Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Genome-wide association study reveals genes associated with the absence of intermuscular bones in tambaqui (Colossoma macropomum)

The presence of intermuscular bones in fisheries products limits the consumption and commercialization potential of many fish species, including tambaqui (Colossoma macropomum). These bones have caused medical emergencies and are an undesirable characteristic for fish farming because their removal is labor-intensive during fish processing. Despite the difficulty in identifying genes related to the lack of intermuscular bone in diverse species of fish, the discovery of individuals lacking intermuscular bones in a Neotropical freshwater characiform fish has provided a unique opportunity to delve into the genetic mechanisms underlying the pathways of intermuscular bone formation. In this study, we carried out a GWAS among boneless and wt tambaqui populations to identify markers associated with a lack of intermuscular bone. After analyzing 11 416 SNPs in 360 individuals (12 boneless and 348 bony), we report 675 significant (P_adj < 0.003) associations for this trait. Of those, 13 associations were located near candidate genes related to the reduction of bone mass, promotion of bone formation, inhibition of bone resorption, central control of bone remodeling, bone mineralization and other related functions. To the best of our knowledge, for the first time, we have successfully identified genes related to a lack of intermuscular bones using GWAS in a non-model species.     

Genetic mapping of the early responses to salt stress in Arabidopsis thaliana

Salt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single set-up is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from 1 h to 7 days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (F_v′/F_m′) greatly affected growth maintenance in the early phase of salt stress, whereas the maximum quantum yield (QY_max) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QY_max and F_v′/F_m′ were selected for validation using T-DNA insertion lines. We characterized an unknown protein kinase found in the QY_max locus that reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the candidate genes identified will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses and identifying associations across different traits and time points, which are likely to constitute the genetic components of salinity tolerance.     

Marker-evaluated selection in rice: shifts in allele frequency among bulks selected in contrasting agricultural environments identify genomic regions of importance to rice adaptation and breeding

Conventional methods for quantitative trait locus (QTL) mapping require the selection of particular traits to be measured based on assumptions as to their importance. We have tested an alternative approach for the location of QTLs—marker-evaluated selection—that makes no prior assumptions as to which traits are important. The results of phenotype selection were evaluated in the products of modified bulk-population breeding that was replicated across a range of rice ecosystems. Selection was carried out in close collaboration with farmers in bulk populations that were all derived from a cross between an Indian upland variety (Kalinga III) and a high-yielding semi-dwarf variety (IR64). Twenty-seven diverse bulks were produced that were screened with molecular markers in order to determine whether shifts could be detected in marker allele frequency as a result of selection and if such changes varied by genomic region across ecosystems. Marker loci linked to important traits for adaptation to specific environments were identified without making any prior assumptions about which traits might be important. Genomic regions from Kalinga III were strongly selected in the upland environments and regions from IR64 in the lowland ones. However, exceptions occurred where the upland parent contributed positively to lowland adaptation and vice versa. The results can be used as a basis for the development of second-cycle varieties, using marker-assisted selection to produce genotypic ideotypes for specific target environments. The very strong selection for genomic regions from the adapted parents of the wide (upland × lowland) cross indicates that, in non-marker-assisted breeding, where genetically distant parents have been used, modified backcross breeding should be efficient. A single backcross to the adapted parent for a specific ecosystem will result in a higher frequency of segregants with the desired high genetic contribution from the adapted parent.     

Genetic Variation for Life History Sensitivity to Seasonal Warming in Arabidopsis thaliana

Climate change has altered life history events in many plant species; however, little is known about genetic variation underlying seasonal thermal response. In this study, we simulated current and three future warming climates and measured flowering time across a globally diverse set of Arabidopsis thaliana accessions. We found that increased diurnal and seasonal temperature (1°–3°) decreased flowering time in two fall cohorts. The early fall cohort was unique in that both rapid cycling and overwintering life history strategies were revealed; the proportion of rapid cycling plants increased by 3–7% for each 1° temperature increase. We performed genome-wide association studies (GWAS) to identify the underlying genetic basis of thermal sensitivity. GWAS identified five main-effect quantitative trait loci (QTL) controlling flowering time and another five QTL with thermal sensitivity. Candidate genes include known flowering loci; a cochaperone that interacts with heat-shock protein 90; and a flowering hormone, gibberellic acid, a biosynthetic enzyme. The identified genetic architecture allowed accurate prediction of flowering phenotypes (R² > 0.95) that has application for genomic selection of adaptive genotypes for future environments. This work may serve as a reference for breeding and conservation genetic studies under changing environments.     

