Molecular Data• Molecular vs. morphological methods

• Morphological characters are not bad characters and they have certain advantages• Easy to obtain and measure, extinct taxa, fossil record is more readily datable.• Also disadvantages:• Environmental influences (phenotypic plasticity)• Ambiguous modifiers in the literature (“somewhat reduced,” “slightly elongated”)• More often continuous• Unpredictable evolution • Only relatively close relationships can be inferred in many cases• Limited number of characters, especially in some taxa

• Advantages of molecular data• Molecular data are genetic

• Phylogeny is predicated on evolutionary relationships – a direct result of genetic change and genealogy• We can look directly at the genetic source(s) for a character• We know that the subject of our observation is heritable

• Molecular methods increase the scope of our observations• All organisms utilize nucleic acids and can thus be compared in some way• Increases the number of simultaneous comparisons we can make• Increases the range of comparisons we can make

Molecular Data•Advantages of molecular data cont.

• Nearly unlimited pool of potential markers• A single 300bp DNA sequence encompasses up to 300 potential characters• Any two humans differ at ~0.1% of nucleotide sites (~3 million potential differences)• Markers are available for a range of time frames and levels of relation• We can look directly at the genetic source(s) for a character

• Homology assessment• Homology is vital to phylogenetic inference• Homology among molecules is relatively easy to assign (but not always)• Homology among morphological characters may or may not be derived from the same genetic heritage

• A common yardstick for measuring divergence• Some evolutionary models predict constant rates of evolutionary change among molecules• If this holds true, genetic divergence is a function of time • Can be harnessed to measure divergence times, rates of evolution, rates of mutation, among even vastly different taxa

Molecular Data•Advantages of molecular data cont.

• Allows for a mechanistic appraisal of evolution• Changes in morphological characters can now be investigated with regard to the underlying molecular basis

• New perspectives can be gained through molecular approaches• Examples:• Asexual transmission – parthenogenetic lizards• Endosymbiont hypothesis• Matrilineal and patrilineal genealogies

Molecular Data•Disadvantages of molecular data

• High cost• Sometimes limits the number of samples and loci that can be evaluated• Costs continue to go down rapidly, however

• Convergent evolution and homoplasy• Extinct taxa• Homology assignment

• Gene duplication, genome duplication, pseudogenes, numts, etc.

Molecular Data• A (very) brief history of molecular methods • 1904 – Serological differences used to investigate organismal similarity

• What happens when an antibody specific for a protein from one organism is mixed with the same protein from a different organism?

Molecular Data• A (very) brief history of molecular methods •1960’s – Allozyme electrophoresis

• The first molecular approach to receive widespread application in systematics. • Based on the fact that different alleles for enzymatic proteins may have different electrophoretic mobilities due to differences in protein net charge (due to amino acid differences), or differences in protein conformation. • Many potential differences between genes that will not be detected by this approach (e.g, silent substitutions; amino acid replacements that do not change net charge).• One can be confident that proteins of different electrophoretic mobility are in fact different.• One CANNOT assume that proteins of the same mobility are actually identical in amino sequence (or that their underlying DNA sequences are the same).

Molecular Data• A (very) brief history of molecular methods • 1970s and 80s – DNA hybridization

• Directly analyze similarity of DNA molecules among taxa• Similar DNA from diverse taxa can be mixed and form heteroduplexes• The greater the match, the higher the melting temperature (Tm)• Differences in peak Tm can be used as genetic distances• Sibley and Ahlquist

Molecular Data• A (very) brief history of molecular methods • 1970s – early 1990s

• mtRFLP – Mitochondrial Restriction Fragment Length Polymorphisms• also applied to plant organelles• out of style after whole/partial mtgenome sequence became widely feasible

Molecular Data• A (very) brief history of molecular methods • 1975 – Revolution #1 – DNA sequencing

• Sanger (dideoxy) sequencing allowed for the direct examination of the DNA sequence

• 1985 – Revolution #2 – Polymerase chain reaction•Allowed for the production of huge amounts of a single fragment in a short time• Automation of the Sanger sequencing process

•PCR and sequencing allowed numerous, diverse molecular markers to be employed

• DNA fingerprinting, RAPDs, RFLP, AFLP, transposable elements, etc.

