Underweight and obese phenotypes can both pose health risks. But whereas obesity has been associated with a number of genetic variants, little is known about the genetic basis of underweight. A large-scale screen of data from 28 cytogenetic centres in Europe and North America now shows that being underweight is frequently associated with duplication of a short region on chromosome 16. Deletion of this same chromosomal region has previously been associated with obesity. The observed associated phenotypes are opposites, or mirrors, of those reported in carriers of deletions at this locus, and correlate with changes in transcript levels for genes within the duplication but not within the adjacent regions. The suggestion is that severe obesity and being underweight could have mirror etiologies, possibly through contrasting effects on energy balance. Both obesity and being underweight have been associated with increased mortality1,2. Underweight, defined as a body mass index (BMI) ≤ 18.5 kg per m2 in adults and ≤ −2 standard deviations from the mean in children, is the main sign of a series of heterogeneous clinical conditions including failure to thrive3,4,5, feeding and eating disorder and/or anorexia nervosa6,7. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported8,9. We previously showed that hemizygosity of a ∼600-kilobase (kb) region on the short arm of chromosome 16 causes a highly penetrant form of obesity that is often associated with hyperphagia and intellectual disabilities10. Here we show that the corresponding reciprocal duplication is associated with being underweight. We identified 138 duplication carriers (including 132 novel cases and 108 unrelated carriers) from individuals clinically referred for developmental or intellectual disabilities (DD/ID) or psychiatric disorders, or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight and BMI. Half of the boys younger than five years are underweight with a probable diagnosis of failure to thrive, whereas adult duplication carriers have an 8.3-fold increased risk of being clinically underweight. We observe a trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive eating behaviours and a significant reduction in head circumference. Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus. The phenotypes correlate with changes in transcript levels for genes mapping within the duplication but not in flanking regions. The reciprocal impact of these 16p11.2 copy-number variants indicates that severe obesity and being underweight could have mirror aetiologies, possibly through contrasting effects on energy balance.

Rare diseases affect more than 300 million people worldwide^1,2,3, yet timely and accurate diagnosis remains an urgent challenge^1,3,4,5. Patients often endure a prolonged ‘diagnostic odyssey’ exceeding 5 years, marked by repeated referrals, misdiagnoses and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burden^4,5. Here we present DeepRare—a multi-agent system for rare disease differential diagnosis decision support^6,7,8 powered by large language models, integrating more than 40 specialized tools and up-to-date knowledge sources. DeepRare processes heterogeneous clinical inputs, including free-text descriptions, structured human phenotype ontology terms and genetic testing results to generate ranked diagnostic hypotheses with transparent reasoning linked to verifiable medical evidence. Evaluated across nine datasets from literature, case reports and clinical centres across Asia, North America and Europe spanning 14 medical specialties, DeepRare demonstrates exceptional performance on 2,919 diseases. In human-phenotype-ontology-based tasks, it achieves an average Recall@1 of 57.18%, outperforming the next best method by 23.79%; in multi-modal tests, it reaches 69.1% compared with Exomiser’s 55.9% on 168 cases. Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability. Our work not only advances rare disease diagnosis but also demonstrates how the latest powerful large-language-model-driven agentic systems can reshape current clinical workflows.

It is particularly meaningful to investigate natural selection in African Americans (AfA) due to the high mortality their African ancestry has experienced in history. In this study, we examined 491,526 autosomal single nucleotide polymorphisms (SNPs) genotyped in 5210 individuals and conducted a genome-wide search for selection signals in 1890 AfA. Several genomic regions showing an excess of African or European ancestry, which were considered the footprints of selection since population admixture, were detected based on a commonly used approach. However, we also developed a new strategy to detect natural selection both pre- and post-admixture by reconstructing an ancestral African population (AAF) from inferred African components of ancestry in AfA and comparing it with indigenous African populations (IAF). Interestingly, many selection-candidate genes identified by the new approach were associated with AfA-specific high-risk diseases such as prostate cancer and hypertension, suggesting an important role these disease-related genes might have played in adapting to a new environment. CD36 and HBB, whose mutations confer a degree of protection against malaria, were also located in the highly differentiated regions between AAF and IAF. Further analysis showed that the frequencies of alleles protecting against malaria in AAF were lower than those in IAF, which is consistent with the relaxed selection pressure of malaria in the New World. There is no overlap between the top candidate genes detected by the two approaches, indicating the different environmental pressures AfA experienced pre- and post-population admixture. We suggest that the new approach is reasonably powerful and can also be applied to other admixed populations such as Latinos and Uyghurs.

Human height is a complex trait with a strong genetic basis. Recently, a significant association between rare copy number variations (CNVs) and short stature has been identified, and candidate genes in these rare CNVs are being explored. This study aims to evaluate the association between mutations in ARID1B gene and short stature, both the syndromic and non-syndromic form. Based on a case-control study of whole genome chromosome microarray analysis (CMA), three overlapping CNVs were identified in patients with developmental disorders who exhibited short stature. ARID1B, a causal gene for Coffin Siris syndrome, is the only gene encompassed by all three CNVs. A following retrospective genotype-phenotype analysis based on a literature review confirmed that short stature is a frequent feature in those Coffin-Siris syndrome patients with ARID1B mutations. Mutation screening of ARID1B coding regions was further conducted in a cohort of 48 non-syndromic short stature patients,andfour novel missense variants including two de novo mutations were found. These results suggest that haploinsufficient mutations of ARID1B are associated with syndromic short stature including Coffin-Siris syndrome and intellectual disability, while rare missense variants in ARID1B are associated with non-syndromic short stature. This study supports the notion that mutations in genes related to syndromic short stature may exert milder effect and contribute to short stature in the general population.