Rare and common variants: twenty arguments

Variant discovery using next-generation sequencing and its future role in pharmacogenetics

Next-generation sequencing (NGS) has enabled the discovery of a multitude of novel and mostly rare variants in pharmacogenes that may alter a patient’s therapeutic response to drugs. In addition to single nucleotide variants, structural variation affecting the number of copies of whole genes or parts of genes can be detected. While current guidelines concerning clinical implementation mostly act upon well-documented, common single nucleotide variants to guide dosing or drug selection, in silico and large-scale functional assessment of rare variant effects on protein function are at the forefront of pharmacogenetic research to facilitate their clinical integration. Here, we discuss the role of NGS in variant discovery, paving the way for more comprehensive genotype-guided pharmacotherapy that can translate to improved clinical care.

Periodontal inflammation: Integrating genes and dysbiosis

Biofilm bacteria co‐evolve and reach a symbiosis with the host on the gingival surface. The disruption of the homeostatic relationship between plaque bacteria and the host can initiate and promote periodontal disease progression. Recent advances in sequencing technologies allow researchers to profile disease‐associated microbial communities and quantify microbial metabolic activities and host transcriptional responses. In addition to confirming the findings from previous studies, new putative pathogens and novel genes that have not previously been associated with periodontitis, emerge. For example, multiple studies have reported that Synergistetes bacteria are associated with periodontitis. Genes involved in epithelial barrier defense were downregulated in periodontitis, while excessive expression of interleukin‐17 was associated with a hyperinflammatory response in periodontitis and with a unique microbial community. Bioinformatics‐enabled gene ontology pathway analyses provide a panoramic view of the bacterial and host activities as they shift from periodontal health to disease. Additionally, host innate factors, such as genetic variants identified by either a candidate‐gene approach or genome‐wide association analyses, have an impact on subgingival bacterial colonization. Transgenic mice carrying candidate genetic variants, or with the deletion of candidate genes mimicking the deleterious loss‐of‐function variant effect, provide experimental evidence validating the biologic relevance of the novel markers associated with the microbial phenotype identified through a statistical approach. Further refinement in bioinformatics, data management approaches, or statistical tools, are required to gain insight into host‐microbe interactions by harmonizing the multidimensional “big” data at the genomic, transcriptional, and proteomic levels.

Genetics of Parkinson's disease: An introspection of its journey towards precision medicine

A substantial proportion of risk for Parkinson's disease (PD) is driven by genetics. Progress in understanding the genetic basis of PD has been significant. So far, highly-penetrant rare genetic alterations in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ-1 and GBA have been linked with typical familial PD and common genetic variability at 90 loci have been linked to risk for PD. In this review, we outline the journey thus far of PD genetics, highlighting how significant advances have improved our knowledge of the genetic basis of PD risk, onset and progression. Despite remarkable progress, our field has yet to unravel how genetic risk variants disrupt biological pathways and molecular networks underlying the pathobiology of the disease. We highlight that currently identified genetic risk factors only represent a fraction of the likely genetic risk for PD. Identifying the remaining genetic risk will require us to diversify our efforts, performing genetic studies across different ancestral groups. This work will inform us on the varied genetic basis of disease across populations and also aid in fine mapping discovered loci. If we are able to take this course, we foresee that genetic discoveries in PD will directly influence our ability to predict disease and aid in defining etiological subtypes, critical steps for the implementation of precision medicine for PD.

Genetic Landscapes Reveal How Human Genetic Diversity Aligns with Geography

Geographic patterns in human genetic diversity carry footprints of population history and provide insights for genetic medicine and its application across human populations. Summarizing and visually representing these patterns of diversity has been a persistent goal for human geneticists, and has revealed that genetic differentiation is frequently correlated with geographic distance. However, most analytical methods to represent population structure do not incorporate geography directly, and it must be considered post hoc alongside a visual summary of the genetic structure. Here, we estimate "effective migration" surfaces to visualize how human genetic diversity is geographically structured. The results reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, the relationship is often subtly distorted. Overall, the visualizations provide a new perspective on genetics and geography in humans and insight to the geographic distribution of human genetic variation.

Targeted resequencing reveals rare variants enrichment in multiple sclerosis susceptibility genes

Although genome-wide association studies have identified a number of common variants associated with multiple sclerosis (MS) susceptibility, little is known about the relevance of rare variants. Here, we aimed to explore the role of rare variants in 14 MS risk genes (FCRL1, RGS1, TIMMDC1, HHEX, CXCR5, LTBR, TSFM, GALC, TRAF3, STAT3, TNFSF14, IFI30, CD40, and CYP24A1) by targeted resequencing in an Iberian population of 524 MS cases and 546 healthy controls. Four rare variants-enriched regions within CYP24A1, FCRL1, RGS1, and TRAF3 were identified as significantly associated with MS. Functional studies revealed significantly decreased regulator of G protein signaling 1 (RGS1) gene expression levels in peripheral blood mononuclear cells from MS patients with RGS1 rare variants compared to noncarriers, whereas no significant differences in gene expression were observed for CYP24A1, FCRL1, and TRAF3 between rare variants carriers and noncarriers. Immunophenotyping showed significant decrease in RGS1 expression in peripheral blood B lymphocytes from MS patients with RGS1 rare variants relative to noncarriers. Lastly, peripheral blood mononuclear cell from MS patients carrying RGS1 rare variants showed significantly lower induction of RGS1 gene expression by interferon-β compared to MS patients lacking RGS1 variants. The presence of rare variants in RGS1 reinforce the ideas of high genetic heterogeneity and a role of rare variants in MS pathogenesis.