New high-quality peach (Prunus persica L. Batsch) genome assembly to analyze the molecular evolutionary mechanism of volatile compounds in peach fruits

Peach (Prunus persica L. Batsch) is an economically important fruit crop worldwide. Although a high-quality peach genome has previously been published, Sanger sequencing was used for its assembly, which generated short contigs. Here, we report a chromosome-level genome assembly and sequence analysis of Chinese Cling, an important founder cultivar for peach breeding programs worldwide. The assembled genome contained 247.33 Mb with a contig N50 of 4.13 Mb and a scaffold N50 of 29.68 Mb, representing 99.8% of the estimated genome. Comparisons between this genome and the recently published one (Lovell peach) uncovered 685 407 single nucleotide polymorphisms, 162 655 insertions and deletions, and 16 248 structural variants. Gene family analysis highlighted the contraction of the gene families involved in flavone, flavonol, flavonoid, and monoterpenoid biosynthesis. Subsequently, the volatile compounds of 256 peach varieties were quantitated in mature fruits in 2015 and 2016 to perform a genome-wide association analysis. A comparison with the identified domestication genomic regions allowed us to identify 25 quantitative trait loci, associated with seven volatile compounds, in the domestication region, which is consistent with the differences in volatile compounds between wild and cultivated peaches. Finally, a gene encoding terpene synthase, located within a previously reported quantitative trait loci region, was identified to be associated with linalool synthesis. Such findings highlight the importance of this new assembly for the analysis of evolutionary mechanisms and gene identification in peach species. Furthermore, this high-quality peach genome provides valuable information for future fruit improvement.     

Pool‐hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples

Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool‐Seq) is becoming a popular strategy for genome‐wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool‐seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool‐Seq data. We introduce Pool‐hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool‐Seq sample. Pool‐hmm includes several options that allow a flexible analysis of Pool‐Seq data, and can be run in parallel on several processors. Source code and documentation for Pool‐hmm is freely available at https://qgsp.jouy.inra.fr/ .     

Neural network on interval-censored data with application to the prediction of Alzheimer's disease

Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large-scale genetic data, interval-censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval-censored and left-truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval-censored data (NN-IC) to construct a prediction model using top variants identified from the genome-wide test. Comprehensive simulation studies show that the NN-IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN-IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles. Data used in the preparation of this article were obtained from the ADNI database.     

Long-term frequency shifts in the chromosomal polymorphisms of Drosophila robusta in the Great Smoky Mountains

We resurveyed an elevational transect in the Great Smoky Mountains National Park first sampled in 1947 for chromosomal polymorphisms in populations of Drosophila robusta . Combining these results with those from previous surveys, unpublished data, and long-term meteorological data from this region up to 2003, we found that these chromosomal polymorphisms had continued to shift in frequency consistent with long-term temperature changes, yet had maintained elevational clines. Intensity of linkage disequilibrium for X-chromosome gene arrangements had shifted up and down the transect over the 56-year sampling period, suggesting shifting patterns of adaptation. Chromosomal frequency changes through the 1980s clearly demonstrated concerted directional evolution in response to cooler temperatures, but over the 20 years until 2003, frequency changes in most high-elevation populations reversed for many of the most temperature-sensitive gene arrangements.  © 2006 The Linnean Society of London, Biological Journal of the Linnean Society , 2006, 88 , 131–141.     

Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests

The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners’ curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners’ curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.     

Adaptations to floodplains in Populus and Salix: the role of collet hairs

There has been growing recent interest in the adaptations of tree species to seedling establishment in active northern temperate floodplains. In the case of Populus and Salix, most or all of the recent papers overlook a scattered, earlier literature showing that, in many Populus and Salix species, just after germination, the collet produces prominent hairs which, in young seedlings, may play an important role in anchorage and water absorption, the collet being the transition zone between the hypocotyl and the radicle. This earlier literature is briefly reviewed. The significance of collet hairs is then related to the very high seedling mortalities in riparian systems caused by high river flows (scour) or by drought stress arising from water level declines and coarse sediment textures. The need for work to establish just which species have collet hairs and to quantify the effect of collet hairs on anchorage and water absorption is emphasized.     

The collaborative study on the genetics of alcoholism: Genetics

This review describes the genetic approaches and results from the family-based Collaborative Study on the Genetics of Alcoholism (COGA). COGA was designed during the linkage era to identify genes affecting the risk for alcohol use disorder (AUD) and related problems, and was among the first AUD-focused studies to subsequently adopt a genome-wide association (GWAS) approach. COGA's family-based structure, multimodal assessment with gold-standard clinical and neurophysiological data, and the availability of prospective longitudinal phenotyping continues to provide insights into the etiology of AUD and related disorders. These include investigations of genetic risk and trajectories of substance use and use disorders, phenome-wide association studies of loci of interest, and investigations of pleiotropy, social genomics, genetic nurture, and within-family comparisons. COGA is one of the few AUD genetics projects that includes a substantial number of participants of African ancestry. The sharing of data and biospecimens has been a cornerstone of the COGA project, and COGA is a key contributor to large-scale GWAS consortia. COGA's wealth of publicly available genetic and extensive phenotyping data continues to provide a unique and adaptable resource for our understanding of the genetic etiology of AUD and related traits.     

