High Throughput Sequencing and Assessing Disease Risk

Abstract

High-throughput sequencing has dramatically improved our ability to determine and diagnose the underlying causes of human disease. The use of whole-genome and whole-exome sequencing has facilitated faster and more cost-effective identification of new genes implicated in Mendelian disease. It has also improved our ability to identify disease-causing mutations for Mendelian diseases whose associated genes are already known. These benefits apply not only in cases in which the objective is to assess genetic disease risk in adults and children, but also for prenatal genetic testing and embryonic testing. High-throughput sequencing has also impacted our ability to assess risk for complex diseases and will likely continue to influence this area of disease research as more and more individuals undergo sequencing and we better understand the significance of variation, both rare and common, across the genome. Through these activities, high-throughput sequencing has the potential to revolutionize medicine.

Figure 1.

A pedigree showing a family history of breast and ovarian cancer. Circles represent women. Squares represent men. An arrow indicates the patient, the 33-year-old women described in case 2 in the text.

Figure 2.

A pedigree for a family showing autosomal dominant inheritance for a hypothetical unknown disease here called “condition X.” Whole-exome or whole-genome sequencing can be performed on multiple affected and unaffected individuals to identify genetic changes present in all affected individuals but no unaffected individuals.

Figure 3.

A sample risk assessment for an individual for type II diabetes. The blue dot indicates a baseline general population risk for a Caucasian male to develop type II diabetes in his lifetime. Each black and gray dot represents the cumulative impact on that risk of individual single-nucleotide polymorphisms (SNPs) (represented by the rsIDs on the left). The final risk number at the bottom, a 34.4% lifetime risk of developing type II diabetes for this individual, is based on the cumulative impact of 10 SNPs (Chen et al. 2012).

Figure 4.

A sample risk assessment for multiple conditions for the same individual in Figure 3. The arrow represents the baseline risk for developing the disease for a Caucasian male. The end of the colored line indicates the final risk taking into account genetic single-nucleotide polymorphisms (SNPs) identified in the individual. Orange lines indicate increased risk and blue indicate decreased risk (Chen et al. 2012).