A novel association test for rare variants based on algebraic statistics

With the rapid growth of next-generation sequencing technology, more and more rare variants are available in the human genome. In recent years, the point of study has already changed direction to rare variants in genome-wide association studies (GWAS). Although a variety of approaches have been proposed to test associations between rare variants and phenotypes of interest, it is far from the end of this problem, and it is worth exploring new statistical methods based on special features of rare variants. As we all know, the most direct way is to evaluate the association in a two-way contingency table if the phenotype is a discrete variable. The numbers of observations are very close or equal to 0s for most of cells in the contingency table due to the extremely low mutation rates of rare variants. In this paper, we propose a novel association test for rare variants based on a generalization of Fisher's exact test, and the p-value of this exact test can be computed under the multivariate hypergeometric distribution in the framework of algebraic statistics. Simulation results show that our proposed method outperforms the existing methods, despite there is heterogeneity among causal variants. We also successfully apply our method into the genetic association study of coronary artery disease and hypertension from the Wellcome Trust Case Control Consortium.

Beyond large-effect loci: large-scale GWAS reveals a mixed large-effect and polygenic architecture for age at maturity of Atlantic salmon

BACKGROUND

Understanding genetic architecture is essential for determining how traits will change in response to evolutionary processes such as selection, genetic drift and/or gene flow. In Atlantic salmon, age at maturity is an important life history trait that affects factors such as survival, reproductive success, and growth. Furthermore, age at maturity can seriously impact aquaculture production. Therefore, characterizing the genetic architecture that underlies variation in age at maturity is of key interest.

RESULTS

Here, we refine our understanding of the genetic architecture for age at maturity of male Atlantic salmon using a genome-wide association study of 11,166 males from a single aquaculture strain, using imputed genotypes at 512,397 single nucleotide polymorphisms (SNPs). All individuals were genotyped with a 50K SNP array and imputed to higher density using parents genotyped with a 930K SNP array and pedigree information. We found significant association signals on 28 of 29 chromosomes (P-values: 8.7 × 10^-133-9.8 × 10^-8), including two very strong signals spanning the six6 and vgll3 gene regions on chromosomes 9 and 25, respectively. Furthermore, we identified 116 independent signals that tagged 120 candidate genes with varying effect sizes. Five of the candidate genes found here were previously associated with age at maturity in other vertebrates, including humans.

DISCUSSION

These results reveal a mixed architecture of large-effect loci and a polygenic component that consists of multiple smaller-effect loci, suggesting a more complex genetic architecture of Atlantic salmon age at maturity than previously thought. This more complex architecture will have implications for selection on this key trait in aquaculture and for management of wild salmon populations.

A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping

Treatment-resistant depression (TRD) occurs in ~30% of patients with major depressive disorder (MDD) but the genetics of TRD was previously poorly investigated. Whole exome sequencing and genome-wide genotyping were available in 1209 MDD patients after quality control. Antidepressant response was compared to non-response to one treatment and non-response to two or more treatments (TRD). Differences in the risk of carrying damaging variants were tested. A score expressing the burden of variants in genes and pathways was calculated weighting each variant for its functional (Eigen) score and frequency. Gene-based and pathway-based scores were used to develop predictive models of TRD and non-response using gradient boosting in 70% of the sample (training) which were tested in the remaining 30% (testing), evaluating also the addition of clinical predictors. Independent replication was tested in STAR*D and GENDEP using exome array-based data. TRD and non-responders did not show higher risk to carry damaging variants compared to responders. Genes/pathways associated with TRD included those modulating cell survival and proliferation, neurodegeneration, and immune response. Genetic models showed significant prediction of TRD vs. response and they were improved by the addition of clinical predictors, but they were not significantly better than clinical predictors alone. Replication results were driven by clinical factors, except for a model developed in subjects treated with serotonergic antidepressants, which showed a clear improvement in prediction at the extremes of the genetic score distribution in STAR*D. These results suggested relevant biological mechanisms implicated in TRD and a new methodological approach to the prediction of TRD.

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

METHODS

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

RESULTS

Two genes (CSMD1, p = 5.32×10-6; CNTNAP5, p = 1.32×10-6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10-5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10-6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10-4) and Alzheimer Disease Up (FDR q = 6.12×10-4).

