A large-scale in vivo screen to investigate the roles of human genes in Drosophila melanogaster.

Understanding the signaling pathways in which genes participate is essential for discovering the etiology of diseases in humans. The model organism, Drosophila melanogaster, has been crucial in understanding the signaling pathways in humans, given the evolutionary conservation of a significant number of genes between the two species. Genetic screens using Drosophila are a useful way of testing large number of genes to study their function and roles within signaling pathways. We conducted a large-scale genetic screen to identify which human genes cause an alteration in the morphology of the Drosophila eye. The GMR-Gal4 was employed to activate a single UAS-human gene in the eye tissue. In total, we screened 802 UAS-human gene stocks, corresponding to 787 human protein-coding genes, for the ability to influence eye development. We found that overexpression of 64 human genes were capable of disrupting eye development, as determined by phenotypic changes in eye texture, size, shape, bristle morphology, and ommatidia organization. Subsequent analysis revealed that the fly genome encodes proteins that are homologous to a majority of the 64 human genes, raising the possibility that overexpression of these transgenes altered eye development by altering the activity of evolutionarily conserved developmental signaling pathways. Consistent with this hypothesis, a secondary screen demonstrated that overexpression of fly homologs produced phenotypes that mimicked those produced by overexpression of the human gene. Our screening has identified 64 human genes capable of inducing phenotypes in the fly, offering a foundation for ongoing research aimed at understanding functionally conserved pathways across species.

No evidence that ACE2 or TMPRSS2 drive population disparity in COVID risks.

Early in the SARS-CoV2 pandemic, in this journal, Hou et al. (BMC Med 18:216, 2020) interpreted public genotype data, run through functional prediction tools, as suggesting that members of particular human populations carry potentially COVID-risk-increasing variants in genes ACE2 and TMPRSS2 far more often than do members of other populations. Beyond resting on predictions rather than clinical outcomes, and focusing on variants too rare to typify population members even jointly, their claim mistook a well known artifact (that large samples reveal more of a population's variants than do small samples) as if showing real and congruent population differences for the two genes, rather than lopsided population sampling in their shared source data. We explain that artifact, and contrast it with empirical findings, now ample, that other loci shape personal COVID risks far more significantly than do ACE2 and TMPRSS2-and that variation in ACE2 and TMPRSS2 per se unlikely exacerbates any net population disparity in the effects of such more risk-informative loci.

A standardised nomenclature for long non-coding RNAs.

The HUGO Gene Nomenclature Committee (HGNC) is the sole group with the authority to approve symbols for human genes, including long non-coding RNA (lncRNA) genes. Use of approved symbols ensures that publications and biomedical databases are easily searchable and reduces the risks of confusion that can be caused by using the same symbol to refer to different genes or using many different symbols for the same gene. Here, we describe how the HGNC names lncRNA genes and review the nomenclature of the seven lncRNA genes most mentioned in the scientific literature.

Overcoming constraints on the detection of recessive selection in human genes from population frequency data.

The identification of genes that evolve under recessive natural selection is a long-standing goal of population genetics research that has important applications to the discovery of genes associated with disease. We found that commonly used methods to evaluate selective constraint at the gene level are highly sensitive to genes under heterozygous selection but ubiquitously fail to detect recessively evolving genes. Additionally, more sophisticated likelihood-based methods designed to detect recessivity similarly lack power for a human gene of realistic length from current population sample sizes. However, extensive simulations suggested that recessive genes may be detectable in aggregate. Here, we offer a method informed by population genetics simulations designed to detect recessive purifying selection in gene sets. Applying this to empirical gene sets produced significant enrichments for strong recessive selection in genes previously inferred to be under recessive selection in a consanguineous cohort and in genes involved in autosomal recessive monogenic disorders.

Beyond standard pipeline and p < 0.05 in pathway enrichment analyses.

A standard pathway/gene-set enrichment analysis, the over-representation analysis, is based on four values: the size of two gene-sets, size of their overlap, and size of the gene universe from which the gene-sets are chosen. The standard result of such an analysis is based on the p-value of a statistical test. We supplement this standard pipeline by six cautions: (1) any p-value threshold to distinguish enriched gene-sets from not-enriched ones is to certain degree arbitrary; (2) genes in a gene-set may be correlated, which potentially overcount the gene-set size; (3) any attempt to impose multiple testing correction will increase the false negative rate; (4) gene-sets in a gene-set database may be correlated, potentially overcount the factor for multiple testing correction; (5) the discrete nature of the data make it possible that a minimum change in counts may lead to a quantum change in the p-value threshold-based conclusion; (6) the two gene-sets may not be chosen from the universe of all human genes, but in fact from a subset of that universe, or even two different subsets of all genes. Careful reconsideration of these issues can have an impact on an enrichment analysis conclusion. Part of our cautions mirror the call from statistician that reaching conclusion from data is not a simple matter of p-value smaller than 0.05, but a thoughtful process with due diligences.

