Next-generation linkage and association methods applied to hypertension: a multifaceted approach to the analysis of sequence data

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

The PPLD has advantages over conventional regression methods in application to moderately sized genome-wide association studies

In earlier work, we have developed and evaluated an alternative approach to the analysis of GWAS data, based on a statistic called the PPLD. More recently, motivated by a GWAS for genetic modifiers of the X-linked Mendelian disorder Duchenne Muscular Dystrophy (DMD), we adapted the PPLD for application to time-to-event (TE) phenotypes. Because DMD itself is relatively rare, this is a setting in which the very large sample sizes generally assembled for GWAS are simply not attainable. For this reason, statistical methods specially adapted for use in small data sets are required. Here we explore the behavior of the TE-PPLD via simulations, comparing the TE-PPLD with Cox Proportional Hazards analysis in the context of small to moderate sample sizes. Our results will help to inform our approach to the DMD study going forward, and they illustrate several respects in which the TE-PPLD, and by extension the original PPLD, offer advantages over regression-based approaches to GWAS in this context.

Bridging the molecular and biological functions of the oxysterol-binding protein family

Oxysterol-binding protein (OSBP) and OSBP-related proteins (ORPs) constitute a large eukaryotic gene family that transports and regulates the metabolism of sterols and phospholipids. The original classification of the family based on oxysterol-binding activity belies the complex dual lipid-binding specificity of the conserved OSBP homology domain (OHD). Additional protein- and membrane-interacting modules mediate the targeting of select OSBP/ORPs to membrane contact sites between organelles, thus positioning the OHD between opposing membranes for lipid transfer and metabolic regulation. This unique subcellular location, coupled with diverse ligand preferences and tissue distribution, has identified OSBP/ORPs as key arbiters of membrane composition and function. Here, we will review how molecular models of OSBP/ORP-mediated intracellular lipid transport and regulation at membrane contact sites relate to their emerging roles in cellular and organismal functions.

A powerful test of independent assortment that determines genome-wide significance quickly and accurately

In the analysis of DNA sequences on related individuals, most methods strive to incorporate as much information as possible, with little or no attention paid to the issue of statistical significance. For example, a modern workstation can easily handle the computations needed to perform a large-scale genome-wide inheritance-by-descent (IBD) scan, but accurate assessment of the significance of that scan is often hindered by inaccurate approximations and computationally intensive simulation. To address these issues, we developed gLOD—a test of co-segregation that, for large samples, models chromosome-specific IBD statistics as a collection of stationary Gaussian processes. With this simple model, the parametric bootstrap yields an accurate and rapid assessment of significance—the genome-wide corrected P-value. Furthermore, we show that (i) under the null hypothesis, the limiting distribution of the gLOD is the standard Gumbel distribution; (ii) our parametric bootstrap simulator is approximately 40 000 times faster than gene-dropping methods, and it is more powerful than methods that approximate the adjusted P-value; and, (iii) the gLOD has the same statistical power as the widely used maximum Kong and Cox LOD. Thus, our approach gives researchers the ability to determine quickly and accurately the significance of most large-scale IBD scans, which may contain multiple traits, thousands of families and tens of thousands of DNA sequences.

Challenges of linkage analysis in the era of whole-genome sequencing

Whole-genome sequencing (WGS) is becoming an affordable technology for the study of the genetics of complex traits. With any new technology, experimental designs and statistical methods, both old and new, must be evaluated. One design seeing a resurgence of interest is the use of families. Genetic Analysis Workshop 18 provided the opportunity to evaluate statistical methods applied to WGS data for family-based studies. We summarize the results of five contributions that used linkage in the context of WGS. The investigators took differing approaches, including assessment of false-positive rates in classic two-point linkage, the effects of heterogeneity on linkage and association tests, and the use of linkage to focus association tests. We describe the primary findings of each contribution and note challenges that are not new to those working in family designs or specific to WGS data; for example, choice of phenotype definition, covariate adjustment, and use of longitudinal data may produce different results, making comparisons challenging. We detail new issues brought about by WGS, such as the elevated genome-wide false-positive rate for classic two-point parametric linkage analysis, computational demands in multipoint calculations, and lack of clarity in how to best use linkage to focus association testing. Finally, we comment on when linkage may be helpful for WGS, highlighting where additional research is needed; for example, although linkage analysis has been successful in the study of rare variants of large effect, how to best use family information in the context of rare variants of moderate effect remains an open research question.