CONCLUSIONS

Both rare variants analysis and imaging-genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

Transcriptional Regulation of Channelopathies in Genetic and Acquired Epilepsies

Epilepsy is a common neurological disorder characterized by recurrent uncontrolled seizures and has an idiopathic “genetic” etiology or a symptomatic “acquired” component. Genetic studies have revealed that many epilepsy susceptibility genes encode ion channels, including voltage-gated sodium, potassium and calcium channels. The high prevalence of ion channels in epilepsy pathogenesis led to the causative concept of “ion channelopathies,” which can be elicited by specific mutations in the coding or promoter regions of genes in genetic epilepsies. Intriguingly, expression changes of the same ion channel genes by augmentation of specific transcription factors (TFs) early after an insult can underlie acquired epilepsies. In this study, we review how the transcriptional regulation of ion channels in both genetic and acquired epilepsies can be controlled, and compare these epilepsy “ion channelopathies” with other neurodevelopmental disorders.

Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.

Sleep deprivation, vigilant attention, and brain function: a review

Vigilant attention is a major component of a wide range of cognitive performance tasks. Vigilant attention is impaired by sleep deprivation and restored after rest breaks and (more enduringly) after sleep. The temporal dynamics of vigilant attention deficits across hours and days are driven by physiologic, sleep regulatory processes-a sleep homeostatic process and a circadian process. There is also evidence of a slower, allostatic process, which modulates the sleep homeostatic setpoint across days and weeks and is responsible for cumulative deficits in vigilant attention across consecutive days of sleep restriction. There are large inter-individual differences in vulnerability to sleep loss, and these inter-individual differences constitute a pronounced human phenotype. However, this phenotype is multi-dimensional; vulnerability in terms of vigilant attention impairment can be dissociated from vulnerability in terms of other cognitive processes such as attentional control. The vigilance decrement, or time-on-task effect-a decline in performance across the duration of a vigilant attention task-is characterized by progressively increasing response variability, which is exacerbated by sleep loss. This variability, while crucial to understanding the impact of sleep deprivation on performance in safety-critical tasks, is not well explained by top-down regulatory mechanisms, such as the homeostatic and circadian processes. A bottom-up, neuronal pathway-dependent mechanism involving use-dependent, local sleep may be the main driver of response variability. This bottom-up mechanism may also explain the dissociation between cognitive processes with regard to trait vulnerability to sleep loss.

An adaptive test for meta-analysis of rare variant association studies

Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data. One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github.com/ytzhong/metaRV and will be on CRAN soon.

A permutation method for detecting trend correlations in rare variant association studies

In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

Searching for solutions to the missing heritability problem

Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth.

Minor alleles are associated with white rust (Albugo occidentalis) susceptibility in spinach (Spinacia oleracea)

Minor alleles (MA) have been associated with disease incidence in human studies, enabling the identification of diagnostic risk factors for various diseases. However, allelic mapping has rarely been performed in plant systems. The goal of this study was to determine whether a difference in MA prevalence is a strong enough risk factor to indicate a likely significant difference in disease resistance against white rust (WR; Albugo occidentalis) in spinach (Spinacia oleracea). We used WR disease severity ratings (WR-DSRs) in a diversity panel of 267 spinach accessions to define resistant- and susceptibility-associated groups within the distribution scores and then tested the single-nucleotide polymorphism (SNP) variants to interrogate the MA prevalence in the most susceptible (MS) vs. most resistant (MR) individuals using permutation-based allelic association tests. A total of 448 minor alleles associated with WR severity were identified in the comparison between the 25% MS and the 25% MR accessions, while the MA were generally similar between the two halves of the interquartile range. The minor alleles in the MS group were distributed across all six chromosomes and made up ~71% of the markers that were also strongly associated with WR in parallel performed genome-wide association study. These results indicate that susceptibility may be highly determined by the disproportionate overrepresentation of minor alleles, which could be used to select for resistant plants. Furthermore, by focusing on the distribution tails, allelic mapping could be used to identify plant markers associated with quantitative traits on the most informative segments of the phenotypic distribution.

Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies

Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

Genetics of COPD

Although chronic obstructive pulmonary disease (COPD) risk is strongly influenced by cigarette smoking, genetic factors are also important determinants of COPD. In addition to Mendelian syndromes such as alpha-1 antitrypsin deficiency, many genomic regions that influence COPD susceptibility have been identified in genome-wide association studies. Similarly, multiple genomic regions associated with COPD-related phenotypes, such as quantitative emphysema measures, have been found. Identifying the functional variants and key genes within these association regions remains a major challenge. However, newly identified COPD susceptibility genes are already providing novel insights into COPD pathogenesis. Network-based approaches that leverage these genetic discoveries have the potential to assist in decoding the complex genetic architecture of COPD.

Genotype imputation and reference panel: a systematic evaluation on haplotype size and diversity

Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that imputation accuracy could always benefit from the increased panel size when the reference panel was population specific, the results brought two new thoughts. First, when the haplotype size of the reference panel was fixed, the imputation accuracy of common and low-frequency variants (Minor Allele Frequency (MAF) > 0.5%) decreased while the population diversity of the reference panel increased, but for rare variants (MAF < 0.5%), a small fraction of diversity in panel could improve imputation accuracy. Second, when the haplotype size of the reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF > 5%) for the European population could always benefit from the expanding sample size. However, for the Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of an extra diverse sample (8-21%). In addition, we evaluated the imputation performances in the existing reference panels, such as the Haplotype Reference Consortium (HRC), 1000 Genomes Project Phase 3 and the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE). For the European population, the HRC panel showed the best performance in our analysis. For the Han Chinese population, we proposed an optimum imputation reference panel constituent ratio if researchers would like to customize their own sequenced reference panel, but a high-quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome; a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).