Sortilin: an unusual suspect in cholesterol metabolism: from GWAS identification to in vivo biochemical analyses, sortilin has been identified as a novel mediator of human lipoprotein metabolism

The concentration of low-density lipoprotein (LDL) cholesterol (C) in plasma is a key determinant of cardiovascular disease risk and human genetic studies have long endeavoured to elucidate the pathways that regulate LDL metabolism. Massive genome-wide association studies (GWASs) of common genetic variation associated with LDL-C in the population have implicated SORT1 in LDL metabolism. Using experimental paradigms and standards appropriate for understanding the mechanisms by which common variants alter phenotypic expression, three recent publications have presented divergent and even contradictory findings. Interestingly, although these reports each linked SORT1 to LDL metabolism, they did not agree on a mechanism to explain the association. Here, we review recent mechanistic studies of SORT1 - the first gene identified by GWAS as a determinant of plasma LDL-C to be evaluated mechanistically.     

On the Prospects of Whole-Genome Association Mapping in Saccharomyces cerevisiae

Advances in sequencing technology have enabled whole-genome sequences to be obtained from multiple individuals within species, particularly in model organisms with compact genomes. For example, 36 genome sequences of Saccharomyces cerevisiae are now publicly available, and SNP data are available for even larger collections of strains. One potential use of these resources is mapping the genetic basis of phenotypic variation through genome-wide association (GWA) studies, with the benefit that associated variants can be studied experimentally with greater ease than in outbred populations such as humans. Here, we evaluate the prospects of GWA studies in S. cerevisiae strains through extensive simulations and a GWA study of mitochondrial copy number. We demonstrate that the complex and heterogeneous patterns of population structure present in yeast populations can lead to a high type I error rate in GWA studies of quantitative traits, and that methods typically used to control for population stratification do not provide adequate control of the type I error rate. Moreover, we show that while GWA studies of quantitative traits in S. cerevisiae may be difficult depending on the particular set of strains studied, association studies to map cis-acting quantitative trait loci (QTL) and Mendelian phenotypes are more feasible. We also discuss sampling strategies that could enable GWA studies in yeast and illustrate the utility of this approach in Saccharomyces paradoxus. Thus, our results provide important practical insights into the design and interpretation of GWA studies in yeast, and other model organisms that possess complex patterns of population structure.     

Selection in action: dissecting the molecular underpinnings of the increasing muscle mass of Belgian Blue Cattle

Background

Belgian Blue cattle are famous for their exceptional muscular development or “double-muscling”. This defining feature emerged following the fixation of a loss-of-function variant in the myostatin gene in the eighties. Since then, sustained selection has further increased muscle mass of Belgian Blue animals to a comparable extent. In the present paper, we study the genetic determinants of this second wave of muscle growth.

Results

A scan for selective sweeps did not reveal the recent fixation of another allele with major effect on muscularity. However, a genome-wide association study identified two genome-wide significant and three suggestive quantitative trait loci (QTL) affecting specific muscle groups and jointly explaining 8-21% of the heritability. The top two QTL are caused by presumably recent mutations on unique haplotypes that have rapidly risen in frequency in the population. While one appears on its way to fixation, the ascent of the other is compromised as the likely underlying MRC2 mutation causes crooked tail syndrome in homozygotes. Genomic prediction models indicate that the residual additive variance is largely polygenic.

Conclusions

Contrary to complex traits in humans which have a near-exclusive polygenic architecture, muscle mass in beef cattle (as other production traits under directional selection), appears to be controlled by (i) a handful of recent mutations with large effect that rapidly sweep through the population, and (ii) a large number of presumably older variants with very small effects that rise slowly in the population (polygenic adaptation).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-796) contains supplementary material, which is available to authorized users.     

Explaining the heritability of an ecologically significant trait in terms of individual quantitative trait loci

Most natural populations display substantial genetic variation in behaviour, morphology, physiology, life history and the susceptibility to disease. A major challenge is to determine the contributions of individual loci to variation in complex traits. Quantitative trait locus (QTL) mapping has identified genomic regions affecting ecologically significant traits of many species. In nearly all cases, however, the importance of these QTLs to population variation remains unclear. In this paper, we apply a novel experimental method to parse the genetic variance of floral traits of the annual plant Mimulus guttatus into contributions of individual QTLs. We first use QTL-mapping to identify nine loci and then conduct a population-based breeding experiment to estimate V(Q), the genetic variance attributable to each QTL. We find that three QTLs with moderate effects explain up to one-third of the genetic variance in the natural population. Variation at these loci is probably maintained by some form of balancing selection. Notably, the largest effect QTLs were relatively minor in their contribution to heritability.