Finding genetic overlaps among diseases based on ranked gene lists

Where do obesity and male infertility collide?

The parallel rise in obesity and male infertility in modern societies necessitates the identification of susceptibility genes underlying these interconnected health issues. In our study, we conducted a comprehensive search in the OMIM database to identify genes commonly associated with male infertility and obesity. Subsequently, we performed an insilico analysis using the REVEL algorithm to detect pathogenic single nucleotide polymorphisms (SNPs) in the coding region of these candidate genes. To validate our findings in vivo, we conducted a comprehensive analysis of SNPs and gene expression of candidate genes in 200 obese infertile subjects and 240 obese fertile individuals using ARMS-PCR. Additionally, we analyzed 20 fertile and 22 infertile obese individuals using Realtime-qPCR. By removing duplicated queries, we obtained 197 obesity-related genes and 102 male infertility-related genes from the OMIM database. Interestingly, the APOB gene was found in common between the two datasets. REVEL identified the rs13306194 variant as potentially pathogenic with a calculated score of 0.524. The study identified a significant association between the AA (P value = 0.001) genotype and A allele (P value = 0.003) of the APOB rs13306194 variant and infertility in obese men. APOB expression levels were significantly lower in obese infertile men compared to obese fertile controls (p < 0.01). Moreover, the AA genotype of rs13306194 APOB was associated with a significant decrease in APOB gene expression in obese infertile men (p = 0.05). There is a significant association between the Waist-to-Hip Ratio (WHR) and LH with infertility in the obese infertile group. These results are likely to contribute to a better understanding of the causes of male infertility and its association with obesity.

Migraine and thyroid dysfunction: Co-occurrence, shared genes and biological mechanisms

BACKGROUND AND PURPOSE

Migraine and thyroid dysfunction, particularly hypothyroidism, are common medical conditions and are known to have high heritability. Thyroid function measures, thyroid stimulating hormone (TSH) and free thyroxine (fT4), are also known to be genetically influenced. Although observational epidemiological studies report an increased co-occurrence of migraine and thyroid dysfunction, a clear and combined interpretation of the findings is currently lacking. A narrative review is provided of the epidemiological and genetic association evidence linking migraine, hypothyroidism, hyperthyroidism and thyroid hormones TSH and fT4.

METHODS

An extensive literature search was conducted in the PubMed database for epidemiological, candidate gene and genome-wide association studies using the terms migraine, headache, thyroid hormones, TSH, fT4, thyroid function, hypothyroidism and hyperthyroidism.

RESULTS

Epidemiological studies suggest a bidirectional relationship between migraine and thyroid dysfunction. However, the nature of the relationship remains unclear, with some studies suggesting migraine increases the risk for thyroid dysfunction whilst other studies suggest the reverse. Early candidate gene studies have provided nominal evidence for MTHFR and APOE, whilst more recently genome-wide association studies have provided robust evidence for THADA and ITPK1 being associated with both migraine and thyroid dysfunction.

CONCLUSIONS

These genetic associations improve our understanding of the genetic relationship between migraine and thyroid dysfunction, provide an opportunity to develop biomarkers to identify migraine patients most likely to benefit from thyroid hormone therapy, and indicate that further cross-trait genetic studies have excellent potential to provide biological insight into their relationship and inform clinical interventions.

A novel similarity score based on gene ranks to reveal genetic relationships among diseases

Knowledge of similarities among diseases can contribute to uncovering common genetic mechanisms. Based on ranked gene lists, a couple of similarity measures were proposed in the literature. Notice that they may suffer from the determination of cutoff or heavy computational load, we propose a novel similarity score SimSIP among diseases based on gene ranks. Simulation studies under various scenarios demonstrate that SimSIP has better performance than existing rank-based similarity measures. Application of SimSIP in gene expression data of 18 cancer types from The Cancer Genome Atlas shows that SimSIP is superior in clarifying the genetic relationships among diseases and demonstrates the tendency to cluster the histologically or anatomically related cancers together, which is analogous to the pan-cancer studies. Moreover, SimSIP with simpler form and faster computation is more robust for higher levels of noise than existing methods and provides a basis for future studies on genetic relationships among diseases. In addition, a measure MAG is developed to gauge the magnitude of association of anindividual gene with diseases. By using MAG the genes and biological processes significantly associated with colorectal cancer are detected.

A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications

Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.