Once a genetic variant is associated with a specific complex disease, its involvement and biological effects must be confirmed. An identified variant may have various effects on protein structure, gene expression, and regulation, or it may have no effect at all. Using genomic, transcriptomic, and epigenomic methods for quantitative trait loci (QTL) analysis, variants that have been associated with disease can be annotated with their functional effect on genes differentially expressed with disease to elucidate disease mechanisms and prioritize genes and pathways for further study.
Differential expression analysis is used to identify and measure changes in gene expression under different conditions or in response to determinate stimuli. Understanding which genes are over - or under- expressed with the desease phenotype is an important step in the determination of which genes and pathways are impacted or may be involved with disease. Although arrays were the first technology applied to large-scale expression studies, they have been replaced, in large part, by RNA sequencing (RNA-Seq) as an emerging best practice. RNA-Seq provides information about gene expression in addition to transcript isoforms, gene fusions, splice variants, and other features without the limitation of prior knowledge.
The RNA-Seq workflow for differential gene expression analysis begins with RNA extraction, library prep, and sequencing. Data analysis includes post-run processing of reads, estimation of individual gene expression levels, normalization and identification of differentially expressed genes). The following methods are available to gain insights into the transcriptome and its potential roles in complex disease:
Epigenetics is the study of biological mechanisms that alter gene activity through regulatory mechanisms without changing the DNA sequence. Identifying the state of the gene regulatory mechanisms, such as open chromatin, methylation, or binding of promoters and transcription factors, provides insight into why genes may be expressed at a given level. In addition to important functions in normal biological processes, epigenetic processes have established links to various complex diseases, including cancers, autoimmune disorders, neurological disorders, and psychiatric disorders. The following methods are available to gain insights into the epigenome and its potential roles in complex disease.
Whole-genome sequencing is a common approach for identifying rare variants associated with complex disease. It is the only method that can call both common and rare variants across the genome including structural variants that may contribute to disease.
CNVs are genomic alterations that result in an abnormal number of copies of one or more genes that are usually caused by structural rearrangements. Like SNPs, certain CNVs have been associated with disease susceptibility. Array-based approaches for detecting de novo CNVs (not present in or transmitted by either parent) offer efficient and reliable large-scale analysis. Arrays can be used to profile genomic variations such as amplifications, deletions, rearrangements, and copy-neutral loss of heterozygosity. However, the role (outcome) of common CNV is currently largely unknown.
While efficient for large CNV detection, genotyping arrays are less sensitive for small CNVs (< 50 kilobases). NGS offers base-pair resolution that can detect small CNVs missed by arrays. This can be useful for studies of missing heritability in complex diseases. The high resolution of sequencing and the high throughput of arrays provide effective genome-wide interrogation options to accomplish research objectives.
A quantitative trait locus (QTL) is a region of DNA associated with a specific phenotype or trait that varies within a population. Typically, QTLs are associated with traits with continuous variance, such as height or skin color, rather than traits with discrete variance, such as hair or eye color. QTL mapping is a statistical analysis to identify which molecular markers lead to a quantitative change of a particular trait.Learn More About QTL
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