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P.M. VanRaden and D.M. Bickhart Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA

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Presentation on theme: "P.M. VanRaden and D.M. Bickhart Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA"— Presentation transcript:

1 P.M. VanRaden and D.M. Bickhart Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA paul.vanraden@ars.usda.gov Plant & Animal Genome, San Diego, California January 9 -11, 2016 (1) Fast Single-Pass Alignment and Variant Calling Using Sequencing Data

2 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (2) Motivation l Genomic methods require much computation w Genotype models replaced pedigree models w Sequence variants replacing chip genotypes w Both increased data by orders of magnitude l Fast methods are available for imputation l Slow methods for alignment, variant calling

3 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (3) Sequence computation l Alignment reports the chromosome location that best matches the short (150-base) DNA segment to the reference map (2.7 billion). l Often both ends of a longer segment are read and these paired ends are located together. l Variant calling reports if each mapped segment contains a reference or alternate allele at any site. These variants could be previously known or newly discovered.

4 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (4) Previous strategies l Almost all programs do alignment, and then variant calling, instead of both together. l Program BWA was examined as a popular alignment strategy, and GATK for calling. w “Mapping reads to the reference is a first critical computational challenge whose cost necessitates that each read be aligned independently, guaranteeing that many reads spanning indels will be misaligned.” DePristo et al (2011) GATK paper

5 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (5) Benefits of new strategy l Most programs align only to Dominette’s DNA l Findmap can align using all known DNA differences among and within breeds l Error rate is reduced by separating known SNPs and indels from machine read errors l Locations are mapped back to the same common reference (UMD3.1)

6 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (6) Algorithm used in findmap.f90 l Read reference map, store in hash table l Read and hash known variants (SNPs & indels) l Process batches of 1 million paired end reads, send to multiple processors sharing memory w Find location where both ends match map w Count alleles (reference, alternate) & errors l Output alignment and variant call files

7 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (7) Gaps, k-mers, and hashing strategy Identify long gaps between the same base (A, C, G, or T) TGGATTCTTTATCACTGAGCTACCTGGGAAGCCAAGTAAGC Extend each gap to a 16-base k-mer, convert to an 8-byte integer: Basenum (1, 2, 3, 4) = Base (A, C, G, T) Hashnum = 4 * Hashnum + Basenum, loop across 16-base k-mer Apply hash function (written by George Wiggans, 1988, USDA) Hash map, then hash each read (or its reverse complement)

8 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (8) Semi-simulated data l Simulated from UMD3.1 reference map l Variant file from run5 of 1,000 bull genomes w 38,062,190 SNPs, 532,179 insertions, and 1,127,620 deletions l Paired ends, length 150, fragment size 1,000 l Advantage of semi-simulated: true locations and true variants are known

9 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (9) Compare BWA and findmap Computation requiredBWA / GATKfindmap / findvar CPU minutes / 1X, 1 thread62911 CPU minutes / 1X, 10 threads 63 2 Memory (Gbytes), 1 thread 4.646 Memory (Gbytes), 10 threads 46 Variant calling CPU / 1X, 1 thread2011 Accuracy Correctly placed segments overall 90.5% 92.9% Both ends of pair correctly placed 87.2% 87.6% Both ends wrong 6.2% 1.8%

10 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (10) Parallel processing speedup

11 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (11) Program series – example resources Task (per animal)ProgramThreadsMinutes Simulate 10X datamap2seq.f90105 Align 10X data and call previous variantsfindmap.f901020 Sum new variantsfindvar.f9018 Imputation (39 million)findhap4.f90101

12 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (12) Accuracy of variant calling / discovery Known variantsSNPInsertionDeletion (%) Correct reference allele99.898.699.8 Correct alternate allele99.8 99.9 Call rate (paired ends ok)86.682.283.7 New variants (Homo / het) Correctly detected (10X)91 / 6354 / 3741 / 27 Falsely detected (10X)10178

13 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (13) Other alignment tests l Perfectly random genome, non-repetitive w Over 99.9% correctly aligned l RepeatMasker and BWA w Took 4.4 instead of 14.1 hours / 1X w Only 45% correctly aligned instead of 91% l Human genome gave results similar to cattle

14 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (14) File format sizes (Mbytes) Unzipped / zipped file sizesBWA, GATK Findmap, Findvar Input data: Sequence reads / 1X (fastq) 6000 / 1800 Output data: Binary alignment file / 1X (.bam) 3200 / 32001200 / 360 Called genotypes / animal (.vcf) 1000 / 3879 / 13

15 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (15) USA use of 1,000 bull genomes l Sequence genotypes from 440 Holsteins l Imputed for 27,000 reference bulls l 700,000 candidate loci + 300,000 HD SNPs l Largest 17K added to 60K routinely used l Average gain of 2.7% reliability across traits l Largest 5K added to next Zoetis chip

16 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (16) Largest genomic databases 23andMeAncestry.com CDCB / USDA Genotypes>1 million1.2 million SpeciesHuman Cattle Countries5549 Genotyping cost$199$99$37-135 Delivery (weeks)6-8 1-2 DNA generationsFew Many EBV reliabilityLow High Reference:http://genomemag.com/davies-23andme/#.VdY722zosY1http://genomemag.com/davies-23andme/#.VdY722zosY1 Web sites:https://www.23andme.com/https://www.23andme.com/ http://dna.ancestry.com/ https://www.cdcb.us/ http://aipl.arsusda.gov/Main/site_main.htm

17 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (17) Conclusions l Program findmap.f90 uses known variants w Alignment is 50X faster than BWA with 1 processor, 30X faster with 10 processors w 2% more segments are mapped correctly w Output files are simpler and 3-10X smaller l Simulation, alignment, variant calling, and imputation programs available from: http://aipl.arsusda.gov/software

18 Paul VanRaden Plant & Animal Genome, San Diego, California, January 9 -11, 2016 (18) Acknowledgments  This research was part of USDA-ARS project 1265-31000-096-00, “Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information.”  Jeff O’Connell provided much advice on alignment and variant calling methods.  The reference map was from U. Maryland  The variant list was from Daetwyler et al.

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