A role for coding functional variants in HNF4A in type 2 diabetes susceptibility

AIMS/HYPOTHESIS

Rare mutations in the gene HNF4A, encoding the transcription factor hepatocyte nuclear factor 4α (HNF-4A), account for ~5% of cases of MODY and more frequent variants in this gene may be involved in multifactorial forms of diabetes. Two low-frequency, non-synonymous variants in HNF4A (V255M, minor allele frequency [MAF] ~0.1%; T130I, MAF ~3.0%)-known to influence downstream HNF-4A target gene expression-are of interest, but previous type 2 diabetes association reports were inconclusive. We aimed to evaluate the contribution of these variants to type 2 diabetes susceptibility through large-scale association analysis.

METHODS

We genotyped both variants in at least 5,745 cases and 14,756 population controls from the UK and Denmark. We also undertook an expanded association analysis that included previously reported and novel genotype data obtained in Danish, Finnish, Canadian and Swedish samples. A meta-analysis incorporating all published association studies of the T130I variant was subsequently carried out in a maximum sample size of 14,279 cases and 26,835 controls.

RESULTS

We found no association between V255M and type 2 diabetes in either the initial (p = 0.28) or the expanded analysis (p = 0.44). However, T130I demonstrated a modest association with type 2 diabetes in the UK and Danish samples (additive per allele OR 1.17 [95% CI 1.08-1.28]; p = 1.5 × 10⁻⁴), which was strengthened in the meta-analysis (OR 1.20 [95% CI 1.10-1.30]; p = 2.1 × 10⁻⁵).

CONCLUSIONS/INTERPRETATION

Our data are consistent with T130I as a low-frequency variant influencing type 2 diabetes risk, but are not conclusive when judged against stringent standards for genome-wide significance. This study exemplifies the difficulties encountered in association testing of low-frequency variants.

Comparison of genotype imputation strategies using a combined reference panel for chicken population

Using whole-genome sequence (WGS) data are supposed to be optimal for genome-wide association studies and genomic predictions. However, sequencing thousands of individuals of interest is expensive. Imputation from single nucleotide polymorphisms panels to WGS data is an attractive approach to obtain highly reliable WGS data at low cost. Here, we conducted a genotype imputation study with a combined reference panel in yellow-feather dwarf broiler population. The combined reference panel was assembled by sequencing 24 key individuals of a yellow-feather dwarf broiler population (internal reference panel) and WGS data from 311 chickens in public databases (external reference panel). Three scenarios were investigated to determine how different factors affect the accuracy of imputation from 600 K array data to WGS data, including: genotype imputation with internal, external and combined reference panels; the number of internal reference individuals in the combined reference panel; and different reference sizes and selection strategies of an external reference panel. Results showed that imputation accuracy from 600 K to WGS data were 0.834±0.012, 0.920±0.007 and 0.982±0.003 for the internal, external and combined reference panels, respectively. Increasing the reference size from 50 to 250 improved the accuracy of genotype imputation from 0.848 to 0.974 for the combined reference panel and from 0.647 to 0.917 for the external reference panel. The selection strategies for the external reference panel had no impact on the accuracy of imputation using the combined reference panel. However, if only an external reference panel with reference size >50 was used, the selection strategy of minimizing the average distance to the closest leaf had the greatest imputation accuracy compared with other methods. Generally, using a combined reference panel provided greater imputation accuracy, especially for low-frequency variants. In conclusion, the optimal imputation strategy with a combined reference panel should comprehensively consider genetic diversity of the study population, availability and properties of external reference panels, sequencing and computing costs, and frequency of imputed variants. This work sheds light on how to design and execute genotype imputation with a combined external reference panel in a livestock population.

Low-frequency pre-treatment HIV drug resistance: effects on 2-year outcome of first-line efavirenz-based antiretroviral therapy

OBJECTIVES

Assess the impact of pre-treatment high-frequency and low-frequency drug-resistant HIV variants on long-term outcomes of first-line efavirenz-based antiretroviral therapy (ART).

DESIGN

Prospective observational study.

METHODS

Participants' pre-treatment plasma RNA had two sections of HIV pol encoding reverse transcriptase sequenced (Illumina, MiSeq) using unique molecular identifiers to detect wild-type (pre-treatment drug-resistant variants less than 1% of viral quasispecies), low-frequency (1-9%) or high-frequency drug-resistant variants (10-100%). Associations between pre-treatment drug resistance and virologic outcomes over 24 months of efavirenz-based ART were assessed for the number and frequency of mutations by drug class and other resistance parameters.

RESULTS

Virologic failure was detected in 30 of 352 (9%) and pre-treatment drug-resistant variants were detected in the viral quasispecies of 31 of 352 (9%) participants prescribed efavirenz-based ART. Survival analyses revealed statistically significant associations between pre-treatment drug resistance at low (P < 0.0001) and high (P < 0.001) frequencies, at oligonucleotide ligation assay (OLA) (P < 0.00001) and non-OLA (P < 0.01) codons, to a single-antiretroviral class (P < 0.00001), and a shorter time to virologic failure of efavirenz-based ART. Regression analyses detected independent effects across resistance categories, including both low-frequency (P < 0.01) and high-frequency (P < 0.001) drug-resistant variants.

CONCLUSION

We observed that pre-treatment HIV drug resistance detected at low frequencies increased the risk of virologic failure over 24 months of efavirenz-based ART, but that most failures, regardless of drug-resistant variants' frequencies, were detected within a year of ART initiation. These observations suggest that when efavirenz-based ART is prescribed, screening for pre-treatment drug resistance by an assay capable of detecting low-frequency variants, including OLA, may guide clinicians to prescribe more effective ART.

Predicting gene expression responses to environment in Arabidopsis thaliana using natural variation in DNA sequence

The evolution of gene expression responses is a critical component of adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype to phenotype map is not well resolved for cis regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to environment. We tested if flexible machine learning models could learn some of the underlying cis-regulatory genotype to phenotype map. We tested this approach using cold-responsive transcriptome profiles in 5 diverse Arabidopsis thaliana accessions. We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and down-stream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis-regulatory motifs in DNA sequences to predict expression response to environment. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by biological complexity of regulation and the large potential regulatory code. Overall, approaches to predict DEGs between specific environments based only on proximate DNA sequences require further development, and additional information may be required.