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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.

Interindividual differences in drug disposition are important causes for adverse drug reactions and lack of drug response. The majority of phase I and phase II drug-metabolizing enzymes (DMEs) are polymorphic and constitute essential factors for the outcome of drug therapy. Recently, both genome-wide association (GWA) studies with a focus on drug response, as well as more targeted studies of genes encoding DMEs have revealed in-depth information and provided additional information for variation in drug metabolism and drug response, resulting in increased knowledge that aids drug development and clinical practice. In addition, an increasing number of meta-analyses have been published based on several original and often conflicting pharmacogenetic studies. Here, we review data regarding the pharmacogenomics of DMEs, with particular emphasis on novelties. We conclude that recent studies have emphasized the importance of CYP2C19 polymorphism for the effects of clopidogrel, whereas the CYP2C9 polymorphism appears to have a role in anticoagulant treatment, although inferior to VKORC1. Furthermore, the analgesic and side effects of codeine in relation to CYP2D6 polymorphism are supported and the influence of CYP2D6 genotype on breast cancer recurrence during tamoxifen treatment appears relevant as based on three large studies. The influence of CYP2D6 polymorphism on the effect of antidepressants in a clinical setting is yet without any firm evidence, and the relation between CYP2D6 ultrarapid metabolizers and suicide behavior warrants further studies. There is evidence for the influence of CYP3A5 polymorphism on tacrolimus dose, although the influence on response is less studied. Recent large GWA studies support a link between CYP1A2 polymorphism and blood pressure as well as coffee consumption, and between CYP2A6 polymorphism and cigarette consumption, which in turn appears to influence the lung cancer incidence. Regarding phase II enzyme polymorphism, the anticancer treatment with mercaptopurines and irinotecan is still considered important in relation to the polymorphism of TPMT and UGT1A1 , respectively. There is a need for further clarification of the clinical importance and use of all these findings, but the recent research in the field that encompasses larger studies and a whole genome perspective, improves the possibilities be able to make firm and cost-effective recommendations for drug treatment in the future.

ObjectivesMethotrexate has long been used successfully and is preferred worldwide for the treatment of low-risk gestational trophoblastic neoplasia. However, 26.4% of patients develop resistance and require changes to second-line chemotherapy. In the search for personalised treatment approaches, a link has been found between the pharmacogenomics of methotrexate and the response in various diseases. The aim of this study was to explore the effects of ABCB1 and SCLO1B1 gene polymorphisms and the methotrexate treatment response in patients with low-risk gestational trophoblastic neoplasia. Secondary objectives were to investigate the association of single nucleotide polymorphism (SNP) genotypes with toxicity profiles, and to evaluate other factors associated with the response.MethodsRecords of all patients with low-risk gestational trophoblastic neoplasia were reviewed and patients who received methotrexate as a single agent were invited to participate in the study. DNA was extracted from peripheral blood samples from 18 patients and assessed for ABCB1 (3435C>T) and SCLO1B1 (521T>C).ResultsFor the ABCB1 polymorphism, CT was the most common genotype (61.1%), followed by CC (27.8%) and TT (11.1%), indicating that TT had a 1.6-fold higher risk of methotrexate-resistance when compared to the wild-type and heterozygous alleles. The risk of methotrexate-related toxicity was 2.67-fold higher in CT/CC patients who showed a better response to methotrexate. The SCLO1B1 polymorphism was not associated with treatment outcomes.ConclusionsABCB1 polymorphism might be useful as a biomarker for predicting the response to methotrexate in patients with low-risk gestational trophoblastic neoplasia.

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

The rapid development and application of molecular marker assays have facilitated genomic selection and genome‐wide linkage and association studies in wheat breeding. Although PCR‐based markers (e.g. simple sequence repeats and functional markers) and genotyping by sequencing have contributed greatly to gene discovery and marker‐assisted selection, the release of a more accurate and complete bread wheat reference genome has resulted in the design of single‐nucleotide polymorphism (SNP) arrays based on different densities or application targets. Here, we evaluated seven types of wheat SNP arrays in terms of their SNP number, distribution, density, associated genes, heterozygosity and application. The results suggested that the Wheat 660K SNP array contained the highest percentage (99.05%) of genome‐specific SNPs with reliable physical positions. SNP density analysis indicated that the SNPs were almost evenly distributed across the whole genome. In addition, 229 266 SNPs in the Wheat 660K SNP array were located in 66 834 annotated gene or promoter intervals. The annotated genes revealed by the Wheat 660K SNP array almost covered all genes revealed by the Wheat 35K (97.44%), 55K (99.73%), 90K (86.9%) and 820K (85.3%) SNP arrays. Therefore, the Wheat 660K SNP array could act as a substitute for other 6 arrays and shows promise for a wide range of possible applications. In summary, the Wheat 660K SNP array is reliable and cost‐effective and may be the best choice for targeted genotyping and marker‐assisted selection in wheat genetic improvement.

