Seed dormancy is an important trait associated with pre-sprouting and malting quality in barley. Genome-wide association studies (GWASs) have been used to detect quantitative trait loci (QTLs) underlying complex traits in major crops. In the present study, we collected 295 barley (Hordeum vulgare L.) accessions from Australia, Europe, Canada and China. A total of 25,179 single nucleotide polymorphism (SNP)/diversity arrays technology sequence markers were used for population structure, linkage disequilibrium and GWAS analysis. Candidate genes within QTL regions were investigated, and their expression levels were analysed using RNAseq data. Five QTLs for seed dormancy were identified. One QTL was mapped on chromosome 1H, and one QTL was mapped on chromosome 4H, while three QTLs were located on chromosome 5H. This is the first report of a QTL on the short arm of chromosome 5H in barley. Molecular markers linked to the QTL can be used for marker-assisted selection in barley breeding programmes.

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

We examine some of the genetic features of neuroticism (N) taking as an animal model the Maudsley Reactive (MR) and Maudsley Nonreactive (MNR) rat strains which were selectively bred, respectively, for high and low open-field defecation (OFD) starting in the late 1950s. To draw analogies with human genetic studies, we explore the genetic correlation of N with irritable bowel syndrome (IBS). We review progress with the rat model and developments in the field of human complex trait genetics, including genetic association studies that relate to current understanding of the genetics of N. The widespread differences in the tone of the peripheral sympathetic nervous system that have been found between the Maudsley strains, particularly those observed in the colon, may underly the differences in OFD (MNR, higher sympathetic tone and zero defecation). In humans, a large genome-wide association study (GWAS) reported six genes contributing to IBS, four of which were implicated in mood and anxiety disorders or were expressed in the brain, with three of the four also expressed in the nerve fibers and ganglia of the gut. Heritability of N is estimated at around 50% in twin and family studies, and GWASs identified hundreds of loci, enabling estimation of genome-wide correlations (rg) with other traits. Significantly, the estimate for rg between risk of IBS, anxiety, N, and depression was >0.5 and suggested genetic pleiotropy without evidence for causal mechanisms. Findings on the adrenergic pharmacology of the colon, coupled with new understanding of the role of the locus ceruleus in modifying afferent information from this organ, generate hypotheses that challenge traditional cause/effect notions about the relationship of the central nervous system to peripheral events in response to stress, suggest specific targets for gene action in the Maudsley model and emphasize the value of reciprocal evaluation of genetic architecture underlying N in rodents and humans.

Iron (Fe) is an essential element for all organisms. Fe deficiency can limit plant production and cause anaemia in humans. The improvement of Fe homoeostasis could resolve both problems. Fe homoeostasis in Aegilops tauschii, the D genome donor of bread wheat, is not fully understood. Here, we analysed physiological traits in 42 accessions of Ae. tauschii associated with Fe homoeostasis, i.e. mugineic acid family phytosiderophores (MAs), phenylamides, SPAD values and metal concentrations. All traits showed diversity, suggesting the presence of candidate genes in the Ae. tauschii accessions, which could improve Fe homoeostasis in bread wheat. All accessions mainly produced and secreted mainly 2′-deoxymugineic acid among MAs, but eight of them secreted unknown products from their roots under Fe deficiency. It was revealed that 15 kinds of phenylamides and 2 kinds of bread wheat phytoalexins were produced in Fe-deficient roots of Ae. tauschii. Multivariate and principal component analyses showed that chlorophyll content was correlated with shoot Fe concentration. Genome-wide association study analysis associated several genomic markers with the variations in each trait analysed. Our results suggest that Ae. tauschii has alleles that could improve Fe homoeostasis to generate Fe-deficiency-tolerant or Fe-biofortified bread wheat.

Over the years, numerous observational studies have substantiated that various dietary choices have opposing effects on CVD. However, the causal effect has not yet been established. Thus, we conducted a Mendelian randomisation (MR) analysis to reveal the causal impact of dietary habits on CVD. Genetic variants strongly associated with 20 dietary habits were selected from publicly available genome-wide association studies conducted on the UK Biobank cohort (n 449 210). Summary-level data on CVD were obtained from different consortia (n 159 836–977 323). The inverse-variance weighted method (IVW) was the primary outcome, while MR-Egger, weighted median and MR Pleiotropy RESidual Sum and Outlier were used to assess heterogeneity and pleiotropy. We found compelling evidence of a protective causal effect of genetic predisposition towards cheese consumption on myocardial infarction (IVW OR = 0·67; 95 % CI = 0·544, 0·826; P = 1·784 × 10−4) and heart failure (IVW OR = 0·646; 95 % CI = 0·513, 0·814; P = 2·135 × 10−4). Poultry intake was found to be a detrimental factor for hypertension (IVW OR = 4·306; 95 % CI = 2·158, 8·589; P = 3·416 × 10−5), while dried fruit intake was protective against hypertension (IVW OR = 0·473; 95 % CI = 0·348, 0·642; P = 1·683 × 10−6). Importantly, no evidence of pleiotropy was detected. MR estimates provide robust evidence for a causal relationship between genetic predisposition to 20 dietary habits and CVD risk, suggesting that well-planned diets may help prevent and reduce the risk of CVD.

