Review: Genetic mutations affecting bull fertility

Cattle are a well-suited "model organism" to study the genetic underpinnings of variation in male reproductive performance. The adoption of artificial insemination and genomic prediction in many cattle breeds provide access to microarray-derived genotypes and repeated measurements for semen quality and insemination success in several thousand bulls. Similar-sized mapping cohorts with phenotypes for male fertility are not available for most other species precluding powerful association testing. The repeated measurements of the artificial insemination bulls' semen quality enable the differentiation between transient and biologically relevant trait fluctuations, and thus, are an ideal source of phenotypes for variance components estimation and genome-wide association testing. Genome-wide case-control association testing involving bulls with either aberrant sperm quality or low insemination success revealed several causal recessive loss-of-function alleles underpinning monogenic reproductive disorders. These variants are routinely monitored with customised genotyping arrays in the male selection candidates to avoid the use of subfertile or infertile bulls for artificial insemination and natural service. Genome-wide association studies with quantitative measurements of semen quality and insemination success revealed quantitative trait loci for male fertility, but the underlying causal variants remain largely unknown. Moreover, these loci explain only a small part of the heritability of male fertility. Integrating genome-wide association studies with gene expression and other omics data from male reproductive tissues is required for the fine-mapping of candidate causal variants underlying variation in male reproductive performance in cattle.

Open problems in human trait genetics

Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them.

Causal variants in Maturity Onset Diabetes of the Young (MODY) - A systematic review

BACKGROUND

Maturity Onset Diabetes of the Young (MODY) is an autosomal dominant type of diabetes. Pathogenic variants in fourteen genes are reported as causes of MODY. Its symptoms overlap with type 1 and type 2 diabetes. Reviews for clinical characteristics, diagnosis and treatments are available but a comprehensive list of genetic variants, is lacking. Therefore this study was designed to collect all the causal variants involved in MODY, reported to date.

METHODS

We searched PubMed from its date of inception to December 2019. The search terms we used included disease names and name of all the known genes involved. The ClinVar database was also searched for causal variants in the known 14 MODY genes.

RESULTS

The record revealed 1647 studies and among them, 326 studies were accessed for full-text. Finally, 239 studies were included, as per our inclusion criteria. A total of 1017 variants were identified through literature review and 74 unpublished variants from Clinvar database. The gene most commonly affected was GCK, followed by HNF1a. The traditional Sanger sequencing was used in 76 % of the cases and 65 % of the studies were conducted in last 10 years. Variants from countries like Jordan, Oman and Tunisia reported that the MODY types prevalent worldwide were not common in their countries.

CONCLUSIONS

We expect that this paper will help clinicians interpret MODY genetics results with greater confidence. Discrepancies in certain middle-eastern countries need to be investigated as other genes or factors, like consanguinity may be involved in developing diabetes.

Beyond GWAS: from simple associations to functional insights

Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.

MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires

Background

Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers.

Methods

The dataset comprised of 1337 heifers genotyped using a GeneSeek® Genomic Profiler panel (74677 SNPs), representing the daughters from 78 sires. After performing marker quality control, 64800 SNPs were retained. Haplotypes carried by each sire at six previously identified QTL on BTAs 5, 14 and 18 for heifer pregnancy and BTAs 8, 11 and 22 for antral follicle count were constructed using findhap software. The significance of the contrasts between the effects of every two paternally-inherited haplotype alleles were used to identify sires that were heterozygous at each QTL. Whole-genome sequencing data localized to the haplotypes from six sires and 20 other ancestors were used to identify sequence variants that were concordant with the haplotype contrasts. Enrichment analyses were applied to these variants using KEGG and MeSH libraries.

Results

A total of six (BTA 5), six (BTA 14) and five (BTA 18) sires were heterozygous for heifer pregnancy QTL whereas six (BTA 8), fourteen (BTA 11), and five (BTA 22) sires were heterozygous for number of antral follicles’ QTL. Due to inadequate representation of many haplotype alleles in the sequenced animals, fine mapping analysis could only be reliably performed for the QTL on BTA 5 and 14, which had 641 and 3733 concordant candidate sequence variants, respectively. The KEGG “Circadian rhythm” and “Neurotrophin signaling pathway” were significantly associated with the genes in the QTL on BTA 5 whereas 32 MeSH terms were associated with the QTL on BTA 14. Among the concordant sequence variants, 0.2% and 0.3% were classified as missense variants for BTAs 5 and 14, respectively, highlighting the genes MTERF2, RTMB, ENSBTAG00000037306 (miRNA), ENSBTAG00000040351, PRKDC, and RGS20. The potential causal mutations found in the present study were associated with biological processes such as oocyte maturation, embryo development, placenta development and response to reproductive hormones.

Conclusions

The identification of heterozygous sires by positionally phasing SNP chip data and contrasting haplotype effects for previously detected QTL can be used for fine mapping to identify potential causal mutations and candidate genes. Genomic variants on genes MTERF2, RTBC, miRNA ENSBTAG00000037306, ENSBTAG00000040351, PRKDC, and RGS20, which are known to have influence on reproductive biological processes, were detected.

