Translational Genomics

Ben Hayes – Genomics for livestock breeding

• ref population with known genotypes and phenotypes.
• generate genomic breeding value equation (linear weighted sum of SNPs)
  • selection population from Marker genotypes (with SNPs). Chose which to use for breeding
• SNPs don’t work across population or breeds
  • not dense enough
  • accuracy errodes rapidlyu
  • genome seq data instead of 50K SNP data?
• 27 breeds, 1000 – 2K individual cows sequenced. 35 million SNPs, 2 million INDEL
• BayesRC
  • allows different proportion of variants in each class
  • allows tissue specific differences in expression
  • ‘class’ e.g. synomous vs. non-synonomous variations
• two traits selected
  • lactation genes
  • temperment (farmers don’t like being kicked)
• experiment
  • 20,000 individuals with SNP ChIPs, from Holstein (B&W), Jeresey
  • apply results in a different breed not in the original data (Red cows)
  • BAESRC makes a much clearer distinguishment of SNP in PARE(?) lactase associated gene
  • .1% of genes explain 1% of variance. 95% of genes explain 0%
• temperment
  • TMEM113D – linked in mouse and humans to anxiety and panic behaviors.
• conclusions
  • sequence data + improved method -> more precise mapping
  • need greater information about classes (e.g. ENCODE)

Eli Rodger-Melnick: Open chromatin

• intro to maize: Diploid, 2.3 Gb genome, 10 chromosomes, high diversity (human-to-chimp scale). rapid LD decay
• light and heavy MNase Digest in whole root and whole shoot. Call MNase hyper-sensitive
• gene Tb1 regulates growth of lateral branches.
  • regulated by two transposable elements 65 Kb upstream.
  • MNase hyper sensitive sites ID enhancers and promoters
• compare 2 lines, with 50 SNPs, test algorithm for assigning explanatory variance
• MNase HS sites are a small subset of the low methylation regions
• over half of the HS sites in root are shared in shoot.
• most MNase sites are near genes (though 95% are outside of genes). Minor peaks in frequency around 100 kb out
• GWAS hits are enriched in and around open chromatin.
• MNase HS distribution explains nearly as much of the variance in GWAS as CDS (despite representing less total part of the genome than CDS). Larger erniched if normalized by representation
• shoot specific data explains most traits (but most traits are shoot related).

Questions

• why MNase instead of FAIREseq or DNH

De Groot – Cancer application

• genomic instability, genomic heterogeneity, complex, heterogeneous spatial context
• use tissue engineering and micro-fludics to understand
• why micro-fluidics — more assays on same sample volume, especially
• use passive pumping, — different in pressure, operated with pipettes, no pumps
• exploit low mixing in microfluidic volumes to create diffusion gradients/migration experiments.
• Application to multiple Myeloma
  • a blood cancer that affects the eldery
  • manageable (with difficulty), terminal condition, strong interaction with microenbironment
  • myeloma resides in bone marrow — contributes to drug resistance
• setup:
  • two culture chambers, connected by central well with small channels which allow for difffusion in uniform gradients
• findings
  • monoculture not as predictive (separated but overlappping distributions)
  • in context of whole explaint in outside chambers, response is much better seperated
• new setup: hanging drop culture: two well (allows adding food, treatments etc (and removing?) without disrupting 3D)
  • developed porous polymeric scaffold.
  • compare direct co-culture (physical interaction) and indirect coculture (share extracellular fluid)
  • stromal cells on scaffold added to mylenoma cells in bottom of droplet

Diane Dickel: Large scale in vivo enhancer deletion with CRISPR/Cas9 (Berkeley)

• excellent talk, not open

Denis Lo – non-invasive molecular diagnostics

• non-invasive prenatal testing
• Plamsa DNA can be isolated from blood
  • can sequence these and separate
• fetal DNA fragments tend to be small.
  • also see different distribution of peaks
  • fetal DNA depleted in linker after fragmentation (less heterochromatin)
• mtDNA is very short, no peaks (no histones)
• the greater the fraction of the DNA coming from the fetus the shorter the size distribution
• can ID trisomies based on this size diagnostic (not based on counting)
• can combine counting and size for more robust
• can we detect cancer in this way? — cancer has CNV can be detected
• now extending this approach to cohort of 200 patients, 32 healthy, 67 HBV carriers, 90 HCC patients (tumors) 84% detected.
  • this is all traditional sequencing measure
• more tumor DNA the more short sequences detected.
• biggest size difference corresponds with more tumors
• antibodies bind circulating DNA? use anti-DNA antibodies
• patients with active SLE have a larger peak at small levels, related to the amount of anti-DNA in the plasma
  • enriched in hypomethylated DNA.
• increased tumor cell death, plasma DNA goes up more.

