A brief history of human disease genetics

Claussnitzer, Melina; Cho, Judy H.; Collins, Rory; Cox, Nancy J.; Dermitzakis, Emmanouil T.; Hurles, Matthew E.; Kathiresan, Sekar; Kenny, Eimear E.; Lindgren, Cecilia M.; MacArthur, Daniel G.; North, Klaus; Plon, Sharon E.; Rehm, Heidi L.; Risch, Neil; Rotimi, Charles N.; Shendure, Jay; Soranzo, Nicole; McCarthy, Mark

doi:10.1038/s41586-019-1879-7

Cited by 550 publications

(476 citation statements)

References 184 publications

(208 reference statements)

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“…Although GRCh38 reference is of very high quality 6 , it contains gaps at repetitive and structurally diverse regions and it is likely biased towards individuals of European ancestry 7,8 . The use of NRSs from diverse ancestries can improve variant discovery and genotyping 7,9 , help identifying causal variants 10,11 , and ultimately facilitate disease research and clinical applications 12 . An early estimate quantified that the entire human pangenome would contain an additional Mb with respect to the reference sequence 13 .…”

Section: Introductionmentioning

confidence: 99%

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Manni

Zdobnov

2020

Preprint

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Human pan-genome studies offer the opportunity to identify human non-reference sequences (NRSs) which are, by definition, not represented in the reference human genome (GRCh38). NRSs serve as useful catalogues of genetic variation for population and disease studies and while the majority consists of repetitive elements, a substantial fraction is made of non-repetitive, non-reference (NRNR) sequences. The presence of non-human sequences in these catalogues can inflate the number of "novel" human sequences, overestimate the genetic differentiation among populations, and jeopardize subsequent analyses that rely on these resources. We uncovered almost 2,000 contaminant sequences of microbial origin in NRNR sequences from recent human pan-genome studies. The contaminant contigs (3,501,302 bp) harbour genes totalling 4,720 predicted proteins (>40 aa). The major sources of contamination are related to Rhyzobiales, Burkholderiales, Pseudomonadales and Lactobacillales, which may have been associated with the original samples or introduced later during sequencing experiments. We additionally observed that the majority of human novel protein-coding genes described in one of the studies entirely overlap repetitive regions and are likely to be false positive predictions.We report here the list of contaminant sequences in three recent human pan-genome catalogues and discuss strategies to increase decontamination efficacy for current and future pan-genome studies.

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Section: Introductionmentioning

confidence: 99%

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Manni

Zdobnov

2020

Preprint

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“…Hundreds of genetic loci have been implicated in autoimmune and inflammatory diseases, but the mechanisms by which these effect disease remain largely unknown 1 . An important first step in uncovering these mechanisms is to reduce associated haplotypes down to specific causal variants, whose biological effects mediate disease risk, but statistical attempts to do this have been frustrated by strong linkage disequilibrium (LD), resulting in only a minority of loci being resolved 2–4 .…”

Section: Introductionmentioning

confidence: 99%

“…This leaves the majority of GWAS loci either unresolved or unresolvable, and the ambition of identifying disease mechanisms largely unrealised 8 . To compound this challenge, the specific gene(s) that are affected by disease-associated variants have not been confirmed for most loci 1 . Many associated haplotypes, for example, contain multiple genes with little or no evidence for any one being causally involved, while other associations are entirely located within intergenic regions (or “gene deserts”) and are often reported to lack candidate genes.…”

Section: Introductionmentioning

confidence: 99%

Resolving mechanisms of immune-mediated disease in primary CD4 T cells

Bourges

Burren

et al. 2020

Preprint

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Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by linkage disequilibrium. To determine whether causal variants could be identified via their functional effects, we adapted a massively-parallel reporter assay for use in primary CD4 T-cells, key effectors of many immune-mediated diseases. Using the results to guide further study, we provide a generalisable framework for resolving disease mechanisms from non-coding associations -illustrated by a locus linked to 6 immune-mediated diseases, where the lead functional variant causally disrupts a super-enhancer within an NF-κB-driven regulatory circuit, triggering unrestrained T-cell activation. Keywords MPRA, primary T cells, TNFAIP3, super-enhancer, GWAS 7Shan, X. et al. Deficiency of PTEN in Jurkat T cells causes constitutive localization of Itk to the plasma membrane and hyperresponsiveness to CD3 stimulation. Mol Cell Biol 20, 6945-57 (2000).

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“…Modern definition of Mendelian disease is complex, with variable levels of monogenicity, penetrance (Claussnitzer et al, 2020), epistasis, gene x environment interactions and indeed pleiotropy observed (Chakravorty & Hegde, 2017). In MendelVar, we follow the working definitions from Chakravorty & Hegde (2017) and Hansen et al (2019) with broadly defined Mendelian genes to include genes causal for highly penetrant monogenic and oligogenic diseases, including diseases involving cell mosaicism, recurrent de novo structural variants causing mostly developmental diseases, such as Smith-Magenis syndrome and some germline cancer susceptibility genes such as BRCA1/BRCA2.…”

Section: Introductionmentioning

confidence: 99%

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

Sobczyk

Gaunt

Paternoster

2020

Preprint

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Gene prioritisation at GWAS loci necessities careful assembly and examination of different types of molecular evidence to arrive at a set of plausible candidates. In many human traits, common small-effect mutations may subtly dysregulate the function of the very same genes which are impacted by rare, large-effect mutations causing Mendelian disease of similar phenotype. However, information on gene-Mendelian disease associations, rare pathogenic mutations driving the disease, and the disease phenotype ontology is dispersed across many data sources and does not integrate easily with enrichment analysis.MendelVar is a new webserver facilitating transfer of knowledge from Mendelian disease research into interpretation of genetic associations from GWAS of complex traits.MendelVar allows querying of pre-defined or LD-determined genomic intervals against a comprehensive integrated database to find overlap with genes linked to Mendelian disease. Next, MendelVar looks for enrichment of any Human Phenotype Ontology, DiseaseOntology and other ontology/pathway terms associated with identified Mendelian genes. In addition, MendelVar provides a list of all overlapping pathogenic and likely pathogenic variants for Mendelian disease sourced from ClinVar. Inclusion of information obtained from MendelVar in post-GWAS gene annotation pipelinescan strengthen the case for causal importance of some genes. Moreover, as genes with Mendelian disease evidence may make for more successful drug targets, this may be particularly useful in drug discovery pipelines. Taking GWAS summary statistics for malepattern baldness, intelligence and atopic dermatitis, we demonstrate the use of MendelVar in prioritizing candidate genes at these loci which are linked to relevant enriched ontology terms. MendelVar is freely available at https

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A brief history of human disease genetics

Cited by 550 publications

References 184 publications

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Microbial contaminants cataloged as novel human sequences in recent human pan-genomes

Resolving mechanisms of immune-mediated disease in primary CD4 T cells

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

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