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Purpose Large-scale, population-based biobanks integrating health records and genomic profiles may provide a platform to identify individuals with disease-predisposing genetic variants. Here, we recall probands carrying familial hypercholesterolemia (FH)-associated variants, perform cascade screening of family members, and describe health outcomes affected by such a strategy. Methods The Estonian Biobank of Estonian Genome Center, University of Tartu, comprises 52,274 individuals. Among 4776 participants with exome or genome sequences, we identified 27 individuals who carried FH-associated variants in the LDLR , APOB , or PCSK9 genes. Cascade screening of 64 family members identified an additional 20 carriers of FH-associated variants. Results Via genetic counseling and clinical management of carriers, we were able to reclassify 51% of the study participants from having previously established nonspecific hypercholesterolemia to having FH and identify 32% who were completely unaware of harboring a high-risk disease-associated genetic variant. Imaging-based risk stratification targeted 86% of the variant carriers for statin treatment recommendations. Conclusion Genotype-guided recall of probands and subsequent cascade screening for familial hypercholesterolemia is feasible within a population-based biobank and may facilitate more appropriate clinical management.

The field of cytogenetics has already entered the molecular era and a rapid expansion of its contribution is seen in genomic disease management. Among the evolving advanced molecular techniques, with an impeccable balance of high specificity, sensitivity and assay rapidity, fluorescence in situ hybridization has made its home in routine clinical laboratory. Today, its clinical application is vivid in every phase of disease management of a number of malignancies. The rapid growth in the knowledge of specific associations between distinct chromosomal abnormalities and different types of cancers will necessitate simultaneous detection of multiple abnormalities using multicolor/multiplex fluorescence in situ hybridization tests more often in the near future. Also, as the human genome sequence is ascertained, genome-wide screening with microarray technology will gain eminence in the clinical scenario, yield better solutions and bring the concept of personalized medicine in cancer closer to reality than ever before.

Background Balancing the yield, quality and resistance to disease is a daunting challenge in crop breeding due to the negative relationship among these traits. Large-scale genomic landscape analysis of germplasm resources is considered to be an efficient approach to dissect the genetic basis of the complex traits. Central China is one of the main regions where the japonica rice is produced. However, dozens of high-yield rice varieties in this region still exist with low quality or susceptibility to blast disease, severely limiting their application in rice production. Results Here, we re-sequence 200 japonica rice varieties grown in central China over the past 30 years and analyze the genetic structure of these cultivars using 2.4 million polymorphic SNP markers. Genome-wide association mapping and selection scans indicate that strong selection for high-yield and taste quality associated with low-amylose content may have led to the loss of resistance to the rice blast fungus Magnaporthe oryzae . By extensive bioinformatic analyses of yield components, resistance to rice blast, and taste quality, we identify several superior alleles for these traits in the population. Based on this information, we successfully introduce excellent taste quality and blast-resistant alleles into the background of two high-yield cultivars and develop two elite lines, XY99 and JXY1, with excellent taste, high yield, and broad-spectrum of blast resistance. Conclusions This is the first large-scale genomic landscape analysis of japonica rice varieties grown in central China and we demonstrate a balancing of multiple agronomic traits by genomic-based strategy.

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

Background More than 2 million SARS-CoV-2 genome sequences have been generated and shared since the start of the COVID-19 pandemic and constitute a vital information source that informs outbreak control, disease surveillance, and public health policy. The Pango dynamic nomenclature is a popular system for classifying and naming genetically-distinct lineages of SARS-CoV-2, including variants of concern, and is based on the analysis of complete or near-complete virus genomes. However, for several reasons, nucleotide sequences may be generated that cover only the spike gene of SARS-CoV-2. It is therefore important to understand how much information about Pango lineage status is contained in spike-only nucleotide sequences. Here we explore how Pango lineages might be reliably designated and assigned to spike-only nucleotide sequences. We survey the genetic diversity of such sequences, and investigate the information they contain about Pango lineage status. Results Although many lineages, including the main variants of concern, can be identified clearly using spike-only sequences, some spike-only sequences are shared among tens or hundreds of Pango lineages. To facilitate the classification of SARS-CoV-2 lineages using subgenomic sequences we introduce the notion of designating such sequences to a “lineage set”, which represents the range of Pango lineages that are consistent with the observed mutations in a given spike sequence. Conclusions We find that many lineages, including the main variants-of-concern, can be reliably identified by spike alone and we define lineage-sets to represent the lineage precision that can be achieved using spike-only nucleotide sequences. These data provide a foundation for the development of software tools that can assign newly-generated spike nucleotide sequences to Pango lineage sets.

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele. Meta-analysis of genome-wide association studies on Alzheimer’s disease and related dementias identifies new loci and enables generation of a new genetic risk score associated with the risk of future Alzheimer’s disease and dementia.