Identification of deleterious and regulatory genomic variations in known asthma loci

Background

Candidate gene and genome-wide association studies have identified hundreds of asthma risk loci. The majority of associated variants, however, are not known to have any biological function and are believed to represent markers rather than true causative mutations. We hypothesized that many of these associated markers are in linkage disequilibrium (LD) with the elusive causative variants.

Methods

We compiled a comprehensive list of 449 asthma-associated variants previously reported in candidate gene and genome-wide association studies. Next, we identified all sequence variants located within the 305 unique genes using whole-genome sequencing data from the 1000 Genomes Project. Then, we calculated the LD between known asthma variants and the sequence variants within each gene. LD variants identified were then annotated to determine those that are potentially deleterious and/or functional (i.e. coding or regulatory effects on the encoded transcript or protein).

Results

We identified 10,130 variants in LD (r² > 0.6) with known asthma variants. Annotations of these LD variants revealed that several have potentially deleterious effects including frameshift, alternate splice site, stop-lost, and missense. Moreover, 24 of the LD variants have been reported to regulate gene expression as expression quantitative trait loci (eQTLs).

Conclusions

This study is proof of concept that many of the genetic loci previously associated with complex diseases such as asthma are not causative but represent markers of disease, which are in LD with the elusive causative variants. We hereby report a number of potentially deleterious and regulatory variants that are in LD with the reported asthma loci. These reported LD variants could account for the original association signals with asthma and represent the true causative mutations at these loci.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0953-2) contains supplementary material, which is available to authorized users.

Identifying causal variants at the interferon lambda locus in case-control studies: Utilizing non-synonymous variant rs117648444 to probe the role of IFN-λ4

Genetic variants at the interferon lambda (IFNL) locus have been associated with several human phenotypes in both disease and health. In chronic hepatitis C virus (HCV) infections, where the IFNL variants were first identified to be associated with response to interferon-α-ribavirin therapy, the available data clearly suggests that the causal variant could be the dinucleotide polymorphism rs368234815 that causes an open reading frame-shift in the IFNL4 gene resulting in expression of a functional IFN-λ4, a new type III IFN. In other human diseases/phenotypes where IFNL variants have been recently associated with, the causal mechanism remains unclear. In vitro evidence has shown that other IFNL variants (rs28416813, rs4803217) may regulate expression of another type III IFN, IFN-λ3. Therefore, expression of a functional IFN-λ4 and quantitative differences in IFN-λ3 expression are two potential causal mechanisms behind the observed phenotypes. Since these two potential causal mechanisms involve features of mutual exclusivity and overlapping functions, it is difficult to differentiate one from the other, in vivo, in absence of other implicating evidences. In addition, the strong linkage disequilibrium (LD) observed in many populations at the IFNL locus makes it difficult to tease out the actual functional/causal variants responsible for the phenotypes. The non-synonymous single nucleotide polymorphism rs117648444 that alters the activity of IFN-λ4 and the LD structure in the IFNL region which leads to a confounding effect of rs117648444 on other IFNL variants, provide us with additional tools in case-control studies to probe the role of IFN-λ4.

Genetic profile of patients with early onset inflammatory bowel disease. Gene. 2018;645:18-29. [PMID: 29248579 DOI: 10.1016/j.gene.2017.12.029]

Inflammatory Bowel disease (IBD) is a widespread pathological condition with clinical heterogeneity and with different levels of severity. Although IBD usually occurs in young adults, onset in childhood and infancy are described in patients within the 10th and second year of age. By genome-wide association studies and meta-analysis, several genetic loci have been identified associated with an increased risk of developing IBD in Western populations with variants that may alter the normal mucosal immunity in the gastrointestinal tract. The clinical complexity and the heterogeneity of the IBD phenotype probably reflect the presence of genetic heterogeneity where different genes or combinations of them may be involved, together with environmental factors. We hypothesized that patients with early onset IBD could have either more severe genetic variants in genes associated with IBD or multiple variants in different genes. Under the multifactorial diseases is crucial to consider the small contribution of a single variant in all not only to other small variations in the same gene but also in different genes belonging to the same pathway. We performed direct gene sequencing looking for 94 variations in NOD2, ATG16L1, IL23R, IL10R, IL10 and XIAP genes previously shown as correlated with IBD both in multifactorial and in Mendelian models. All variants identified are known in literature as being associated with IBD except for three variants in the genes NOD2, IL10 and IL10RB that even though present in online databases have never been involved in association studies on IBD patients. Moreover, we coupled genetic variants identification with an accurate "in silico" analysis to verify their predictive impact on the protein structure and function. The in-silico prediction of these variants results as benign therefore even if they exhibit a very low frequency in control population being benign, they cannot be considered pathogenic as monogenic disease but fall within the multifactorial range. The variants identified in our study partially reflect the association data described in the literature but there are no significant differences with the onset of disease (VEO vs EO-IBD).

Challenges and progress in interpretation of non-coding genetic variants associated with human disease

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.