BackgroundThe B cell-activating factor (BAFF) promotes the maturation, differentiation, and survival of B lymphocytes, which play an important role in the pathogenesis of systemic lupus erythematosus (SLE). This cytokine has been associated with SLE disease activity, hence TNFSF13B gene, which encodes BAFF, along with its diverse single-nucleotide polymorphisms (SNPs) might also be associated with more severe SLE activity. The aim of this study was to explore the association between TNFSF13B genetic polymorphisms with disease activity, as well as serum soluble BAFF (s-BAFF) levels in SLE patients.MethodsThis cross-sectional study included SLE patients in rheumatology clinic and in-patient wards of Dr. Hasan Sadikin General Hospital, Bandung, between February 2020 -March 2022. One single nucleotide rs9514828 (C>T -871) polymorphism in the 5’ regulatory region of the TNFSF13B gene were identified by PCR-sequencing. Serum BAFF levels were measured by ELISA and disease activity was measured by MEX-SLEDAI score. The active disease was determined by MEX-SLEDAI 2. The statistical analysis was performed using Mann-Whitney, Chi-Square, and Spearman test for the correlation.ResultsThere were 107 SLE patients, all female with median age of 32 years old (interquartile range [IQR], 15–64). The median disease duration was 5 years (IQR 0.5–22), whereas the median s-BAFF levels was 846.9 pg/mL (IQR 185–6979.88). The s-BAFF levels were significantly elevated in active disease patients compared to inactive disease (953.17 pg/ml vs 781.4 pg/mL; p 0.008). There was a weak positive correlation between s-BAFF levels and disease activity (r 0.311; p 0.001). There were no differences in genotype TNFSF13B polymorphisms with the disease activity.ConclusionsAlthough the rs9514828 (C>T -871) of TNFSF13B gene promoter does not seem to be related with SLE activity, s-BAFF levels is, however, correlated with disease activity. Further study to explore other polymorphisms in TNFSF13B gene is of great interest.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4 – 23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice. A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

Coding variants represent many of the strongest associations between genotype and phenotype; however, they exhibit inter-individual differences in effect, termed ‘variable penetrance’. Here, we study how cis-regulatory variation modifies the penetrance of coding variants. Using functional genomic and genetic data from the Genotype-Tissue Expression Project (GTEx), we observed that in the general population, purifying selection has depleted haplotype combinations predicted to increase pathogenic coding variant penetrance. Conversely, in cancer and autism patients, we observed an enrichment of penetrance increasing haplotype configurations for pathogenic variants in disease-implicated genes, providing evidence that regulatory haplotype configuration of coding variants affects disease risk. Finally, we experimentally validated this model by editing a Mendelian single-nucleotide polymorphism (SNP) using CRISPR/Cas9 on distinct expression haplotypes with the transcriptome as a phenotypic readout. Our results demonstrate that joint regulatory and coding variant effects are an important part of the genetic architecture of human traits and contribute to modified penetrance of disease-causing variants. Analysis of GTEx, cancer and autism data sets shows that cis-regulatory variation can modify the penetrance of coding variants. Deleterious coding variants on regulatory haplotypes resulting in high expression are enriched in disease cohorts and selected against in general populations.

Psoriasis is a common inflammatory skin disease that has been reported to be associated with obesity. We aimed to investigate a possible causal relationship between body mass index (BMI) and psoriasis. Following a review of published epidemiological evidence of the association between obesity and psoriasis, mendelian randomization (MR) was used to test for a causal relationship with BMI. We used a genetic instrument comprising 97 single-nucleotide polymorphisms (SNPs) associated with BMI as a proxy for BMI (expected to be much less confounded than measured BMI). One-sample MR was conducted using individual-level data (396,495 individuals) from the UK Biobank and the Nord-Trøndelag Health Study (HUNT), Norway. Two-sample MR was performed with summary-level data (356,926 individuals) from published BMI and psoriasis genome-wide association studies (GWASs). The one-sample and two-sample MR estimates were meta-analysed using a fixed-effect model. To test for a potential reverse causal effect, MR analysis with genetic instruments comprising variants from recent genome-wide analyses for psoriasis were used to test whether genetic risk for this skin disease has a causal effect on BMI. Published observational data showed an association of higher BMI with psoriasis. A mean difference in BMI of 1.26 kg/m2 (95% CI 1.02-1.51) between psoriasis cases and controls was observed in adults, while a 1.55 kg/m2 mean difference (95% CI 1.13-1.98) was observed in children. The observational association was confirmed in UK Biobank and HUNT data sets. Overall, a 1 kg/m2 increase in BMI was associated with 4% higher odds of psoriasis (meta-analysis odds ratio [OR] = 1.04; 95% CI 1.03-1.04; P = 1.73 × 10(-60)). MR analyses provided evidence that higher BMI causally increases the odds of psoriasis (by 9% per 1 unit increase in BMI; OR = 1.09 (1.06-1.12) per 1 kg/m2; P = 4.67 × 10(-9)). In contrast, MR estimates gave little support to a possible causal effect of psoriasis genetic risk on BMI (0.004 kg/m2 change in BMI per doubling odds of psoriasis (-0.003 to 0.011). Limitations of our study include possible misreporting of psoriasis by patients, as well as potential misdiagnosis by clinicians. In addition, there is also limited ethnic variation in the cohorts studied. Our study, using genetic variants as instrumental variables for BMI, provides evidence that higher BMI leads to a higher risk of psoriasis. This supports the prioritization of therapies and lifestyle interventions aimed at controlling weight for the prevention or treatment of this common skin disease. Mechanistic studies are required to improve understanding of this relationship.