Severe infections and psychiatric disorders have a large impact on both society and the individual. Studies investigating these conditions and the links between them are therefore important. Most past studies have focused on binary phenotypes of particular infections or overall infection, thereby losing some information regarding susceptibility to infection as reflected in the number of specific infection types, or sites, which we term infection load. In this study we found that infection load was associated with increased risk for attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, schizophrenia and overall psychiatric diagnosis. We obtained a modest but significant heritability for infection load (h2 = 0.0221), and a high degree of genetic correlation between it and overall psychiatric diagnosis (rg = 0.4298). We also found evidence supporting a genetic causality for overall infection on overall psychiatric diagnosis. Our genome-wide association study for infection load identified 138 suggestive associations. Our study provides further evidence for genetic links between susceptibility to infection and psychiatric disorders, and suggests that a higher infection load may have a cumulative association with psychiatric disorders, beyond what has been described for individual infections.

Drug development is essential to the advancement of human health, however, the process is slow, costly, and at high risk of failure at all stages. A promising strategy for expediting and improving the probability of success in the drug development process is the use of naturally randomized human genetic variation for drug target identification and validation. These data can be harnessed using the Mendelian randomization (MR) analytic paradigm to proxy the lifelong consequences of genetic perturbations of drug targets. In this review, we discuss the myriad applications of the MR paradigm for human drug target identification and validation. We review the methodology and applications of MR, key limitations of MR, and potential future opportunities for research. Throughout the review, we refer to illustrative examples of MR analyses investigating the consequences of genetic inhibition of interleukin 6 signaling which, in some cases, have anticipated results from randomized controlled trials. As human genetic data become more widely available, we predict that MR will serve as a key pillar of support for drug development efforts.

One reason behind the failure to understand the neurobiological background of affective disorders and develop more effective treatments is their heterogeneity warranting identification of clinically meaningful endophenotypes. Affective temperaments, considered subclinical manifestations and pathoplastic contributors of affective illnesses may constitute such endophenotypes. 775 general population subjects were phenotyped for affective temperaments using TEMPS-A, and genotyped using Illumina’s CoreExom PsychChip yielding 573141 variants. Primary SNP-based association tests were calculated using linear regression models assuming an additive genetic effect with the first 10 calculated principal components, gender, age, and other affective temperaments as covariates; a Bonferroni-corrected genome-wide significance threshold set at p≤5.0×10−8, and suggestive significance threshold set at p≤1.0x10-5. SNP-level relevances were aggregated to gene-level with the PEGASUS method, with a Bonferroni-corrected significance level set at 2.0×10-6, and suggestive significance thrshold set at p≤4.0×10-4. Functional effects of most significant SNPs as reported in public open databases based on expression quantitative trait loci (eQTL) and 3D-chromatin interactions were explored using FUMA v1.3.5. In SNP-based tests a novel genome-wide significant variant, rs3798978 (p=4.44x10-8) and several other suggestively significant SNPs in ADGRB3 were found for anxious temperament along with suggestively significant SNPs for the other four affective temperaments. In gene-based tests suggestively significant findings emerged for all five temperaments. Functional analysis suggested that several of the identified variants influence gene-expression levels or participate in chromatin interactions in brain areas implicated in affective disorders. In the next step these findings should be investigated in patient samples, and in other models of affective disorders and related phenotypes.

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as “genetic propensities” for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene–environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d’être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.

Genetic studies of complex traits often show disparities in estimated heritability depending on the method used, whether by genomic associations or twin and family studies. We present a simulation of individual genomes with dynamic environmental conditions to consider how linear and nonlinear effects, gene-by-environment interactions, and gene-by-environment correlations may work together to govern the long-term development of complex traits and affect estimates of heritability from common methods. Our simulation studies demonstrate that the genetic effects estimated by genome wide association studies in unrelated individuals are inadequate to characterize gene-by-environment interaction, while including related individuals in genome-wide complex trait analysis (GCTA) allows gene-by-environment interactions to be recovered in the heritability. These theoretical findings provide an explanation for the “missing heritability” problem and bridge the conceptual gap between the most common findings of GCTA and twin studies. Future studies may use the simulation model to test hypotheses about phenotypic complexity either in an exploratory way or by replicating well-established observations of specific phenotypes.