Extensive impact of low-frequency variants on the phenotypic landscape at population-scale

Genome-wide association studies (GWAS) allow to dissect complex traits and map genetic variants, which often explain relatively little of the heritability. One potential reason is the preponderance of undetected low-frequency variants. To increase their allele frequency and assess their phenotypic impact in a population, we generated a diallel panel of 3025 yeast hybrids, derived from pairwise crosses between natural isolates and examined a large number of traits. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a third is governed by non-additive effects, with complete dominance having a key role. By performing GWAS on the diallel panel, we found that associated variants with low frequency in the initial population are overrepresented and explain a fraction of the phenotypic variance as well as an effect size similar to common variants. Overall, we highlighted the relevance of low-frequency variants on the phenotypic variation.

Rare variants contribute disproportionately to quantitative trait variation in yeast

How variants with different frequencies contribute to trait variation is a central question in genetics. We use a unique model system to disentangle the contributions of common and rare variants to quantitative traits. We generated ~14,000 progeny from crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) for 38 traits. We combined our results with sequencing data for 1011 yeast isolates to show that rare variants make a disproportionate contribution to trait variation. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, and that negative selection has shaped the relationship between variant frequency and effect size. We leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects.

Rare-variant collapsing analyses for complex traits: guidelines and applications

The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.

Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives

Recent advances in high-throughput experimentation have put the exploration of genome sequences at the forefront of precision medicine. In an effort to interpret the sequencing data, numerous computational methods have been developed for evaluating the effects of genome variants. Interestingly, despite the fact that every person has as many synonymous (sSNV) as non-synonymous single nucleotide variants, our ability to predict their effects is limited. The paucity of experimentally tested sSNV effects appears to be the limiting factor in development of such methods. Here, we summarize the details and evaluate the performance of nine existing computational methods capable of predicting sSNV effects. We used a set of observed and artificially generated variants to approximate large scale performance expectations of these tools. We note that the distribution of these variants across amino acid and codon types suggests purifying evolutionary selection retaining generated variants out of the observed set; i.e., we expect the generated set to be enriched for deleterious variants. Closer inspection of the relationship between the observed variant frequencies and the associated prediction scores identifies predictor-specific scoring thresholds of reliable effect predictions. Notably, across all predictors, the variants scoring above these thresholds were significantly more often generated than observed. which confirms our assumption that the generated set is enriched for deleterious variants. Finally, we find that while the methods differ in their ability to identify severe sSNV effects, no predictor appears capable of definitively recognizing subtle effects of such variants on a large scale.

Update on the Role of the Non-Canonical Wnt/Planar Cell Polarity Pathway in Neural Tube Defects

Neural tube defects (NTDs), including spina bifida and anencephaly, represent the most severe and common malformations of the central nervous system affecting 0.7–3 per 1000 live births. They result from the failure of neural tube closure during the first few weeks of pregnancy. They have a complex etiology that implicate a large number of genetic and environmental factors that remain largely undetermined. Extensive studies in vertebrate models have strongly implicated the non-canonical Wnt/planar cell polarity (PCP) signaling pathway in the pathogenesis of NTDs. The defects in this pathway lead to a defective convergent extension that is a major morphogenetic process essential for neural tube elongation and subsequent closure. A large number of genetic studies in human NTDs have demonstrated an important role of PCP signaling in their etiology. However, the relative contribution of this pathway to this complex etiology awaits a better picture of the complete genetic architecture of these defects. The emergence of new genome technologies and bioinformatics pipelines, complemented with the powerful tool of animal models for variant interpretation as well as significant collaborative efforts, will help to dissect the complex genetics of NTDs. The ultimate goal is to develop better preventive and counseling strategies for families affected by these devastating conditions.

Inherited alterations of TGF beta signaling components in Appalachian cervical cancers

PURPOSE

This study examined targeted genomic variants of transforming growth factor beta (TGFB) signaling in Appalachian women. Appalachian women with cervical cancer were compared to healthy Appalachian counterparts to determine whether these polymorphic alleles were over-represented within this high-risk cancer population, and whether lifestyle or environmental factors modified the aggregate genetic risk in these Appalachian women.

METHODS

Appalachian women's survey data and blood samples from the Community Awareness, Resources, and Education (CARE) CARE I and CARE II studies (n = 163 invasive cervical cancer cases, 842 controls) were used to assess gene-environment interactions and cancer risk. Polymorphic allele frequencies and socio-behavioral demographic measurements were compared using t tests and χ2 tests. Multivariable logistic regression was used to evaluate interaction effects between genomic variance and demographic, behavioral, and environmental characteristics.

