Meiotic gene conversion in humans:rate, sex ratio, and GC bias
Amy L. Williams
June 19, 2013University of Chicago
Gene conversion defined
•Meiosis: produces haploid germ cells withrecombinations
•Gene conversion: short segment copied intogiven chromosome from other homolog
Meiosis
Crossover
GeneConversion
Two types of
recombination:
•Number of gene conversions per meiosis?
–4-15× # crossovers? Jeffreys and May (2004)
•Length of gene conversion tracts?
–55-290 bp? Jeffreys and May (2004)
Study question 1: gene conversion rate?
•Number of gene conversions per meiosis?
–4-15× # crossovers? Jeffreys and May (2004)
•Length of gene conversion tracts?
–55-290 bp? Jeffreys and May (2004)
•Per base-pair rate? Fraction of genome affected
–R = (number × tract length) / genome length
–2.2×10-6 to 4.4×10-5? Jeffreys and May (2004)
Study question 1: gene conversion rate?
Study question 2: male vs. female rate?
•Gender differences in rate?
–Crossovers: female rate 1.78× male (deCODE)
Study question 3 & 4: GC bias? Localization?
•GC bias observed in allelic transmissions?
•Crossover hot spots influence location?
•Locations of gene conversions independent ina given meiosis?
Myers et al., Science 2005
Summary: study questions
1.Genome-wide de novo gene conversion rate?
2.Different rate between males/females?
3.Extent of GC bias in tracts?
4.Localization: Hotspots? Tracts independent?
Outline
•Background / study questions
•Study design and methods
•Results
–SNP chip data
–Sequence data
Approaches to identify gene conversions
•Linkage disequilibrium based
–Can give rate estimate
–Averaged over human history, both genders
•Sperm-based
–Many meiotic products: per-individual estimates
–Single molecule: genome-wide assays difficult
•Pedigree-based
–De novo, per-gender events observable
–Data for many samples required
Study design: SNP chip data for pedigrees
•Primary analysis: pedigree SNPchip data
•Challenge: small tracts
–Tracts covered by ≤ 1 SNP
–Not all tracts covered, but stillobtain overall rate
•Chip data give per base-pair rate
–R = # gene conversions / # informative sites
Datasets for analysis
•Mexican American pedigrees
•Data source 1: San Antonio Family Studies
–2,490 genotyped samples, 80 pedigrees
–SNP chip genotypes (Illumina 1M, 660k)
–Can estimate de novo gene conversion rate
Datasets for analysis
•Mexican American pedigrees
•Data source 1: San Antonio Family Studies
–2,490 genotyped samples, 80 pedigrees
–SNP chip genotypes (Illumina 1M, 660k)
–Can estimate de novo gene conversion rate
•Data source 2: T2D-GENES Consortium
–607 sequenced samples, 20 pedigrees
–Whole genome sequence (Complete Genomics)
–Can examine tract length, distribution, etc.
•Though need deep data on single family to do so
Study design: SNP chip data for pedigrees
•Pedigree-based haplotypes/phasereveal recombinations
–Heterozygous sites: informative forrecombination
•Phasing method: Hapi
–Phases nuclear families
–Williams et al., Genome Biol. 2010
Family-based phase reveals recombinations
•Hapi output: paternal haplotype transmissions
Crossover:
Haplotype 2
Haplotype 1
Family-based phase reveals recombinations
•Hapi output: paternal haplotype transmissions
Crossover:
Gene Conversion:
Haplotype 2
Haplotype 1
Other pedigree phasing methods
•Most pedigree phasing methods slow
–Runtime complexity for phasing ~O(m 22n)
•n = # non-founders
•m = # markers
–Example: nuclear family with 11 children
•4,194,304 states per marker
•Can merge exponential class of states
•Many states extremely unlikely to be optimal
Hapi: efficient phasing of nuclear families
•Hapi: state space reduction improves efficiency
–Merges exponential class of states
–Omits states that cannot yield optimal solution
•Applied to family with 11 children
–Average per marker states: 4.2, maximum 48
Hapi: efficient phasing of nuclear families
•Hapi: state space reduction improves efficiency
–Merges exponential class of states
–Omits states that cannot yield optimal solution
•Applied to family with 11 children
–Average per marker states: 4.2, maximum 48
Program
All families (N=103)
Runtime
Speedup
Hapi
3.1 s
-
Merlin
1,005 s
323×
Allegro v2
7,661 s
2,462×
Superlink
1,393 s*
448×
* Superlink failed to analyze 11 child family; 8/11 children used