Questions

• classify cancer types? – not yet, but maybe in combinations with other data
• histones still associated in plasma DNA? – yes

Boris Rebolledo-Jaramillo on mtDNA

• D-loop, relatively recent part of sequence, also most frequently mutated. involved in replication and transcription (which are interdependent in mtDNA)
• most mutations which cause disease are heteroplasmic
  • severity of disease corresponds with fraction of abberant genomes
  • heteroplasmy goes through stochastic bottleneck
• experiment:
  • blood and cheek swab samples from mother and child. Isolate mtDNA
  • developed robust pipeline to ID artifcats from sample contamination, vial swapping
• Focus on sites with >1000 seq depth and MAF > 1%, -> 172 sites. significant by R. Nileson method
• validate sites using illumina vs Sanger
• ID heteroplasmy only in child or only in mother
  • evidence of bottleneck. Can calculate quantitative effect
• Germ-line mutation rates.
  • D-loop 0.08, full mtDNA 0.013 (lower limit due to filtering for high confidence)
• maternal age dependence on bottleneck frequency?
  • yes, older mothers more heteroplasmies
  • older mothers, more heteroplasmies in the child.
• disease associated alleles – 1 in 8 mothers are carriers (but all are healthy in this study)
  • in 1 child levels of heteroplasmy are comparable to those in patients with the mtDNA disesase.
• checked inference also looking at hair: frequencies are similar.

Siim Sober (U. Tartu, Estonia): RNA-seq of placental transcriptional landscape in normal and complicated pregnacies

• Placenta: an important organ.
• observed in dutch hunger that starvation in mothers during pregancy leads to lasting effects of metabolism of children (e.g. diabetes)
• placenta disorder: preeclampsia – leads to premature delivery, 5% of pregnancies, linked to proteinuria (not enough protein?)
• Experimental design
  • 8 normal births
  • 8 of reach of 4 issues: small gestational age, large gestitational age, preeclampsia, and maternal diabetes.
  • RNA seq, average 17x coverage.
• enriched in placental genes.
• ID genes associated with differential expression as a function of delivery (vaginal vs c-section) fetal gender differences are all sex-chromosome genes.
• birth length and maternal age and number of births did not lead to any differential gene expression in the placenta.
• preclambpsia gene expression is clearly distinct for numerous genes from other pregnancies.
• correlated expression in baby size.

Ana Vinuela

• regulatory landscape of Islet
• 90% of variants associated with traits and diseases by GWAS are non-coding
• effect of non-coding variants largely studied by eQTL — (expected effect on levels of gene expression). But this must be done on the right tissue.
• type 2 diabetes.
  • islet of langerhans produce insulin
• setup
  • difficult to get pancreatic samples
  • combined multiple studies data: 259 samples (whole Islets). 26 samples for Beta cells
  • need approach to remove batch effects by lab
• normalizing
  • PC1 and PC2 cluster the different labs. Correct these effects in the first 10 PCs so the difference between labs disappears.
• now doing eQTL discovery
• ID islet eQTLs (mostly in promoters, as typically observed).
• have an IRX3 eQTL

Diverse growth tolerance

• Mimulus grows in both serpintine and non-serpintine soils
• focus today: also grows in toxic soils of abandoned mines
  • is this from new mutations developed since?
  • Bradshaw previously argued found tolerant indviduals from distant populations, so must be pre-existing
• focus on copper mines at copperopolis
• tolerant plants can grow roots in presence of copper — genetic basis with tolerance dominant mendilian.
• plants from the mines survive much better in copper mine soils.
• Cu tolerance locus explains most but not all of the variance
• Cu locus fell into a near-centromere region.
• find many examples of tolerance of sweeps occurring between mine and non-mine
• don’t find signal at Tol1 region of sweep
• core haplotype

ANdy Clark – Opssum genomic imprinting

• ~100 imprintd genes, ~20% are neuronal
• why give up diploidy?
• questions
  • how conserved are molecular mechanisms?
  • what are all the genes?
  • how do they get arise?
• examples:
  • H19 methylation difference – promoter silencing (methylated of allele silenced ?)
  • imprinting control region determines binding of CTCF between enhancer and IGF2 locus (methylated allele active ?)
• lot of variation of in which genes are imprinted across species
  • no imprinted genes in platypus, between marisupuls and other mammals
• Experiment
  • reciprical cross
  • RNA-seq and ChIPseq and methylation of extra embryonic tissue and embryonic tissue
  • ID SNPs and annotate expression.

findings

• major imprinted genes still imprinted
• Ube3a is imprinted in human and mouse but not in opossum
• new coding genes have differential methylation but noncoding do not
• methylation appears hemi-methylated by Pyro.
• marsups mostly inactive that paternal X. Small number of escapers — don’t overlap the eutherian mammal genes
• 8 eutherian mammal imprinted genes found imprinted in marsup.
• “collateral damage” expression of genes in introns of other genes affected in allele specific way by one of them acquiring imprinting.