RESULTS

Several alleles demonstrated significant interaction with smoking (TP53 rs1042522, TGFB1 rs1800469), alcohol consumption (NQO1 rs1800566), and sexual intercourse before the age of 18 (TGFBR1 rs11466445, TGFBR1 rs7034462, TGFBR1 rs11568785). Interestingly, we noted a significant interaction between "Appalachian self-identity" variables and NQO1 rs1800566. Multivariable logistic regression of cancer status in an over-dominant TGFB1 rs1800469/TGFBR1 rs11568785 model demonstrated a 3.03-fold reduction in cervical cancer odds. Similar decreased odds (2.78-fold) were observed in an over-dominant TGFB1 rs1800469/TGFBR1 rs7034462 model in subjects who had no sexual intercourse before age 18.

CONCLUSIONS

This study reports novel associations between common low-penetrance alleles in the TGFB signaling cascade and modified risk of cervical cancer in Appalachian women. Furthermore, our unexpected findings associating Appalachian identity and NQO1 rs1800566 suggests that the complex environmental exposures that contribute to Appalachian self-identity in Appalachian cervical cancer patients represent an emerging avenue of scientific exploration.

Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

QTL and candidate genes associated with leaf anion concentrations in response to phosphate supply in Arabidopsis thaliana

BACKGROUND

Phosphorus is often present naturally in the soil as inorganic phosphate, Pi, which bio-availability is limited in many ecosystems due to low soil solubility and mobility. Plants respond to low Pi with a Pi Starvation Response, involving Pi sensing and long-distance signalling. There is extensive cross-talk between Pi homeostasis mechanisms and the homeostasis mechanism for other anions in response to Pi availability.

RESULTS

Recombinant Inbred Line (RIL) and Genome Wide Association (GWA) mapping populations, derived from or composed of natural accessions of Arabidopsis thaliana, were grown under sufficient and deficient Pi supply. Significant treatment effects were found for all traits and significant genotype x treatment interactions for the leaf Pi and sulphate concentrations. Using the RIL/QTL population, we identified 24 QTLs for leaf concentrations of Pi and other anions, including a major QTL for leaf sulphate concentration (SUL2) mapped to the bottom of chromosome (Chr) 1. GWA mapping found 188 SNPs to be associated with the measured traits, corresponding to 152 genes. One of these SNPs, associated with leaf Pi concentration, mapped to PP2A-1, a gene encoding an isoform of the catalytic subunit of a protein phosphatase 2A. Of two additional SNPs, associated with phosphate use efficiency (PUE), one mapped to AT5G49780, encoding a leucine-rich repeat protein kinase involved in signal transduction, and the other to SIZ1, a gene encoding a SUMO E3 ligase, and a known regulator of P starvation-dependent responses. One SNP associated with leaf sulphate concentration was found in SULTR2;1, encoding a sulphate transporter, known to enhance sulphate translocation from root to shoot under P deficiency. Finally, one SNP was mapped to FMO GS-OX4, a gene encoding glucosinolate S-oxygenase involved in glucosinolate biosynthesis, which located within the confidence interval of the SUL2 locus.

CONCLUSION

We identified several candidate genes with known functions related to anion homeostasis in response to Pi availability. Further molecular studies are needed to confirm and validate these candidate genes and understand their roles in examined traits. Such knowledge will contribute to future breeding for improved crop PUE .

Contribution of Four Polymorphisms in Renin-Angiotensin-Aldosterone-Related Genes to Hypertension in a Thai Population

Introduction

The roles of genes in the renin-angiotensin-aldosterone system (RAAS) in hypertension, including angiotensin-converting enzyme (ACE), angiotensinogen (AGT), angiotensin II receptor type 1 (AGTR1), and aldosterone synthase (CYP11B2), have been widely studied across different ethnicities, but there has been no such investigation in Thai population.

Materials and Methods

Using 4,150 Thais recorded in the Electricity Generating Authority of Thailand (EGAT) study, we examined the association of rs1799752, rs699, rs5186, and rs1799998 located in or near ACE, AGT, AGTR1, and CYP11B2 genes in hypertension. We investigated their roles in hypertension using multivariate logistic regression and further examined their roles in blood pressure (BP) using quantile regression. Sex, age, and BMI were adjusted as potential confounders.

Results

We did not observe associations between hypertension and rs1799752 (P=0.422), rs699 (P=0.36), rs5186 (P=0.49), and rs1799998 (P=0.71). No evidence of association between these SNPs and BP was found across an entire distribution. A nonlinear relationship between age and BP was observed.

Conclusion

In Thai population, our study showed no evidence of association between RAAS-related genes and hypertension. While our study is the first and largest study to investigate the role of RAAS-related genes in hypertension in Thai population, restricted statistical power due to limited sample size is a limitation.

Placebo effects and the molecular biological components involved

Pharmacologically inactive substances have been used in medicine for more than 700 years and can trigger beneficial responses in the human body, which is referred to as the placebo effects or placebo responses. This effect is robust enough to influence psychosocial and physiological responses to the placebo and to active treatments in many settings, which has led to increased interest from researchers. In this article, we summarise the history of placebo, the characteristics of placebo effects and recent advancements reported from the studies on placebo effects and highlight placebome studies to identify various molecular biological components associated with placebo effects. Although placebos have a long history, the placebome concept is still in its infancy. Although behavioural, neurobiological and genetic studies have identified that molecules in the dopamine, opioid, serotonin and endocannabinoid systems might be targets of the placebo effect, placebome studies with a no-treatment control (NTC) are necessary to identify whole-genome genetic targets. Although bioinformatics analysis has identified the molecular placebome module, placebome studies with NTCs are also required to validate the related findings.