Background As Nigeria has the sixth-highest population in the world and a significant amount of inbound and outbound travel, the characterization of SARS-CoV-2 genomic diversity across the country is critical for understanding novel pandemic dynamics. We describe the genomic diversity of SARS-CoV-2 in Nigeria throughout the COVID-19 pandemic and examine the coverage of Nigeria's genomic surveillance system. Methods Genome sequences and sample metadata were downloaded from the GISAID repository. A beta regression was used to test for a relationship between fully resolved nucleotide proportion over time, as a proxy for data quality. Sample and sequencing source were compared to assess geographic coverage. Results A total of 7759 COVID-19 sequences collected from February 2020 to March 2023 were included. The majority were collected in 2021 (76.6%) and South West (43%). Eleven states (30%) reported 10 or fewer SARS-CoV-2 genomes across the entire period. The genome sequences submitted to GISAID from Nigeria were of high quality with very few unresolved nucleotides. Waves 4 and 5, predominantly Omicron lineages, show higher diversity around position 23 kb than the other waves. Overall, the Nigeria Centre for Disease Control (NCDC) and state-run hospitals were the largest contributors to the sample collection efforts during this study period. However, the collection efforts shifted over time from NCDC in waves 1–3 to regional hospitals and other healthcare facilities in waves 4–5, although this pattern varied by geopolitical zone (GPZ). Sequencing efforts also shifted from research laboratories during the first waves to NCDC during waves 4 and 5. Conclusions The findings suggest the need for a coordinated sequencing strategy and standardized protocols to improve genomic surveillance during future outbreaks of existing and novel pathogens. A network of sequencing laboratories that includes at least one in each GPZ, linked to and coordinated by the national reference laboratory at NCDC might provide more balanced coverage for future pandemics and pathogen surveillance.

Background Fragile sites are the chromosomal regions that are susceptible to breakage, and their frequency varies among the human population. Based on the frequency of fragile site induction, they are categorized as common and rare fragile sites. Common fragile sites are sensitive to replication stress and often rearranged in cancer. Rare fragile sites are the archetypal trinucleotide repeats. Fragile sites are known to be involved in chromosomal rearrangements in tumors. Human miRNA genes are also present at fragile sites. A better understanding of genes and miRNAs lying in the fragile site regions and their association with disease progression is required. Result HumCFS is a manually curated database of human chromosomal fragile sites. HumCFS provides useful information on fragile sites such as coordinates on the chromosome, cytoband, their chemical inducers and frequency of fragile site (rare or common), genes and miRNAs lying in fragile sites. Protein coding genes in the fragile sites were identified by mapping the coordinates of fragile sites with human genome Ensembl (GRCh38/hg38). Genes present in fragile sites were further mapped to DisGenNET database, to understand their possible link with human diseases. Human miRNAs from miRBase was also mapped on fragile site coordinates. In brief, HumCFS provides useful information about 125 human chromosomal fragile sites and their association with 4921 human protein-coding genes and 917 human miRNA’s. Conclusion User-friendly web-interface of HumCFS and hyper-linking with other resources will help researchers to search for genes, miRNAs efficiently and to intersect the relationship among them. For easy data retrieval and analysis, we have integrated standard web-based tools, such as JBrowse, BLAST etc. Also, the user can download the data in various file formats such as text files, gff3 files and Bed-format files which can be used on UCSC browser. Database URL: http://webs.iiitd.edu.in/raghava/humcfs/

Background Sorghum anthracnose is a major disease that hampers the productivity of the crop globally. The disease is caused by the hemibiotrophic fungal pathogen Colletotrichum sublineola . The identification of anthracnose-resistant sorghum genotypes, defining resistance loci and the underlying genes, and their introgression into adapted cultivars are crucial for enhancing productivity. In this study, we conducted field experiments on 358 diverse accessions of Ethiopian sorghum. Quantitative resistance to anthracnose was evaluated at locations characterized by a heavy natural infestation that is suitable for disease resistance screening. Results The field-based screening identified 53 accessions that were resistant across locations, while 213 accessions exhibited variable resistance against local pathotypes. Genome-wide association analysis (GWAS) was performed using disease response scores on 329 accessions and 83,861 single nucleotide polymorphisms (SNPs) generated through genotyping-by-sequencing (GBS). We identified 38 loci significantly associated with anthracnose resistance. Interestingly, a subset of these loci harbor genes encoding receptor-like kinases (RLK), nucleotide-binding leucine-rich repeats (NLRs), stress-induced antifungal tyrosine kinase that have been previously implicated in disease resistance. A SNP on chromosome 4 (S04_66140995) and two SNPs on chromosome 2 (S02_75784037, S02_2031925), localized with-in the coding region of genes that encode a putative stress-induced antifungal kinase, an F-Box protein, and Xa21-binding RLK that were strongly associated with anthracnose resistance. We also identified highly significant associations between anthracnose resistance and three SNPs linked to genes (Sobic.002G058400, Sobic.008G156600, Sobic.005G033400) encoding an orthologue of the widely known NLR protein (RPM1), Leucine Rich Repeat family protein, and Heavy Metal Associated domain-containing protein, respectively. Other SNPs linked to predicted immune response genes were also significantly associated with anthracnose resistance. Conclusions The sorghum germplasm collections used in the present study are genetically diverse. They harbor potentially useful, yet undiscovered, alleles for anthracnose resistance. This is supported by the identification of novel loci that are enriched for disease resistance regulators such as NLRs, LRKs, Xa21-binding LRK, and antifungal proteins. The genotypic data available for these accessions offer a valuable resource for sorghum breeders to effectively improve the crop. The genomic regions and candidate genes identified can be used to design markers for molecular breeding of sorghum diseases resistance.

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .