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Mosaic loss of chromosome Y (LOY) in leukocytes is the most common form of clonal mosaicism, caused by dysregulation in cell-cycle and DNA damage response pathways. Previous genetic studies have focussed on identifying common variants associated with LOY, which we now extend to rarer, protein-coding variation using exome sequences from 82,277 male UK Biobank participants. We find that loss of function of two genes— CHEK2 and GIGYF1 —reach exome-wide significance. Rare alleles in GIGYF1 have not previously been implicated in any complex trait, but here loss-of-function carriers exhibit six-fold higher susceptibility to LOY (OR = 5.99 [3.04–11.81], p = 1.3 × 10 −10 ). These same alleles are also associated with adverse metabolic health, including higher susceptibility to Type 2 Diabetes (OR = 6.10 [3.51–10.61], p  = 1.8 × 10 −12 ), 4 kg higher fat mass ( p  = 1.3 × 10 −4 ), 2.32 nmol/L lower serum IGF1 levels ( p  = 1.5 × 10 −4 ) and 4.5 kg lower handgrip strength ( p  = 4.7 × 10 −7 ) consistent with proposed GIGYF1 enhancement of insulin and IGF-1 receptor signalling. These associations are mirrored by a common variant nearby associated with the expression of GIGYF1 . Our observations highlight a potential direct connection between clonal mosaicism and metabolic health. Mosaic loss of chromosome Y (LOY) is a common form of clonal mosaicism in leukocytes. Here, the authors extend genetic association analyses to rare variation using exome-sequence data from 82,277 males, finding that loss-of-function alleles in GIGYF1 are associated with six-fold higher susceptibility to both LOY and Type 2 Diabetes.

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets 1 – 4 . Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype–protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort 5 . We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene–protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2 , encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4 , identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2- CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery. A set of three papers in Nature reports a new proteomics resource from the UK Biobank and initial analysis of common and rare genetic variant associations with plasma protein levels.

Premature ovarian insufficiency (POI) affects 1% of women and is a leading cause of infertility. It is often considered to be a monogenic disorder, with pathogenic variants in ~100 genes described in the literature. We sought to systematically evaluate the penetrance of variants in these genes using exome sequence data in 104,733 women from the UK Biobank, 2,231 (1.14%) of whom reported at natural menopause under the age of 40 years. We found limited evidence to support any previously reported autosomal dominant effect. For nearly all heterozygous effects on previously reported POI genes, we ruled out even modest penetrance, with 99.9% (13,699 out of 13,708) of all protein-truncating variants found in reproductively healthy women. We found evidence of haploinsufficiency effects in several genes, including TWNK (1.54 years earlier menopause, P  = 1.59 × 10 −6 ) and SOHLH2 (3.48 years earlier menopause, P  = 1.03 × 10 −4 ). Collectively, our results suggest that, for the vast majority of women, POI is not caused by autosomal dominant variants either in genes previously reported or currently evaluated in clinical diagnostic panels. Our findings, plus previous studies, suggest that most POI cases are likely oligogenic or polygenic in nature, which has important implications for future clinical genetic studies, and genetic counseling for families affected by POI. Using exome sequencing data from 104,733 postmenopausal women in the UK Biobank, an analysis of 105 gene variants previously reported as associated with premature ovarian insufficiency reveals that most cases are not monogenic.

Telomeres protect chromosome ends from damage and their length is linked with human disease and aging. We developed a joint telomere length metric, combining quantitative PCR and whole-genome sequencing measurements from 462,666 UK Biobank participants. This metric increased SNP heritability, suggesting that it better captures genetic regulation of telomere length. Exome-wide rare-variant and gene-level collapsing association studies identified 64 variants and 30 genes significantly associated with telomere length, including allelic series in ACD and RTEL1 . Notably, 16% of these genes are known drivers of clonal hematopoiesis—an age-related somatic mosaicism associated with myeloid cancers and several nonmalignant diseases. Somatic variant analyses revealed gene-specific associations with telomere length, including lengthened telomeres in individuals with large SRSF2 -mutant clones, compared with shortened telomeres in individuals with clonal expansions driven by other genes. Collectively, our findings demonstrate the impact of rare variants on telomere length, with larger effects observed among genes also associated with clonal hematopoiesis. Genome-wide association analysis of an improved telomere length score, calculated from quantitative PCR and whole-genome sequencing measurements in 462,666 individuals in the UK Biobank, identifies novel genes and variants underlying this trait.

Human genetic studies of common variants have provided substantial insight into the biological mechanisms that govern ovarian ageing 1 . Here we report analyses of rare protein-coding variants in 106,973 women from the UK Biobank study, implicating genes with effects around five times larger than previously found for common variants ( ETAA1 , ZNF518A , PNPLA8 , PALB2 and SAMHD1 ). The SAMHD1 association reinforces the link between ovarian ageing and cancer susceptibility 1 , with damaging germline variants being associated with extended reproductive lifespan and increased all-cause cancer risk in both men and women. Protein-truncating variants in ZNF518A are associated with shorter reproductive lifespan—that is, earlier age at menopause (by 5.61 years) and later age at menarche (by 0.56 years). Finally, using 8,089 sequenced trios from the 100,000 Genomes Project (100kGP), we observe that common genetic variants associated with earlier ovarian ageing associate with an increased rate of maternally derived de novo mutations. Although we were unable to replicate the finding in independent samples from the deCODE study, it is consistent with the expected role of DNA damage response genes in maintaining the genetic integrity of germ cells. This study provides evidence of genetic links between age of menopause and cancer risk. Analyses focusing on protein-truncating variants from 106,973 women from in the UK Biobank identify variants in genes that reinforce the link between reproductive lifespan in women and cancer risk in both sexes.