Transcriptome-Wide Association Supplements Genome-Wide Association in Zea mays

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.

Genetic underpinnings of lung function and COPD

Spirometry based measurement of lung function is a global initiative for chronic obstructive lung disease (GOLD) standard to diagnose chronic obstructive pulmonary disease (COPD), one of the leading causes of mortality worldwide. The environmental and behavioural risk factors for COPD includes tobacco smoking, air pollutants and biomass fuel exposure, which can induce one or more abnormal lung function patterns. While smoking remains the primary risk factor, only 15-20% smokers develop COPD, indicating that the genetic factors are also likely to play a role. According to the study of Global Burden of Disease 2015, ∼174 million people across the world have COPD. From a comprehensive literature search conducted using the 'PubMed' and 'GWAS Catalogue' databases, and reviewing the literature available, only a limited number of studies were identified which had attempted to investigate the genetics of COPD and lung volumes, implying a huge research gap. With the advent of genomewide association studies several genetic variants linked to lung function and COPD, like HHIP, HTR4, ADAM19 and GSTCD etc., have been found and validated in different population groups, suggesting their potential role in determining lung volume and risk for COPD. This article aims at reviewing the present knowledge of the genetics of lung function and COPD.

Construction, characteristics and high throughput molecular screening methodologies in some special breeding populations: a horticultural perspective

Advanced marker technologies are widely used for evaluation of genetic diversity in cultivated crops, wild ancestors, landraces or any special plant genotypes. Developing agricultural cultivars requires the following steps: (i) determining desired characteristics to be improved, (ii) screening genetic resources to help find a superior cultivar, (iii) intercrossing selected individuals, (iv) generating genetically hybrid populations and screening them for agro-morphological or molecular traits, (v) evaluating the superior cultivar candidates, (vi) testing field performance at different locations, and (vii) certifying. In the cultivar development process valuable genes can be identified by creating special biparental or multiparental populations and analysing their association using suitable markers in given populations. These special populations and advanced marker technologies give us a deeper knowledge about the inherited agronomic characteristics. Unaffected by the changing environmental conditions, these provide a higher understanding of genome dynamics in plants. The last decade witnessed new applications for advanced molecular techniques in the area of breeding,with low costs per sample. These, especially, include next-generation sequencing technologies like reduced representation genome sequencing (genotyping by sequencing, restriction site-associated DNA). These enabled researchers to develop new markers, such as simple sequence repeat and single- nucleotide polymorphism, for expanding the qualitative and quantitative information onpopulation dynamics. Thus, the knowledge acquired from novel technologies is a valuable asset for the breeding process and to better understand the population dynamics, their properties, and analysis methods.

Interplay between genome-wide implicated genetic variants and environmental factors related to childhood antisocial behavior in the UK ALSPAC cohort

We investigated gene-environment (G × E) interactions related to childhood antisocial behavior between polymorphisms implicated by recent genome-wide association studies (GWASs) and two key environmental adversities (maltreatment and smoking during pregnancy) in a large population cohort (ALSPAC). We also studied the MAOA candidate gene and addressed comorbid attention-deficit/hyperactivity disorder (ADHD). ALSPAC is a large, prospective, ethnically homogeneous British cohort. Our outcome consisted of mother-rated conduct disorder symptom scores at age 7;9 years. G × E interactions were tested in a sex-stratified way (α = 0.0031) for four GWAS-implicated variants (for males, rs4714329 and rs9471290; for females, rs2764450 and rs11215217), and a length polymorphism near the MAOA-promoter region. We found that males with rs4714329-GG (P = 0.0015) and rs9471290-AA (P = 0.0001) genotypes were significantly more susceptible to effects of smoking during pregnancy in relation to childhood antisocial behavior. Females with the rs11215217-TC genotype (P = 0.0018) were significantly less susceptible to effects of maltreatment, whereas females with the MAOA-HL genotype (P = 0.0002) were more susceptible to maltreatment effects related to antisocial behavior. After adjustment for comorbid ADHD symptomatology, aforementioned G × E's remained significant, except for rs11215217 × maltreatment, which retained only nominal significance. Genetic variants implicated by recent GWASs of antisocial behavior moderated associations of smoking during pregnancy and maltreatment with childhood antisocial behavior in the general population. While we also found a G × E interaction between the candidate gene MAOA and maltreatment, we were mostly unable to replicate the previous results regarding MAOA-G × E's. Future studies should, in addition to genome-wide implicated variants, consider polygenic and/or multimarker analyses and take into account potential sex stratification.

