Whole Genome Amplification with Phi29 DNA Polymerase to Enable Genetic or Genomic Analysis of Samples of Low DNA Yield

AbstractWhile buccal cells provide an easily accessible source of genomic DNA, the amount extracted may be insufficient for many studies. Whole genome amplification (WGA) using multiple displacement amplification (MDA) may optimize buccal cell genomic DNA yield. We compared the usefulness, in epidemiological surveys, of DNA derived from buccal cells collected by alcohol mouthwash and amplified by WGA protocol and standard protocols. Buccal cell collection kits were mailed to 300 randomly selected members of a large cohort study, and 189 subjects returned buccal cell samples. We determined: (i) which QIAamp

Download Full-text

Comparison of single-cell whole-genome amplification strategies

10.1101/443754 ◽

2018 ◽

Cited By ~ 6

Author(s):

Nuria Estévez-Gómez ◽

Tamara Prieto ◽

Amy Guillaumet-Adkins ◽

Holger Heyn ◽

Sonia Prado-López ◽

...

Keyword(s):

Single Cell ◽

Whole Genome Amplification ◽

Whole Genome ◽

Sequencing Data ◽

Genome Amplification ◽

Dna Yield ◽

Amplification Bias ◽

Random Amplification ◽

Low Pass ◽

Amplicon Size

Single-cell genomics is an alluring area that holds the potential to change the way we understand cell populations. Due to the small amount of DNA within a single cell, whole-genome amplification becomes a mandatory step in many single-cell applications. Unfortunately, single-cell whole-genome amplification (scWGA) strategies suffer from several technical biases that complicate the posterior interpretation of the data. Here we compared the performance of six different scWGA methods (GenomiPhi, REPLIg, TruePrime, Ampli1, MALBAC, and PicoPLEX) after amplifying and low-pass sequencing the complete genome of 230 healthy/tumoral human cells. Overall, REPLIg outperformed competing methods regarding DNA yield, amplicon size, amplification breadth, amplification uniformity –being the only method with a random amplification bias–, and false single-nucleotide variant calls. On the other hand, non-MDA methods, and in particular Ampli1, showed less allelic imbalance and ADO, more reliable copy-number profiles and less chimeric amplicons. While no single scWGA method showed optimal performance for every aspect, they clearly have distinct advantages. Our results provide a convenient guide for selecting a scWGA method depending on the question of interest while revealing relevant weaknesses that should be considered during the analysis and interpretation of single-cell sequencing data.

Download Full-text

Whole-genome amplification-based GenomiPhi for multiple genomic analysis of individual early porcine embryos

Theriogenology ◽

10.1016/j.theriogenology.2010.12.018 ◽

2011 ◽

Vol 75 (8) ◽

pp. 1543-1549 ◽

Cited By ~ 8

Author(s):

Eri Akasaka ◽

Akio Ozawa ◽

Hironori Mori ◽

Yamato Mizobe ◽

Mitsutoshi Yoshida ◽

...

Keyword(s):

Whole Genome Amplification ◽

Genomic Analysis ◽

Whole Genome ◽

Genome Amplification

Download Full-text

Synthesis of universal unmethylated control DNA by nested whole genome amplification with ϕ29 DNA polymerase

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2005.01.088 ◽

2005 ◽

Vol 329 (1) ◽

pp. 219-223 ◽

Cited By ~ 29

Author(s):

Naoyuki Umetani ◽

Michiel F.G. de Maat ◽

Takuji Mori ◽

Hiroya Takeuchi ◽

Dave S.B. Hoon

Keyword(s):

Dna Polymerase ◽

Whole Genome Amplification ◽

Whole Genome ◽

Genome Amplification

Download Full-text

Evaluating Whole Genome Amplification via Multiply-Primed Rolling Circle Amplification for SNP Genotyping of Samples With Low DNA Yield

Twin Research and Human Genetics ◽

10.1375/twin.8.4.368 ◽

2005 ◽

Vol 8 (4) ◽

pp. 368-375 ◽

Cited By ~ 15

Author(s):

Kaisa Silander ◽

Kati Komulainen ◽

Pekka Ellonen ◽

Minttu Jussila ◽

Mervi Alanne ◽

...

Keyword(s):

Genomic Dna ◽

Large Scale ◽

Whole Genome Amplification ◽

Rolling Circle Amplification ◽

Limiting Factor ◽

Whole Genome ◽

Genome Amplification ◽

Rolling Circle ◽

Allele Dropout ◽

Dna Yield

AbstractThe amount of available DNA is often a limiting factor in pursuing genetic analyses of large-scale population cohorts. An association between higher DNA yield from blood and several phenotypes associated with inflammatory states has recently been demonstrated, suggesting that exclusion of samples with very low DNA yield may lead to biased results in statistical analyses. Whole genome amplification (WGA) could present a solution to the DNA concentration-dependent sample selection. The aim was to thoroughly assess WGA for samples with low DNA yield, using the multiply-primed rolling circle amplification method. Fifty-nine samples were selected with the lowest DNA yield (less than 7.5µg) among 799 samples obtained for one population cohort. The genotypes obtained from two replicate WGA samples and the original genomic DNA were compared by typing 24 single nucleotide polymorphisms (SNPs). Multiple genotype discrepancies were identified for 13 of the 59 samples. The largest portion of discrepancies was due to allele dropout in heterozygous genotypes in WGA samples. Pooling the WGA DNA replicates prior to genotyping markedly improved genotyping reproducibility for the samples, with only 7 discrepancies identified in 4 samples. The nature of discrepancies was mostly homozygote genotypes in the genomic DNA and heterozygote genotypes in the WGA sample, suggesting possible allele dropout in the genomic DNA sample due to very low amounts of DNA template. Thus, WGA is applicable for low DNA yield samples, especially if using pooled WGA samples. A higher rate of genotyping errors requires that increased attention be paid to genotyping quality control, and caution when interpreting results.

Download Full-text