Question

• What fraction of imprinting requirement is expression level,
  • recently acquired genes become lethal

Differences in patterns of recombination

• fly preferentially recombine near centromere, human near telomere, plant, away from centromere.
• birds do not have PRDM9
• compare recombination rates in zebra finch and long-tailed finch (similar to human to chimp divergence)
• zebrafinch and longtailed finch have more typical levels of nucleotide diversity — though many
• identify ‘hotspots’ of recombination
• most of the hotspots are shared between the species – 73% (close to detection level – possible all are shared)
• evidence of GC bias at hotspots
• hotspots appear to be conserved across greater genomic distances — unlike mammals.
• recombination peaks at transcription start sites. (genome acessable areas)
• birds do hybridize more readily — maybe this is related to the lack or recombination.

Di Rienzo – GWAS and ancestry of high altitude adaptation in tibetans

• Tibetas do not have the aclimatized Hb phenotype
• which physiological and genetic adaptations allow them to cope with low O2 (if not more Hb)
• regulators of hypoxia response: EPAS1 and EGLN1
• propose Tibetans are admixture of ancestral low-alt and ancestral high-alt, where sherpa are pure decedents of ancestral HA.
• second round of adaptation occurred after admixture
• cohort of ethnic Tibetans with SNP data (exome or genome?)
• find selective sweep at EP300, regulator of HIF1alpha
• EPAS1 gene also strong hit.
• also SLCO1A2 – invovled in heme turover. Heparin sulfate synthesis gene. and previously known EGLN1
• prominent role for transcriptional regulators
• no GWA association for Hb O2 – many true determinants do not reach genome wide signficance. in-signficant power despite sample size ~1000.
• Hb levels throughout lifespan is not the trait under selection (?)
• adaption in oxygen supply to fetus?
• both pregancies and live birth alleles in tibetians are signficantly enriched in Tibitans.
• Hb and Oxygen saturation linked genes show slight but insignificant increase among Tibitans.
• explanation of tradeoff — high Hb levels at high altitude increase blood viscosity leads to problems with pregancy and with strokes, heart disease etc.

Bachtrog

• Awesome but not tweetable: see protected post

Dowell (Carroll Lab): evolution of snake venom

• generating molecular novelity
  • duplication and new evolution
  • fusing of two ancestral poteins
• how frequently being used in other systems
• system: origin of snake venom
  • co-evolution of predator and prey. / adaptation co-variation
  • within species lots of variation.
• Western diamond-back rattlesnake venom: mostly metalloproteases and serine proteases. Also PLA2 (focus here)
• neurotoxic Pla2 heterodimer
• rattlesnake (Crotlids) phylogeny of non-neurotoxic, neurtoxicity prevelant on multiple branches. PLA2 on multiple scattered branches — likely ancestors had neurotoxic venomn, lost in many modern lineages.
• find other venom PLA2 genes mixed in with the locus of the dominant PLA genes in Mojhave rattlesnakes
• most of the transcription is just these two genes, not the near by PLA2s
• aligned locus to itself (duplication event?) — data supports 3(?) duplication events
• align to python genome, find locus but find only 1 bPla2 gene, not MtxA and B – no duplication event in this common ancestor.
• find orthogs in MtxB and MtxA in diamond back (here these are not the major used PLA2s) highly expressed with a key residue mutated. (Western D.R. not neurotoxic)

conclusions

• neurotoxic behavior is ancesteral and lost

Questions

• why lose? – larger snakes generally less neurotoxic, true in rattlesnakes
  • also correlated in scorpions, especially with strong hands, don’t need strong neurotoxin

Fu – an early modern human with recent Neandertal ancestor (Oase 1)

• 1-4% geneflow from Neandertal to modern human (not present in African humans)
• early modern human genomes, ~36 kya, and ~45 kya samples have neaderthals. Dated mixture ~50K yeears ago
• new ~37 Kyr human from ~Adriadic pennisula
• challenges: lots of microbe DNA, little degraded endogenous DNA, Fresh human DNA contamination.
• solution signature: cytosine deamination, C -> U read as T.
• mtDNS (more copies). 67% contamination, mostly from European. damage fragment much further back on lineage
• in solution capture (to replace shotgun), to get high efficiency recovery of nuclear DNA.
• equally close to pre-agriculture europeans and Asians, further away from modern european human / post-farming
• Oase 1 has 5-11% Neandertal (closer than modern).
  • also has larger contiguous chunks than others
  • estimate only 4 to 5 generations. Must have had admixture multiple times, not just ~50K years ago, also ~30-40K years ago.
  • this individual may be a decendent of both
• modern human Y does not have identified neandertal

Hilary Martin (Donnelly Group, oxford)

