Evolutionary Genome Biology Gabor T. Marth, D.Sc. Department of Biology, Boston College

Lecture overview 1. Inter-species evolution and comparative genomics 2. Intra-species evolution, population genomics, and human origins

1. Inter-species evolution and comparative genomics Initial sequencing and comparative analysis of the mouse genome Mouse Genome Sequencing Consortium Nature 420,

Questions of Evolutionary Biology What are the taxological relationships between living organisms (which organisms are more or less closely related to each other)? How do genes evolve? How do genomes evolve? How do comparisons with other organisms help us understand our own genome?

Mechanisms of molecular evolution

DNA sequence evolution: mutations

Phylogenetic relationships (1) Higgs and Attwood, Bioinformatics and Molecular Evolution, Blackwell Publishing Multiple alignment of mammalian mitochondrial small subunit rRNA sequences

Phylogenetic relationships (2) Higgs and Attwood, Bioinformatics and Molecular Evolution, Blackwell Publishing Jukes-Cantor distance matrix for mammalian mitochondrial small subunit rRNA sequences

Phylogenetic relationships (3) Higgs and Attwood, Bioinformatics and Molecular Evolution, Blackwell Publishing Phylogenetic tree constructed from mammalian mitochondrial small subunit rRNA sequences

Gene structure evolution: duplications

Gene duplication – paralogs Lander et al. Initial sequencing and analysis of the human genome, Nature, 2001

Evolution of chromosome organization

Synteny Initial sequencing and comparative analysis of the mouse genome Mouse Genome Sequencing Consortium Nature 420,

Gene classes across organisms Lander et al. Initial sequencing and analysis of the human genome, Nature, 2001

Gene conservation across organisms Lander et al. Initial sequencing and analysis of the human genome, Nature, 2001

Comparative genomics helps gene annotations

2. Intra-species evolution, population genomics, and human origins

Questions about human evolution How do we discover / assess genetic variations? What is the level of diversity across humans? How can we model the ancestral and mutation processes? What do phylogenetic analyses of human mitochondrial sequences tell us about human origins and dispersal? Does mitochondrial DNA give us the full picture? What do we learn from model-fitting analysis of nuclear DNA? A single wave of out-of-Africa migration or multiple waves?

Human genetic diversity polymorphism density along chromosomes varies widely average polymorphism rate between a pair of human chromosomes: 1 SNP in 1,300 bp of sequence

What explains heterogeneity? G+C nucleotide content CpG di-nucleotide content recombination rate functional constraints 3’ UTR5.00 x ’ UTR4.95 x Exon, overall4.20 x Exon, coding3.77 x synonymous 366 / 653 non-synonymous287 / 653 Variance is so high that these quantities are poor predictors of nucleotide diversity in local regions hence random processes are likely to govern the basic shape of the genome variation landscape  (random) genetic drift

The origin of genetic variations sequence variations are the result of mutation events TAAAAAT TAACAAT TAAAAAT TAACAAT TAAAAATTAACAAT TAAAAAT MRCA mutations are propagated down through generations and determine present-day variation patterns

Recombination messes up phylogenies acggttatgtaga accgttatgtaga acggttatgtaga accgttatgtaga because of recombination, DNA sequences may not have a unique common ancestor, hence phylogenetic analysis may not apply

What does mtDNA say about human origins? However, the mitochondrion is only a single locus (~16kb, short on the scale of the 3Gb human genome) Campbell and Heyer. Genomics, Proteomics, Bioinformatics. Cummings.

What does nuclear DNA say? Because of recombination, phylogenetic analysis is not feasible (there is not a unique tree that can explain the ancestry of DNA sequences) Instead, one uses statistical “genetic analysis” i.e. one examines the statistical properties of the possible ancestries that produced the nucleotide sequences observed in individuals

Polymorphism data 1. marker density (MD): distribution of number of SNPs in pairs of sequences “rare” “common” 2. allele frequency spectrum (AFS): distribution of SNPs according to allele frequency in a set of samples Clone 1 Clone 2# SNPs AL00675AL AS81034AK CB00341AL SNPMinor alleleAllele count A/GA1 C/TT9 A/GG3

Population genetic models past present stationaryexpansioncollapse MD (simulation) AFS (direct form) history bottleneck

Data fitting: polymorphism density best model is a bottleneck shaped population size history present N 1 =6,000 T 1 =1,200 gen. N 2 =5,000 T 2 =400 gen. N 3 =11,000 Marth et al. PNAS 2003 our conclusions from the marker density data are confounded by the unknown ethnicity of the public genome sequence we looked at allele frequency data from ethnically defined samples

Data fitting: allele frequency present N1=20,000 T1=3,000 gen. N2=2,000 T2=400 gen. N3=10,000 model consensus: bottleneck bottleneck ~ 3,000 generations (or 100,000 years) ago

Data from other human populations European data African data bottleneck modest but uninterrupted expansion Marth et al. Genetics 2004

What nuclear DNA tells us Recent African OriginMultiregional our results