Genetic risk factors for autoimmune hepatitis: implications for phenotypic heterogeneity and biomarkers for drug response

AbstractAutoimmune hepatitis (AIH) is a rare chronic progressive liver disease with autoimmune features. It mainly affects middle-aged women. AIH is occasionally complicated with liver cirrhosis that worsens the prognosis. Genetic and environmental factors are involved in the pathogenesis of AIH. Genetic studies of other diseases have been revealing of pathogenesis and drug efficacy. In this review, we summarize the genetic risk factors for AIH, including human leukocyte antigen (HLA) and non-HLA genes. A genome-wide association study (GWAS) on European AIH revealed the strongest associations to be with single nucleotide variants (SNVs) in HLA. Predisposing alleles for AIH were DRB1*03:01 and DRB1*04:01 in Europeans; DRB1*04:04, DRB1*04:05, and DRB1*13:01 in Latin Americans; and DRB1*04:01 and DRB1*04:05 in Japanese. Other risk SNVs in non-HLA genes for AIH were found by a candidate gene approach, but several SNVs were confirmed in replication studies. Some genetic factors of AIH overlapped with those of other autoimmune diseases. Larger-scale GWASs of other ethnic groups are required. The results of genetic studies might provide an explanation for the phenotypic heterogeneity of AIH and biomarkers for drug responses.

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Genetic risk factors for variant Creutzfeldt–Jakob disease: a genome-wide association study

The Lancet Neurology ◽

10.1016/s1474-4422(08)70265-5 ◽

2009 ◽

Vol 8 (1) ◽

pp. 57-66 ◽

Cited By ~ 94

Author(s):

Simon Mead ◽

Mark Poulter ◽

James Uphill ◽

John Beck ◽

Jerome Whitfield ◽

...

Keyword(s):

Risk Factors ◽

Association Study ◽

Genetic Risk ◽

Genome Wide Association Study ◽

Genetic Risk Factors ◽

Genome Wide Association ◽

Genome Wide ◽

A Genome ◽

Jakob Disease

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Genetics, Transcriptomics and Meta-Taxonomics in Visceral Leishmaniasis

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2020.590888 ◽

2020 ◽

Vol 10 ◽

Author(s):

Jenefer M. Blackwell ◽

Michaela Fakiola ◽

Om Prakash Singh

Keyword(s):

Risk Factors ◽

Visceral Leishmaniasis ◽

Genetic Risk ◽

Leishmania Donovani ◽

Genome Wide Association Study ◽

Vaccine Development ◽

Genetic Risk Factors ◽

Interleukin 10 ◽

Rrna Genes ◽

Single Nucleotide Variants

Visceral leishmaniasis (VL) caused by parasites of the Leishmania donovani complex can be fatal in susceptible individuals. Understanding the interactions between host and pathogen is one way to obtain leads to develop better drugs and for vaccine development. In recent years multiple omics-based approaches have assisted researchers to gain a more global picture of this interaction in leishmaniasis. Here we review results from studies using three omics-based approaches to study VL caused by L. donovani in India: (i) chip-based analysis of single nucleotide variants in the first genome-wide association study of host genetic risk factors for VL, followed by analysis of epitope binding to HLA DRB1 risk versus protective alleles; (ii) transcriptional profiling demonstrating pathways important in Amphotericin B treated compared to active VL cases, including demonstration that anti-interleukin-10 unleashes a storm of chemokines and cytokines in whole blood responses to soluble leishmania antigen in active cases; and (iii) a meta-taxonomic approach based on sequencing amplicons derived from regions of 16S ribosomal RNA (16S rRNA) and 18S rRNA genes that allowed us to determine composition of both prokaryotic and eukaryotic gut microflora in VL cases compared to endemic controls. Overall, our omics-based approaches demonstrate that global analyses of genetic risk factors, host responses to infection, and the interaction between host, parasite and the microbiome can point to the most critical factors that determine the outcome of infection

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The ABCC4 gene is associated with pyometra in golden retriever dogs

Scientific Reports ◽

10.1038/s41598-021-95936-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maja Arendt ◽

Aime Ambrosen ◽

Tove Fall ◽

Marcin Kierczak ◽

Katarina Tengvall ◽

...

Keyword(s):

Risk Factors ◽

Genetic Risk ◽

Mixed Model ◽

Genome Wide Association Study ◽

Genetic Risk Factors ◽

Disease Development ◽

Mixed Model Approach ◽

Genome Wide ◽

A Genome ◽

Increased Risk

AbstractPyometra is one of the most common diseases in female dogs, presenting as purulent inflammation and bacterial infection of the uterus. On average 20% of intact female dogs are affected before 10 years of age, a proportion that varies greatly between breeds (3–66%). The clear breed predisposition suggests that genetic risk factors are involved in disease development. To identify genetic risk factors associated with the disease, we performed a genome-wide association study (GWAS) in golden retrievers, a breed with increased risk of developing pyometra (risk ratio: 3.3). We applied a mixed model approach comparing 98 cases, and 96 healthy controls and identified an associated locus on chromosome 22 (p = 1.2 × 10–6, passing Bonferroni corrected significance). This locus contained five significantly associated SNPs positioned within introns of the ATP-binding cassette transporter 4 (ABCC4) gene. This gene encodes a transmembrane transporter that is important for prostaglandin transport. Next generation sequencing and genotyping of cases and controls subsequently identified four missense SNPs within the ABCC4 gene. One missense SNP at chr22:45,893,198 (p.Met787Val) showed complete linkage disequilibrium with the associated GWAS SNPs suggesting a potential role in disease development. Another locus on chromosome 18 overlapping the TESMIN gene, is also potentially implicated in the development of the disease.

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How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases

Artificial Life ◽

10.1162/artl_a_00308 ◽

2020 ◽

Vol 26 (1) ◽

pp. 23-37 ◽

Cited By ~ 2

Author(s):

Jason H. Moore ◽

Randal S. Olson ◽

Peter Schmitt ◽

Yong Chen ◽

Elisabetta Manduchi

Keyword(s):

Risk Factors ◽

Genetic Risk ◽

Genetic Architecture ◽

Large Scale ◽

Genome Wide Association Study ◽

Real Data ◽

Genetic Risk Factors ◽

Human Diseases ◽

Nucleotide Polymorphisms ◽

A Genome

Susceptibility to common human diseases such as cancer is influenced by many genetic and environmental factors that work together in a complex manner. The state of the art is to perform a genome-wide association study (GWAS) that measures millions of single-nucleotide polymorphisms (SNPs) throughout the genome followed by a one-SNP-at-a-time statistical analysis to detect univariate associations. This approach has identified thousands of genetic risk factors for hundreds of diseases. However, the genetic risk factors detected have very small effect sizes and collectively explain very little of the overall heritability of the disease. Nonetheless, it is assumed that the genetic component of risk is due to many independent risk factors that contribute additively. The fact that many genetic risk factors with small effects can be detected is taken as evidence to support this notion. It is our working hypothesis that the genetic architecture of common diseases is partly driven by non-additive interactions. To test this hypothesis, we developed a heuristic simulation-based method for conducting experiments about the complexity of genetic architecture. We show that a genetic architecture driven by complex interactions is highly consistent with the magnitude and distribution of univariate effects seen in real data. We compare our results with measures of univariate and interaction effects from two large-scale GWASs of sporadic breast cancer and find evidence to support our hypothesis that is consistent with the results of our computational experiment.

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