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Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci ( P  < 5 × 10 −8 ), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis. A genome-wide association study and Metabochip meta-analysis of body mass index (BMI) detects 97 BMI-associated loci, of which 56 were novel, and many loci have effects on other metabolic phenotypes; pathway analyses implicate the central nervous system in obesity susceptibility and new pathways such as those related to synaptic function, energy metabolism, lipid biology and adipogenesis. Genetic correlates of obesity In the second of two Articles in this issue from the GIANT Consortium, Elizabeth Speliotes and collegues conducted a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), commonly used to define obesity and assess adiposity, to find 97 BMI-associated loci, of which 56 were novel. Many of these loci have significant effects on other metabolic phenotypes. The 97 loci account for about 2.7% of BMI variation, and genome-wide estimates suggest common variation accounts for more than 20% of BMI variation. Pathway analyses implicate the central nervous system in obesity susceptibility including synaptic function, glutamate signaling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies. Genome-wide association analyses of 41,917 bipolar disorder cases and 371,549 controls of European ancestry provide new insights into the etiology of this disorder and identify novel therapeutic leads and potential opportunities for drug repurposing.

Genetic variants located within the 12p13.33/RAD52 locus have been associated with lung squamous cell carcinoma (LUSC). Here, within 5,947 UADT cancers and 7,789 controls from 9 different studies, we found rs10849605, a common intronic variant in RAD52, to be also associated with upper aerodigestive tract (UADT) squamous cell carcinoma cases (OR = 1.09, 95% CI: 1.04-1.15, p = 6x10(-4)). We additionally identified rs10849605 as a RAD52 cis-eQTL inUADT(p = 1x10(-3)) and LUSC (p = 9x10(-4)) tumours, with the UADT/LUSC risk allele correlated with increased RAD52 expression levels. The 12p13.33 locus, encompassing rs10849605/RAD52, was identified as a significant somatic focal copy number amplification in UADT(n = 374, q-value = 0.075) and LUSC (n = 464, q-value = 0.007) tumors and correlated with higher RAD52 tumor expression levels (p = 6x10(-48) and p = 3x10(-29) in UADT and LUSC, respectively). In combination, these results implicate increased RAD52 expression in both genetic susceptibility and tumorigenesis of UADT and LUSC tumors.

Crop yields must increase to address food insecurity. Grain weight, determined by grain length and width, is an important yield component, but our understanding of the underlying genes and mechanisms is limited. We used genetic mapping and near isogenic lines (NILs) to identify, validate and fine-map a major quantitative trait locus (QTL) on wheat chromosome 5A associated with grain weight. Detailed phenotypic characterisation of developing and mature grains from the NILs was performed. We identified a stable and robust QTL associated with a 6.9% increase in grain weight. The positive interval leads to 4.0% longer grains, with differences first visible 12 d after fertilization. This grain length effect was fine-mapped to a 4.3 cM interval. The locus also has a pleiotropic effect on grain width (1.5%) during late grain development that determines the relative magnitude of the grain weight increase. Positive NILs have increased maternal pericarp cell length, an effect which is independent of absolute grain length. These results provide direct genetic evidence that pericarp cell length affects final grain size and weight in polyploid wheat. We propose that combining genes that control distinct biological mechanisms, such as cell expansion and proliferation, will enhance crop yields.

• The cotton fibre serves as a valuable experimental system to study cell wall synthesis in plants, but our understanding of the genetic regulation of this process during fibre development remains limited. • We performed a genome-wide association study (GWAS) and identified 28 genetic loci associated with fibre quality in allotetraploid cotton. To investigate the regulatory roles of these loci, we sequenced fibre transcriptomes of 251 cotton accessions and identified 15 330 expression quantitative trait loci (eQTL). • Analysis of local eQTL and GWAS data prioritised 13 likely causal genes for differential fibre quality in a transcriptome-wide association study (TWAS). Characterisation of distal eQTL revealed unequal genetic regulation patterns between two subgenomes, highlighted by an eQTL hotspot (Hot216) that established a genome-wide genetic network regulating the expression of 962 genes. The primary regulatory role of Hot216, and specifically the gene encoding a KIP-related protein, was found to be the transcriptional regulation of genes responsible for cell wall synthesis, which contributes to fibre length by modulating the developmental transition from rapid cell elongation to secondary cell wall synthesis. • This study uncovered the genetic regulation of fibre-cell development and revealed the molecular basis of the temporal modulation of secondary cell wall synthesis during plant cell elongation.

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis - and trans -expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis -eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans -eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans -eQTL. Trans -eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes. Analyses of expression profiles from whole blood of 31,684 individuals identify cis -expression quantitative trait loci (eQTL) effects for 88% of genes and trans -eQTL effects for 37% of trait-associated variants.

In the water-limited bread wheat production environment of southern Australia, large advances in grain yield have previously been achieved through the introduction and improved understanding of agronomic traits controlled by major genes, such as the semi-dwarf plant stature and photoperiod insensitivity. However, more recent yield increases have been achieved through incremental genetic advances, of which, breeders and researchers do not fully understand the underlying mechanism(s). A doubled haploid population was utilised, derived from a cross between RAC875, a relatively drought-tolerant breeders’ line and Kukri, a locally adapted variety more intolerant of drought. Experiments were performed in 16 environments over four seasons in southern Australia, to physiologically dissect grain yield and to detect quantitative trait loci (QTL) for these traits. Two stage multi-environment trial analysis identified three main clusters of experiments (forming distinctive environments, ENVs), each with a distinctive growing season rainfall patterns. Kernels per square metre were positively correlated with grain yield and influenced by kernels per spikelet, a measure of fertility. QTL analysis detected nine loci for grain yield across these ENVs, individually accounting for between 3 and 18% of genetic variance within their respective ENVs, with the RAC875 allele conferring increased grain yield at seven of these loci. These loci were partially dissected by the detection of co-located QTL for other traits, namely kernels per square metre. While most loci for grain yield have previously been reported, their deployment and effect within local germplasm are now better understood. A number of novel loci can be further exploited to aid breeders’ efforts in improving grain yield in the southern Australian environment.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods. Using this combined approach, we prioritize 10,642 unique gene–trait pairs across 113 complex traits and diseases with high precision, finding not only well-established gene–trait relationships but nominating new genes at unresolved loci, such as LGR4 for estimated glomerular filtration rate and CCR7 for deep vein thrombosis. Overall, we demonstrate that PoPS provides a powerful addition to the gene prioritization toolbox. Polygenic Priority Score (PoPS) prioritizes candidate effector genes at complex trait loci by integrating genome-wide association summary statistics with other data types. Combining PoPS with methods that leverage local genetic signals further improves the performance.

Background Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Results We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease. Conclusions This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2 , CACNA1A and MPPED2 ), two that seem specific for migraine without aura (near SPINK2 and near FECH ) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide ( CALCA/CALCB ) and serotonin 1F receptor ( HTR1F ). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology. Genome-wide association analyses identify 123 susceptibility loci for migraine and implicate neurovascular mechanisms in its pathophysiology. Subtype analyses highlight risk loci specific for migraine with or without aura in addition to shared risk variants.