Assessment of whole genome amplification for sequence capture and massively parallel sequencing

Abstract

Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumor tissue samples from 16 patients diagnosed with non-small cell lung cancer. The results obtained provide a comprehensive overview of the merits of these techniques for mutation analysis. We evaluated the identified genetic variants, and found that most (74%) of them were observed in both the amplified and the unamplified sequence data. Eighty-nine percent of the variations found by WGA were shared with unamplified DNA. We demonstrate a strategy for avoiding allelic bias by including RNA-sequencing information.

Figure 1. Genetic variants unique or shared between analysis.

A) Venn diagram illustrating the distribution of SNVs for patient 140. Unique variants found by WGA are represented in light blue, and unique variants found without amplification are shown in light green. Shared variants identified by both methods are shown in green. B) Boxplot of the coverage of genetic variants found uniquely by WGA, without amplification, and with both methods for patient 140. The two leftmost boxes represent shared variant calls with coverage in those positions for WGA and without amplification, respectively. The two rightmost boxes represent the coverage over unique positions for each method. C) Venn diagram illustrating the distribution of SNVs for patient 295. Unique variants found by WGA are represented in light blue, and unique variants found without amplification are shown in light green. Shared variants identified by both methods are shown in green. D) Boxplot of the coverage of genetic variants found uniquely by WGA, without amplification, and with both methods for patient 295. The two leftmost boxes represent shared variant calls with coverage in those positions for WGA and without amplification, respectively. The two rightmost boxes represent the coverage over unique positions for each method.

Figure 2. Example of coverage representation in unamplified and whole genome amplified DNA in patient 295 across the SPINK1 gene.

Figure 3. The relationship between variant and total reads per position depending on analysis.

A) The relationship between the numbers of variant reads divided by total reads for SNVs identified by sequencing WGA and unamplified DNA per position in patient 140. B) The relationship between the numbers of variant reads divided by total reads for SNVs identified by sequencing WGA and unamplified DNA per position in patient 295.