Hapi: efficient phasing of nuclear families
•Hapi: state space reduction improves efficiency
–Merges exponential class of states
–Omits states that cannot yield optimal solution
•Applied to family with 11 children
–Average per marker states: 4.2, maximum 48
Program
All families (N=103)
≤ 3 children (N=86)
Runtime
Speedup
Runtime
Speedup
Hapi
3.1 s
-
2.2 s
-
Merlin
1,005 s
323×
8.7 s
3.8×
Allegro v2
7,661 s
2,462×
14.5 s
6.4×
Superlink
1,393 s*
448×
38.8 s
17.2×
* Superlink failed to analyze 11 child family; 8/11 children used
Applying Hapi to multi-generational pedigrees
•Hapi currently applies to nuclear families
–For 3-generation pedigrees analyzed for geneconversions, omit sites with phase conflicts
•Will not bias results, but data are reduced
Applying Hapi to multi-generational pedigrees
•Hapi currently applies to nuclear families
–For 3-generation pedigrees analyzed for geneconversions, omit sites with phase conflicts
•Will not bias results, but data are reduced
•Extension to Hapi possible to efficiently analyzearbitrarily large pedigrees
–Most San Antonio Family Studies pedigrees toolarge to be phased in practical time
Approach to identifying gene conversions
1.Perform QC, phase 3-generation pedigrees
2.Find gene conversions in 2nd generation:single SNP double crossovers
3.Confirm:
–Gene converted allele in 3rd generation
–Other allele in 2nd generation sibling(s)
•False positive only if ≥ 2 genotyping errors
Outline
•Background / study questions
•Study design and methods
•Results
–SNP chip data
–Sequence data
Current analysis dataset
•Analyzed SNP chip data for 16 pedigrees
–Data for both parents, 3+ children, 1+ grandchild
–190 samples
–42 meioses (21 paternal, 21 maternal)
•4.15×106 informative sites
•Rate: 7.95×10-6/bp/generation
–Within range of Jeffreys and May (2004)
–Close to LD-based estimates
Result 1: 33 putative gene conversions, rate
Male
Female
•Rate: 7.95×10-6/bp/generation
–Within range of Jeffreys and May (2004)
–Close to LD-based estimates
Result 1: 33 putative gene conversions, rate
Male
Female
Are these real geneconversions?
•19 sites sequenced by T2D-GENES Consortium
–18/19 gene conversion genotypes verified
•Differing site looks like sequencing artifact
–2nd generation recipient has genotype mismatch3rd generation grandchild shows same genotype
–If sequence data correct,gene conversion ingrandchild
T2D-GENES sequence confirms events
•More female gene conversions than male
–Females transmit 1.54× males
–Difference (yet) not significant – larger sample coming
•Different rates expected based on crossovers
–Female crossover rate 1.78× male (deCODE)
Result 2: gene conversion rates by gender
Result 3: gene conversions localize in hotspots
2.71% of genome in≥10 cM/Mb hotspots
Result 3: gene conversions localize in hotspots
10/33 gene conversionswith ≥10 cM/Mb:
P=1.1×10-8
2.71% of genome in≥10 cM/Mb hotspots
Result 4: observe extreme GC bias
•31 GC informative sites
–A/C, A/GT/C, T/G
•GC transmission in 74% of cases(95% CI 59% – 90%)
–GC bias likely (P=5.3×10-3)
Outline
•Background / study questions
•Study design and methods
•Results
–SNP chip data
–Sequence data
Sequence near chip-identified gene conversions
•Sequence available for 11/33 putative sites
Sequence near chip-identified gene conversions
•Sequence available for 11/33 putative sites
•Shortest resolution for tract length ≤ 143 bp
Sequence near chip-identified gene conversions
•Sequence available for 11/33 putative sites
•Clustered gene conversions in 4 sequences
Sequence near chip-identified gene conversions
•Sequence available for 11/33 putative sites
•Clustered gene conversions in 4 sequences
Boxed regions confirmedby Sanger sequencing
Relationship to complex crossover?
Haplotype 2
Haplotype 1
Conclusions
•Estimate of de novo gene conversion rate
–7.95×10-6/bp/generation
–Females: 1.54× gene conversions vs. males
•Enriched in hotspots: similar mechanism tocrossover
•GC vs AT allele transmitted ~3:1 – GC bias
•Complex/clustered gene conversions observedin sequence data
–Suggests unique correlation within short region
The T2D-GENES Consortium (NIDDK)San Antonio Family Studies (NIDDK, NIMH)
NHGRI NRSA Fellowship
Acknowledgements
Nick Patterson
David Reich
John Blangero
Giulio Genovese
Tom Dyer
Kati Truax
