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Danielle Posthuma and colleagues perform a large meta-analysis for intelligence and determine genetic overlap with several neuropsychiatric and metabolic traits. They find 15 new significant loci and implicate 40 new genes, most of which are predominantly expressed in the brain. Intelligence is associated with important economic and health-related life outcomes 1 . Despite intelligence having substantial heritability 2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered 3 , 4 , 5 . Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10 −8 ) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10 −6 ), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10 −6 ). Despite the well-known difference in twin-based heritability 2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation ( r g = 0.89, LD score regression P = 5.4 × 10 −29 ). These findings provide new insight into the genetic architecture of intelligence.

The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

KEY MESSAGE: Genome resolution of a major QTL associated with the Rk locus in cowpea for resistance to root-knot nematodes has significance for plant breeding programs and R gene characterization. Cowpea (Vigna unguiculata L. Walp.) is a susceptible host of root-knot nematodes (Meloidogyne spp.) (RKN), major plant-parasitic pests in global agriculture. To date, breeding for host resistance in cowpea has relied on phenotypic selection which requires time-consuming and expensive controlled infection assays. To facilitate marker-based selection, we aimed to identify and map quantitative trait loci (QTL) conferring the resistance trait. One recombinant inbred line (RIL) and two F2:3 populations, each derived from a cross between a susceptible and a resistant parent, were genotyped with genome-wide single nucleotide polymorphism (SNP) markers. The populations were screened in the field for root-galling symptoms and/or under growth-chamber conditions for nematode reproduction levels using M. incognita and M. javanica biotypes. One major QTL was mapped consistently on linkage group VuLG11 of each population. By genotyping additional cowpea lines and near-isogenic lines derived from conventional backcrossing, we confirmed that the detected QTL co-localized with the genome region associated with the Rk locus for RKN resistance that has been used in conventional breeding for many decades. This chromosomal location defined with flanking markers will be a valuable target in marker-assisted breeding and for positional cloning of genes controlling RKN resistance.

Despite being a complex many-body system, the atomic nucleus exhibits simple structures for certain ‘magic’ numbers of protons and neutrons. The calcium chain in particular is both unique and puzzling: evidence of doubly magic features are known in 40,48 Ca, and recently suggested in two radioactive isotopes, 52,54 Ca. Although many properties of experimentally known calcium isotopes have been successfully described by nuclear theory, it is still a challenge to predict the evolution of their charge radii. Here we present the first measurements of the charge radii of 49,51,52 Ca, obtained from laser spectroscopy experiments at ISOLDE, CERN. The experimental results are complemented by state-of-the-art theoretical calculations. The large and unexpected increase of the size of the neutron-rich calcium isotopes beyond N = 28 challenges the doubly magic nature of 52 Ca and opens new intriguing questions on the evolution of nuclear sizes away from stability, which are of importance for our understanding of neutron-rich atomic nuclei. Doubly magic atomic nuclei — having a magic number of both protons and neutrons — are very stable. Now, experiments revealing unexpectedly large charge radii for a series of Ca isotopes put the doubly magic nature of the 52 Ca nucleus into question.

The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies 1 – 5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies 6 – 10 . To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability 11 – 13 , we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P -value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders. Cross-trait meta-analysis on 12 psychiatric disorders identifies the genetic overlap between pairs of disorders and highlights the challenges of cross-trait genetic research.

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample ( n  = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets. Genome-wide analyses in >1 million individuals identify new loci and pathways associated with insomnia. The findings implicate key brain areas and cell types in the neurobiology of insomnia and highlight potential targets for developing new treatments.

Neuroticism is an important risk factor for psychiatric traits, including depression 1 , anxiety 2 , 3 , and schizophrenia 4 – 6 . At the time of analysis, previous genome-wide association studies 7 – 12 (GWAS) reported 16 genomic loci associated to neuroticism 10 – 12 . Here we conducted a large GWAS meta-analysis ( n  = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts ( P  = 3.49 × 10 −8 ), medium spiny neurons ( P  = 4.23 × 10 −8 ), and serotonergic neurons ( P  = 1.37 × 10 −7 ). Gene set analyses implicated three specific pathways: neurogenesis ( P  = 4.43 × 10 −9 ), behavioral response to cocaine processes ( P  = 1.84 × 10 −7 ), and axon part ( P  = 5.26 × 10 −8 ). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters 13 (‘depressed affect’ and ‘worry’), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments. A meta-analysis of genome-wide association studies for neuroticism identifies novel loci, pathways and potential drug targets. Further analysis implicates specific brain regions and evaluates genetic overlap with other neuropsychiatric traits.

Chiral effective field theory (χEFT ) provides a systematic approach to describe low-energy nuclear forces. Moreover, χEFT is able to provide well-founded estimates of statistical and systematic uncertainties—although this unique advantage has not yet been fully exploited. We fill this gap by performing an optimization and statistical analysis of all the low-energy constants (LECs) up to next-to-next-to-leading order. Our optimization protocol corresponds to a simultaneous fit to scattering and bound-state observables in the pion-nucleon, nucleon-nucleon, and few-nucleon sectors, thereby utilizing the full model capabilities of χEFT . Finally, we study the effect on other observables by demonstrating forward-error-propagation methods that can easily be adopted by future works. We employ mathematical optimization and implement automatic differentiation to attain efficient and machine-precise first- and second-order derivatives of the objective function with respect to the LECs. This is also vital for the regression analysis. We use power-counting arguments to estimate the systematic uncertainty that is inherent to χEFT , and we construct chiral interactions at different orders with quantified uncertainties. Statistical error propagation is compared with Monte Carlo sampling, showing that statistical errors are, in general, small compared to systematic ones. In conclusion, we find that a simultaneous fit to different sets of data is critical to (i) identify the optimal set of LECs, (ii) capture all relevant correlations, (iii) reduce the statistical uncertainty, and (iv) attain order-by-order convergence in χEFT . Furthermore, certain systematic uncertainties in the few-nucleon sector are shown to get substantially magnified in the many-body sector, in particular when varying the cutoff in the chiral potentials. The methodology and results presented in this paper open a new frontier for uncertainty quantification in ab initio nuclear theory.

The tin isotope 100Sn is of singular interest for nuclear structure due to its closed-shell proton and neutron configurations. It is also the heaviest nucleus comprising protons and neutrons in equal numbers—a feature that enhances the contribution of the short-range proton–neutron pairing interaction and strongly influences its decay via the weak interaction. Decay studies in the region of 100Sn have attempted to prove its doubly magic character1 but few have studied it from an ab initio theoretical perspective2,3, and none of these has addressed the odd-proton neighbours, which are inherently more difficult to describe but crucial for a complete test of nuclear forces. Here we present direct mass measurements of the exotic odd-proton nuclide 100In, the beta-decay daughter of 100Sn, and of 99In, with one proton less than 100Sn. We use advanced mass spectrometry techniques to measure 99In, which is produced at a rate of only a few ions per second, and to resolve the ground and isomeric states in 101In. The experimental results are compared with ab initio many-body calculations. The 100-fold improvement in precision of the 100In mass value highlights a discrepancy in the atomic-mass values of 100Sn deduced from recent beta-decay results4,5.Accurate mass measurements of the indium isotopes adjacent to the doubly magic 100Sn provide critical benchmarks for ab initio theory, which withstands the challenge.

Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution. Several cortical association areas have rapidly expanded in size during human evolution, including elements of the central cognitive default mode network (DMN). Here, the authors show that genes highly divergent between humans and other primates (HAR genes) are particularly expressed in these brain regions.