine genes encoding enzymes in hepatic metabolic pathways and use the SNPs for mapping. Furthermore, these genes, which are involved in utilization and partitioning of nutrients, were examined for their effects on carcass and meat quality traits by linkage analyses. In total, 100 ESTs were screened for SNPs by single strand conformation polymorphism analyses across a diverse panel of animals with a 36% success rate. Twelve of 36 polymorphic loci segregated in a three-generation Duroc x Berlin Miniature Pig (F2) resource population, the DUMI resource population, and were genetically mapped. Interval mapping of the corresponding chromosomes was performed to verify mapping of the genes within quantitative trait loci (QTL) regions detected in this resource population. QTL with genome-wide significance were detected in the vicinity of GNMT, ESTL147 and HGD. These loci therefore are positional candidate genes.     

Role of seed germination in adaptation and reproductive isolation in Arabidopsis lyrata

Seed germination is an important developmental and life history stage. Yet, the evolutionary impact of germination has mainly been studied in the context of dormancy, or for its role in reproductive isolation between species. Here, we aim to examine multiple consequences of genetic divergence on germination traits between two Arabidopsis lyrata subspecies: ssp. petraea (Eurasia) and ssp. lyrata (North America). Postdormancy germination time, a potentially adaptive trait, showed differentiation between the populations, and quantitative trait loci (QTL) mapping revealed that the trait variation is mainly controlled by two antagonistic loci. These QTL areas contain several candidate genes with known function in postdormancy germination in A. thaliana. The sequence variation of three genes was consistent with differential selection, and they also included fixed nonsynonymous substitutions with potential to account for the phenotypic differentiation. We further show that the divergence between the subspecies has led to a slight but significant reduction in hybrid germination proportions, indicating incipient reproductive isolation. Comparison of reciprocal F₁ and F₂ progenies suggests that Bateson–Dobzhansky–Muller incompatibilities likely act through uniparentally inherited factors. Examination of genomewide transmission ratio distortion further revealed that cytonuclear interactions cause substantial pregermination inviability in the hybrids. These results confirm that seed germination has adaptive potential beyond the dormancy stage and that hybrid seed inviability can be one of the first reproductive barriers to arise during divergence.     

Evolutionary and ecological functional genomics,from lab to the wild

Plant phenotypes are the result of both genetic and environmental forces that act to modulate trait expression. Over the last few years, numerous approaches in functional genomics and systems biology have led to a greater understanding of plant phenotypic variation and plant responses to the environment. These approaches, and the questions that they can address, have been loosely termed evolutionary and ecological functional genomics (EEFG), and have been providing key insights on how plants adapt and evolve. In particular, by bringing these studies from the laboratory to the field, EEFG studies allow us to gain greater knowledge of how plants function in their natural contexts.     

Genome-wide identification studies – A primer to explore new genes in plant species

Genome data have accumulated rapidly in recent years, doubling roughly after every 6 months due to the influx of next-generation sequencing technologies. A plethora of plant genomes are available in comprehensive public databases. This easy access to data provides an opportunity to explore genome datasets and recruit new genes in various plant species not possible a decade ago. In the past few years, many gene families have been published using these public datasets. These genome-wide studies identify and characterize gene members, gene structures, evolutionary relationships, expression patterns, protein interactions and gene ontologies, and predict putative gene functions using various computational tools. Such studies provide meaningful information and an initial framework for further functional elucidation. This review provides a concise layout of approaches used in these gene family studies and demonstrates an outline for employing various plant genome datasets in future studies.     

Success and failure in replication of genotype–phenotype associations: How does replication help in understanding the genetic basis of phenotypic variation in outbred populations?

Recent developments in sequencing technologies have facilitated genomewide mapping of phenotypic variation in natural populations. Such mapping efforts face a number of challenges potentially leading to low reproducibility. However, reproducible research forms the basis of scientific progress. We here discuss the options for replication and the reasons for potential nonreproducibility. We then review the evidence for reproducible quantitative trait loci (QTL) with a focus on natural animal populations. Existing case studies of replication fall into three categories: (i) traits that have been mapped to major effect loci (including chromosomal inversion and supergenes) by independent research teams; (ii) QTL fine‐mapped in discovery populations; and (iii) attempts to replicate QTL across multiple populations. Major effect loci, in particular those associated with inversions, have been successfully replicated in several cases within and across populations. Beyond such major effect variants, replication has been more successful within than across populations, suggesting that QTL discovered in natural populations may often be population‐specific. This suggests that biological causes (differences in linkage patterns, allele frequencies or context‐dependencies of QTL) contribute to nonreproducibility. Evidence from other fields, notably animal breeding and QTL mapping in humans, suggests that a significant fraction of QTL is indeed reproducible in direction and magnitude at least within populations. However, there is also a large number of QTL that cannot be easily reproduced. We put forward that more studies should explicitly address the causes and context‐dependencies of QTL signals, in particular to disentangle linkage differences, allele frequency differences and gene‐by‐environment interactions as biological causes of nonreproducibility of QTL, especially between populations.     

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.     

Retracted: Screening of candidate genes and fine mapping of drought tolerance quantitative trait loci on chromosome 4 in rice (Oryza sativa L.) under drought stress

Due to severe water resource shortage, genetics of and breeding for DT (drought tolerance) in rice (Oryza sativa L.) have become one of the hot research topics. Identification of grain yield QTLs (quantitative trait loci) directly related to the DT trait of rice can provide useful information for breeding new drought‐resistant and water‐saving rice varieties via marker‐assisted selection. A population of 105 advanced BILs (backcross introgression lines) derived from a cross between Zhenshan97B and IRAT109 in Zhenshan97B background were grown under drought stress in a field experiment and phenotypic traits were investigated. The results showed that in the target interval of RM273‐RM255 on chromosome 4, three main‐effect QTLs related to panicle length, panicle number, and spikelet number per panicle were identified (LOD [logarithm of the odds] > 2.0). The panicle length‐related QTL had two loci located in the neighboring intervals of RM17308‐RM17305 and RM17349‐RM17190, which explained 18.80% and 20.42%, respectively, of the phenotypic variation, while the panicle number‐related QTL was identified in the interval of RM1354‐RM17308, explaining 11.47% of the phenotypic variation. As far as the spikelet number per panicle‐related QTL was concerned, it was found to be located in the interval of RM17308‐RM17305, which explained 28.08% of the phenotypic variation. Using the online Plant‐GE query system, a total of 13 matched ESTs (expressed sequence tags) were found in the target region, and of the 13 ESTs, 12 had corresponding predicted genes. For instance, the two ESTs CB096766 and CA765747 were corresponded to the same predicted gene LOC_Os04g46370, while the other four ESTs, CA754286, CB000011, CX056247, and CX056240, were corresponded to the same predicted gene LOC_Os04g46390.