•By far, the most prevalent technique for phylogeny inference is DNA sequencing via PCR

• 1980s – mitochondrial DNA • 1990s – nuclear loci• 2000s – whole genome comparisons

•~2005 – Revolution #3 – Next generation sequencing• Allowed for the generation of MASSIVE amounts of data including whole genome phylogenetics

Molecular Data• Molecular markers in phylogenetic analyses• The older ones

• Protein electrophoresis – Allozymes• cheap and easy • good for relatively shallow nodes• problems with knowing source of polymorphism• much more precise methods are available

• Protein immunology • similar to protein electrophoresis • better for deeper nodes

• DNA-DNA hybridization • very broad levels of information• good for a broad range of relationships

• mtDNA RFLP• cheap and easy • good for broad range of relationships • problems with knowing source of polymorphism• much more precise methods are available

Molecular Data• Common modern molecular markers in phylogenetic analyses

• Micro- and minisatellites - tandem repeats• Highly polymorphic• Not great for phylogenetic analysis – too polymorphic, no well established evolutionary model

• SINEs/retrotransposons• Broadly applicable• simple evolutionary model• excellent for shallow and deep nodes• must have either a reference genome or be able to commit substantial monetary resources

• nDNA RFLP• cheap and easy PCR based • good for broad range of relationships • problems with knowing source of polymorphism• much more precise methods are available• modern NGS methods have changed this into a technique called RAD-Seq

Molecular Data• Common modern molecular markers in phylogenetic analyses

• mtDNA sequencing• Broadly applicable• Well established nucleotide substitution models• excellent for shallow nodes, some regions good for deeper nodes• cheap and easy PCR based; preamplified template – easy to use• numts and orthology

• nDNA sequencing• cheap and easy PCR based • good for broad range of relationships • orthology is imperative and sometimes difficult to establish

• We will be spending most of the rest of our time on sequence based analyses

• Mutation–Types of mutations

• Large scale – chromosomal aberrations• Small scale

–Base substitutions– Indels (insertions or deletions)–Somatic vs. germline

Molecular Data

• Mutation– Sources:

• Radiation• Most single nucleotide changes are the result of endogenous

mutations– DNA replication and repair errors– ~1017 replications during a normal human lifespan – Each cell division requires incorporating 6 x 109 bases– Replication errors occur at ~10-10/nucleotide– Any given gene may be the site of ~109 mutations when considering

all cells and all cell divisions– Most of these mutations are inconsequential in the short and long

runs – why?

Molecular Data

• Types of base substitutions• Transitions vs. transversions

• Would you expect more transitions or transversions by chance?• Transition bias

• Can be permanent if not repaired immediately

A G

C T

Molecular Data

• Mutation–Types of single nucleotide mutations

• Mutations in coding DNA–Synonymous – silent

»Do not change the gene product»Degenerate genetic code

–Nonsynonymous – change the gene product–Neutral – can be synonymous or nonsynonymous but more

likely to be synonymous»Amino acid mutabilities»Where is the amino acid? Active site, spacer?» Involved in secondary structure?»Same chemical properties or different?

Molecular Data

Substitution rates vary throughout the genome.

Think about it: Is it really substitution rates that vary throughout the genome?

Molecular Data

• Mutation–Types of single nucleotide mutations

• Mutations in coding DNA–Nondegenerate sites – codon positions where all possible

substitutions are nonsynonymous »The second base of all codons»65% of all codon positions

–2-fold degenerate sites – codon positions where one of the three possible substitutions is synonymous

»19% of codon positions–4-fold degenerate sites – codon positions where all possible

substitutions are synonymous»16% of all codon positions

Molecular Data

11_02.jpg

• Mutation and gene components

Molecular Data

• Selection and substitution rates– How do we tell if a gene has been influenced by natural

selection?• Ks = rate of synonymous substitutions• Ka = rate of nonsynonymous substitutions• If gene is not under selection (“not important”) what will be

relationship between Ka and Ks?– Ka = Ks, Ka/Ks = ~1

• If gene is under selection (“important”)?– Ka/Ks < 1 (stabilizing/purifying); Ka/Ks > 1 (positive/directional)

Rate of Evolution in Brain-Expressed Genes in Humans

Molecular Data

Molecular Data

• A higher mutation rate in males vs. females?– JBS Haldane 1947

Molecular Data

• A higher mutation rate in males vs. females?• However…..• Nature June 12, 2011 – “Variation in genome-wide mutation rates within

and between human families”– J.B.S. Haldane proposed in 1947 that the male germline may be more mutagenic than

the female germline. Diverse studies have supported Haldane’s contention of a higher average mutation rate in the male germline in a variety of mammals, including humans. Here we present, to our knowledge, the first direct comparative analysis of male and female germline mutation rates from the complete genome sequences of two parent-offspring trios. Through extensive validation, we identified 49 and 35 germline de novo mutations (DNMs) in two trio offspring, as well as 1,586 non-germline DNMs arising either somatically or in the cell lines from which the DNA was derived. Most strikingly, in one family, we observed that 92% of germline DNMs were from the paternal germline, whereas, in contrast, in the other family, 64% of DNMs were from the maternal germline. These observations suggest considerable variation in mutation rates within and between families.

Molecular Data

• Criticisms of these observations?– Sample size, replicability?

• If these results hold up, what does that mean with regard to whether we can trust science in general?• Sample size, replicability?• The “science is constantly changing” complaint• The difference between change and refinement• If you’re presented with new facts indicating that you’re wrong about something, do

you continue with your prior thinking or change your mind?

Molecular Data

• Sex-specific differences in mutation rates

Molecular Data