Genetics, adaptation to environmental changes and archaic admixture in the pathogenesis of diabetes mellitus in Indigenous Australians.

Indigenous Australians are particularly affected by type 2 diabetes mellitus (T2D) due to both their genetic susceptibility and a range of environmental and lifestyle risk factors. Recent genetic studies link predisposition to some diseases, including T2D, to alleles acquired from archaic hominins, such as Neanderthals and Denisovans, which persist in the genomes of modern humans today. Indo-Pacific human populations, including Indigenous Australians, remain extremely underrepresented in genomic research with a paucity of data examining the impact of Denisovan or Neanderthal lineages on human phenotypes in Oceania. The few genetic studies undertaken emphasize the uniqueness and antiquity of Indigenous Australian genomes, with possibly the largest proportion of Denisovan ancestry of any population in the world. In this review, we focus on the potential contributions of ancient genes/pathways to modern human phenotypes, while also highlighting the evolutionary roles of genetic adaptation to dietary and environmental changes associated with an adopted Western lifestyle. We discuss the role of genetic and epigenetic factors in the pathogenesis of T2D in understudied Indigenous Australians, including the potential impact of archaic gene lineages on this disease. Finally, we propose that greater understanding of the underlying genetic predisposition may contribute to the clinical efficacy of diabetes management in Indigenous Australians. We suggest that improved identification of T2D risk variants in Oceania is needed. Such studies promise to clarify how genetic and phenotypic differences vary between populations and, crucially, provide novel targets for personalised medical therapies in currently marginalized groups.

Human protein-coding genes and gene feature statistics in 2019.

OBJECTIVE: A well-known limit of genome browsers is that the large amount of genome and gene data is not organized in the form of a searchable database, hampering full management of numerical data and free calculations. Due to the continuous increase of data deposited in genomic repositories, their content revision and analysis is recommended. Using GeneBase, a software with a graphical interface able to import and elaborate National Center for Biotechnology Information (NCBI) Gene database entries, we provide tabulated spreadsheets updated to 2019 about human nuclear protein-coding gene data set ready to be used for any type of analysis about genes, transcripts and gene organization. RESULTS: Comparison with previous reports reveals substantial change in the number of known nuclear protein-coding genes (now 19,116), the protein-coding non-redundant transcriptome space [now 59,281,518 base pair (bp), 10.1% increase], the number of exons (now 562,164, 36.2% increase) due to a relevant increase of the RNA isoforms recorded. Other parameters such as gene, exon or intron mean and extreme length appear to have reached a stability that is unlikely to be substantially modified by human genome data updates, at least regarding protein-coding genes. Finally, we confirm that there are no human introns shorter than 30 bp.

Analysis of RAS mutation in thyroid nodular hyperplasia and follicular neoplasm in a Korean population.

BACKGROUND: To investigate the difference in frequency of RAS mutations between nodular hyperplasia (NH), follicular thyroid adenomas (FTAs) and follicular thyroid carcinomas (FTCs) in a Korean population. METHODS: RAS mutations in 50 NH, 57 FTAs and 39 FTCs between January 2002 and May 2015 were analysed by pyrosequencing. RESULTS: Nine nodules of 50 NHs (18%), 18 nodules of 39 FTCs (46.2%) and 19 nodules of 57 FTAs (33.3%) harboured RAS mutations. Three FTCs and three FTAs showed two point mutations simultaneously. N-RAS codon 61 (n = 6 of 9, 66.7%) and H-RAS codon 61 (n = 3 of 9, 33.3%) were found in NHs. K-RAS codons 12-13, K-RAS codon 61, N-RAS codons 12-13 and H-RAS codons 12-13 were not found in NHs. N-RAS codon 61 (n = 7 of 21, 33.3%), K-RAS codons 12-13 (n = 6 of 21, 28.6%), H-RAS codon 61 (n = 4 of 21, 19.0%), K-RAS codon 61 (n = 3 of 21, 14.3%) and N-RAS codons 12-13 (n = 1 of 21, 4.7%) were found in FTCs, and N-RAS codon 61 (n = 10 of 22, 45.5%), K-RAS codons 12-13 (n = 5 of 22, 22.7%), H-RAS codon 61 (n = 5 of 22, 22.7%), K-RAS codon 61 (n = 1 of 22, 4.5%) and N-RAS codons 12-13 (n = 1 of 22, 4.5%) were observed in FTAs. CONCLUSIONS: The frequencies of RAS mutations among our Korean population were 18% in NHs, 46.2% in FTC and 33.3% in FTAs. N-RAS codon 61 was the most frequent mutation in NHs, FTCs and FTAs, and the frequency was not significantly different among the three groups. K-RAS codons 12-13 were the second most commonly involved site in FTCs and FTAs, whereas no mutation was detected at this site in NHs.

The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses.

GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It automatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. VarElect's capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. © 2016 by John Wiley & Sons, Inc.