• 160 my divergence from other mammmals
• genome published in 2008: 21 autosomes, 10 sex chromosomes. Most scaffolds < 10 kb (error-prone), 20% seq assigned to chrom. No Y (seq female).
• scaffold with long mate apirs, BACs and fosmids. similar scaffold length but fewer errors
• Use GATK and CORTEX to call SNPs in individuals
• population structure (Tasmania clearly isolated on PC1). PC2 see split along NS axis.
• PRDM9 not likely to exist in paltypus.
• still have recombination hotspots (possibly an issue with genome assembly affecting LD estimates)
• some overlaps but a number of divergent hotspots of recombination — most in fact not shared.
• most hotspots not near TSS
• females 5 pairs of Xs, males 5 Xs 5 Ys
• fined evidence of recent combination between X and Y (still polymorophic within the individual river population)
• X onto Y recombination could be due to meiotic recombination or non-allelic homologous recombination mediated by flanking repeats. — transition of autosome to sex chromosome?

Price – Mixed Model association

• Increase power by assuming a mixture of two normal distributions instead on 1 normal – sounds like just adding free parameters not like adding more biology. Can you give some more intuition?
• PCA
  • fast PCA
  • if just interested in the top eigenvector there are faster ways.
  • can multiple by a random KxN matrix to pick out top K eigenvectors
  • agree with other PCA methods such as PLICK
• top 10 eignevectors for 55,000 americans, clustering in lesss than an hour. Get expected ethnic clustering on major eigenvectors
  • also separate Irish / Northern European, and Eastern European in the 4th and 3rd eigenvectors
• ID alcoholism resistance gene in euro populations (previously ID’d in asain populatgions)

Chad Harland – Frequency of mosaicism points towards mutation prone early cleavage cell divisons

• germ line de novo mutations
  • errors in DNA replication
  • DNA breakdown
  • higher in M than F
  • higher in older males
• detect germ line de novo mutations by sequencing parents and child at frequency ~50%
• Damonda dataset (in cattle) 150 pairs of parents
  • 150 probands 2/parents
  • > 5 grand offspring per proband
• detect mutations (6 to 8) incomplete mociacism, 28% of detected mutations occurred in fetus rather than parental gametes.
• detect similar frequencies for female proband as with male proband.
• there exists mosaicism in germ line of parents
• early cell cleavage divisions are more error prone (rapid replication issue?)

Agnela Goncalves, Epigenetic variability in human IPSCs

• large functional and molecular variability
  • reported due to genetic abnormalities
  • cell type of origin
  • culture conditions
  • etc
• are there genetic effects on variability?
• HipSci – goal: create cell bank of stem cells
  • skin biopsy or blood samples from 845 healthy people and 111 rare diseasess
  • reprogrammed into iPSCs
  • validate with transcriptome profiling, compare against previous data.
  • pairwise cnv
  • assay differentiation ability into germ layers (by expression markers)
  • passage 20: assay transcriptome, proteome, chIP-seq histones, methylation pattern,
  • then cell bank.
• sources of phenotypic variation
  • batch, donor, gender, medium, passage number, unexplained residuals
• expression level: 25% variability is by batch effects. a little by donor. medium some effect. more than 50% residual.
• methylation, Nanong Oct4, sox2 etc imaging – dramatic batch effect variation
• by ‘explains most of the variance’ you mean makes the 3rd most signficant contribution after residuals and batch
• 1000s of quantitative trait loci (eQTLs and methylation QTLs)
• this common variatino affects important loci for pluripotency and development
• CD14 is reported as one of t he most epigeneticcally variable genes. 40-80% of variation explained by donor
• most variable fraction of lines in methylation have greatest fraction of variance explained by donors.

Question

• are any of your donor affects related to your healthy vs. rare genetic disease patients?

Maxime Rotivale – Liniing immune responsive regulatory variation and population adaption to pathogen pressure

• ID strong signs of selection on innate immunity genes.
• regulatory variants explain some of these differences.
• 200 participants in Beligium, 100 African 100 European descent
• isolate monocytes
• ID 2000 genes in infulenza response ,350 in antiviral response, 546 in inflammatory response
• MHC response stronger in Europeans, TLR inflamation stronger in Africans
• Balanced allelic expression vs allele favored
  • allele specific events for ~1/3rd of genes
• Allele specific expression QTLs indicate allele specific regulatory SNPs
  • found ASE for ~40% of testable genes.
  • regulatory SNPs identified in 98% of the cases which had >5% of the individuals

Allele specific response to pathogens (envromental effects)

• map allele specific response QTLs?
• do allele specific response QTLs overlap iwth signatures of postive selection?
  • some do: e.g.: ORMDL1 in Europeans
  • LEPROT in Africans.

conclusions

• strong differences in the amplitude of the innate immune response
• both cis and trans differences contribute

Questions

• are there no differences in the prognosis of response to infection between African and Europeon