Hate Speech encompasses different forms of trolling, bullying, harassment, and threats directed against specific individuals or groups. This phenomena is mainly expressed on Social Networks. For sports players, Social Media is a means of communication with the widest part of their fans and a way to face different cyber-aggression forms. These virtual attacks can harm players, distress them, cause them to feel bad for a long time, or even escalate into physical violence. To date, athletes were not observed as a vulnerable group, so they were not a subject of automatic Hate Speech detection and recognition from content published on Social Media. This paper explores whether a model trained on the dataset from one Social Media and not related to any specific domain can be efficient for the Hate Speech binary classification of test sets regarding the sports domain. The experiments deal with Hate Speech detection in Serbian. BiLSTM deep neural network was learned with different parameters, and the results showed high Precision of detecting Hate Speech in sports domain (96% and 97%) and pretty low Recall.

Background Circulating insulin and insulin-like growth factor-1 (IGF-1) concentrations are positively correlated with adiposity. However, the causal effects of insulin and IGF-1 on adiposity are unclear. Methods We performed two-sample Mendelian randomization analyses to estimate the likely causal effects of fasting insulin and IGF-1 on relative childhood adiposity and adult body mass index (BMI). To improve accuracy and biological interpretation, we applied Steiger filtering (to avoid reverse causality) and ‘biological effect’ filtering of fasting insulin and IGF-1 associated variants. Results Fasting insulin-increasing alleles (35 variants also associated with higher fasting glucose, indicative of insulin resistance) were associated with lower relative childhood adiposity ( P  = 3.8 × 10 −3 ) and lower adult BMI ( P  = 1.4 × 10 −5 ). IGF-1-increasing alleles also associated with taller childhood height (351 variants indicative of greater IGF-1 bioaction) showed no association with relative childhood adiposity ( P  = 0.077) or adult BMI ( P  = 0.562). Conversely, IGF-1-increasing alleles also associated with shorter childhood height (306 variants indicative of IGF-1 resistance) were associated with lower relative childhood adiposity ( P  = 6.7 × 10 −3 ), but effects on adult BMI were inconclusive. Conclusions Genetic causal modelling indicates negative effects of insulin resistance on childhood and adult adiposity, and negative effects of IGF-1 resistance on childhood adiposity. Our findings demonstrate the need to distinguish between bioaction and resistance when modelling variants associated with biomarker concentrations.

The rapid development of artificial intelligence, especially AI assistants, is leading to new forms of plagiarism that are difficult to detect using existing methods. Paraphrasing tools make this problem even more complex and challenging especially in minor languages with inadequate resources and tools. This study explores strategies to help detect plagiarism generated by ChatGPT 4.0 and altered by paraphrasing tools. We propose two new datasets consisting of abstracts of doctoral theses in English and Serbian. Both datasets were subjected to ChatGPT paraphrasing, which allowed us to form two classes of texts: human-written and AI-generated, i.e., AI-paraphrased. We then comprehensively compare 19 widely used classification algorithms based on two feature sets: word unigrams and character multigrams. In addition, we compare these to the results of a commercially available pre-trained ChatGPT content detector, ZeroGPT. The results on the English corpus turn out to be very accurate, achieving an accuracy of 95% or more. In contrast, the results on the Serbian corpus were less accurate, achieving an accuracy of just over 85%. Syntax analysis of the training datasets has shown that in Serbian GPT-paraphrased texts, 33.2% of sentences remain the same, and they are found in 63% of documents. GPT-paraphrased English texts showed that 3.2% of sentences remain the same, and they are found in 16% of documents. Syntax analysis of the test datasets has shown that the change of the model temperature influences syntactic features (average number of words and sentences) in English texts and slightly or not in Serbian texts. We attribute all these differences to GPT’s lower paraphrasing ability in minor languages such as Serbian. Presented findings underscore the necessity for making persistent effort in developing tools made for detecting AI-paraphrased texts in academic and professional settings, particularly for minor languages with limited NLP resources, to preserve content integrity and authenticity.

Purpose A hybrid approach is presented, which combines linguistic and statistical information to semi-automatically extract multiword term candidates from texts. Design/methodology/approach The method is designed to be domain and language independent, focusing on languages with rich morphology. Here, it is used for extracting multiword terms from texts in Serbian, belonging to the agricultural engineering domain, as a use case. Predefined syntactic structures were used for multiword terms. For each structure, a finite state transducer was developed, which recognizes text sequences having that structure and outputs the sequence in a normalized form, so that different inflectional forms of the same multiword term can be counted properly. Term candidates were further filtered by their frequencies and evaluated by two domain experts. Findings By using language resources, such as electronic dictionaries and grammars, 928 multiword terms were extracted out of 1,523 multiword terms that were recognized as candidates from a corpus having 42,260 different simple word forms; 870 of these were new, not already contained in the existing electronic dictionary of compounds for Serbian, and they were used to enrich the dictionary. Originality/value The paper presents methodology that can significantly contribute to the development of terminology lexicons in different areas. In this particular use case, some important agricultural engineering concepts were extracted from the text, but this approach could be used for other domains and languages as well.