Beyond the One Gene-One Disease Paradigm: Complex Genetics and Pleiotropy in Inheritable Cardiac Disorders

Inheritable cardiac disorders, which may be associated with cardiomyopathic changes, are often associated with increased risk of sudden death in the young. Early linkage analysis studies in Mendelian forms of these diseases, such as hypertrophic cardiomyopathy and long-QT syndrome, uncovered large-effect genetic variants that contribute to the phenotype. In more recent years, through genotype-phenotype studies and methodological advances in genetics, it has become evident that most inheritable cardiac disorders are not monogenic but, rather, have a complex genetic basis wherein multiple genetic variants contribute (oligogenic or polygenic inheritance). Conversely, studies on genes underlying these disorders uncovered pleiotropic effects, with a single gene affecting multiple and apparently unrelated phenotypes. In this review, we explore these 2 phenomena: on the one hand, the evidence that variants in multiple genes converge to generate one clinical phenotype, and, on the other, the evidence that variants in one gene can lead to apparently unrelated phenotypes. Although multiple conditions are addressed to illustrate these concepts, the experience obtained in the study of long-QT syndrome, Brugada syndrome, and arrhythmogenic cardiomyopathy, and in the study of functions related to SCN5A (the gene coding for the α-subunit of the most abundant sodium channel in the heart) and PKP2 (the gene coding for the desmosomal protein plakophilin-2), as well, is discussed in more detail.

GWEHS: A Genome-Wide Effect Sizes and Heritability Screener

During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations.

Integrating natural history collections and comparative genomics to study the genetic architecture of convergent evolution

Evolutionary convergence has been long considered primary evidence of adaptation driven by natural selection and provides opportunities to explore evolutionary repeatability and predictability. In recent years, there has been increased interest in exploring the genetic mechanisms underlying convergent evolution, in part, owing to the advent of genomic techniques. However, the current 'genomics gold rush' in studies of convergence has overshadowed the reality that most trait classifications are quite broadly defined, resulting in incomplete or potentially biased interpretations of results. Genomic studies of convergence would be greatly improved by integrating deep 'vertical', natural history knowledge with 'horizontal' knowledge focusing on the breadth of taxonomic diversity. Natural history collections have and continue to be best positioned for increasing our comprehensive understanding of phenotypic diversity, with modern practices of digitization and databasing of morphological traits providing exciting improvements in our ability to evaluate the degree of morphological convergence. Combining more detailed phenotypic data with the well-established field of genomics will enable scientists to make progress on an important goal in biology: to understand the degree to which genetic or molecular convergence is associated with phenotypic convergence. Although the fields of comparative biology or comparative genomics alone can separately reveal important insights into convergent evolution, here we suggest that the synergistic and complementary roles of natural history collection-derived phenomic data and comparative genomics methods can be particularly powerful in together elucidating the genomic basis of convergent evolution among higher taxa. This article is part of the theme issue 'Convergent evolution in the genomics era: new insights and directions'.

Power and sample size calculations for high-throughput sequencing-based experiments

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.

Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

Multiple Sclerosis patients carry an increased burden of exceedingly rare genetic variants in the inflammasome regulatory genes

The role of rare genetic variation and the innate immune system in the etiology of multiple sclerosis (MS) is being increasingly recognized. Recently, we described several rare variants in the NLRP1 gene, presumably conveying an increased risk for familial MS. In the present study we aimed to assess rare genetic variation in the inflammasome regulatory network. We performed whole exome sequencing of 319 probands, comprising patients with familial MS, sporadic MS and control subjects. 62 genes involved in the NLRP1/NLRP3 inflammasome regulation were screened for potentially pathogenic rare genetic variation. Aggregate mutational burden was analyzed, considering the variants' predicted pathogenicity and frequency in the general population. We demonstrate an increased (p = 0.00004) variant burden among MS patients which was most pronounced for the exceedingly rare variants with high predicted pathogenicity. These variants were found in inflammasome genes (NLRP1/3, CASP1), genes mediating inflammasome inactivation via auto and mitophagy (RIPK2, MEFV), and genes involved in response to infection with DNA viruses (POLR3A, DHX58, IFIH1) and to type-1 interferons (TYK2, PTPRC). In conclusion, we present new evidence supporting the importance of rare genetic variation in the inflammasome signaling pathway and its regulation via autophagy and interferon-β to the etiology of MS.

Evaluation of linkage disequilibrium, population structure, and genetic diversity in the U.S. peanut mini core collection

Background

Due to the recent domestication of peanut from a single tetraploidization event, relatively little genetic diversity underlies the extensive morphological and agronomic diversity in peanut cultivars today. To broaden the genetic variation in future breeding programs, it is necessary to characterize germplasm accessions for new sources of variation and to leverage the power of genome-wide association studies (GWAS) to discover markers associated with traits of interest. We report an analysis of linkage disequilibrium (LD), population structure, and genetic diversity, and examine the ability of GWA to infer marker-trait associations in the U.S. peanut mini core collection genotyped with a 58 K SNP array.

Results

LD persists over long distances in the collection, decaying to r² = half decay distance at 3.78 Mb. Structure within the collection is best explained when separated into four or five groups (K = 4 and K = 5). At K = 4 and 5, accessions loosely clustered according to market type and subspecies, though with numerous exceptions. Out of 107 accessions, 43 clustered in correspondence to the main market type subgroup whereas 34 did not. The remaining 30 accessions had either missing taxonomic classification or were classified as mixed. Phylogenetic network analysis also clustered accessions into approximately five groups based on their genotypes, with loose correspondence to subspecies and market type. Genome wide association analysis was performed on these lines for 12 seed composition and quality traits. Significant marker associations were identified for arachidic and behenic fatty acid compositions, which despite having low bioavailability in peanut, have been reported to raise cholesterol levels in humans. Other traits such as blanchability showed consistent associations in multiple tests, with plausible candidate genes.

Conclusions

Based on GWA, population structure as well as additional simulation results, we find that the primary limitations of this collection for GWAS are a small collection size, significant remaining structure/genetic similarity and long LD blocks that limit the resolution of association mapping. These results can be used to improve GWAS in peanut in future studies – for example, by increasing the size and reducing structure in the collections used for GWAS.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5824-9) contains supplementary material, which is available to authorized users.

Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.

Using natural variation to understand the evolutionary pressures on plant photosynthesis

Photosynthesis is the gateway of the Sun's energy into the biosphere and the source of the ozone layer; thus it is both provider and protector of life as we know it. Despite its pivotal role we know surprisingly little about the genetic basis of variation in photosynthesis and the selective pressures giving rise to or maintaining this variation. In this review, I will briefly summarise our current knowledge of intraspecific and interspecific variation in photosynthesis to understand the main selective constraints on photosynthesis and what this means for the future of nature and agriculture in a changing world.

Molecular Origins of Complex Heritability in Natural Genotype-to-Phenotype Relationships

The statistical complexity of heredity has long been evident, but its molecular origins remain elusive. To investigate, we charted 90 comprehensive genotype-to-phenotype maps in a large population of wild diploid yeast. In contrast to long-standing assumptions, all types of genetic variation contributed similarly to phenotype. Causal synonymous and regulatory variants exhibited distinct molecular signatures, as did nonlinearities in heterozygote fitness that likely contribute to hybrid vigor. Highly pleiotropic variants altered disordered sequences within signaling hubs, and their effects correlated across environments-even when antagonistic-suggesting that large fitness gains bring concomitant costs. Natural genetic networks defined by the causal loci differed from those determined by precise gene deletions or protein-protein interactions. Finally, we found that traits that would appear omnigenic in less powered studies do in fact have finite genetic determinants. Integrating these molecular principles will be crucial as genome reading and writing become routine in research, industry, and medicine.

Relation Between Genetic Factors and Frailty in Older Adults

OBJECTIVES

Frailty is a geriatric syndrome that identifies individuals at higher risk of disability, institutionalization, and death. We previously reported that frailty is related to oxidative stress and cognitive impairment-related biomarkers. The aim of this study was to determine whether frailty is associated with genetic variants.

DESIGN

Longitudinal population-based cohort of 2488 community-dwelling people from Toledo, Spain, aged 65 years or older.

SETTING AND PARTICIPANTS

We obtained blood samples from 78 individuals with frailty and 74 nonfrail individuals who were nonfrail (according to Fried criteria) from the Toledo Study of Healthy Ageing and extracted DNA using the Chemagic DNA blood kit.

MEASURES

Sample genotyping was carried out by means of Axiom Exome 319 Genotyping Array (Thermo Fisher Scientific), which contains 295,988 markers [single-nucleotide polymorphisms (SNPs) and rare variants], and transferred to the GeneTitan Instrument (Affymetrix).

RESULTS

We found 15 SNPs (P < .001), 18 genes (P < .005), and 4 pathways (P < .05) related to cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, regulation of autophagy, and renin-angiotensin system as the most strongly associated with frailty.

CONCLUSIONS/IMPLICATIONS

The specific genetic features related to energy metabolism, biological processes regulation, cognition, and inflammation highlighted by this preliminary analysis offer useful insights for finding biologically meaningful biomarkers of frailty that allow early diagnosis and treatment. Further research is needed to confirm our novel findings in a larger population. Indeed, the EU-funded FRAILOMICS research effort will address this question.

Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies

BACKGROUND

Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability.

RESULTS

In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy.

CONCLUSIONS

The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.

A nonparametric test for association with multiple loci in the retrospective case-control study

The genome-wide association studies aim at identifying common or rare variants associated with common diseases and explaining more heritability. It is well known that common diseases are influenced by multiple single nucleotide polymorphisms (SNPs) that are usually correlated in location or function. In order to powerfully detect association signals, it is highly desirable to take account of correlations or linkage disequilibrium (LD) information among multiple SNPs in testing for association. In this article, we propose a test SLIDE that depicts the difference of the average multi-locus genotypes between cases and controls and derive its variance-covariance matrix in the retrospective design. This matrix is composed of the pairwise LD between SNPs. Thus SLIDE can borrow the strength from an external database in the population of interest with a few thousands to hundreds of thousands individuals to improve the power for detecting association. Extensive simulations show that SLIDE has apparent superiority over the existing methods, especially in the situation involving both common and rare variants, both protective and deleterious variants. Furthermore, the efficiency of the proposed method is demonstrated in the application to the data from the Wellcome Trust Case Control Consortium.