Lecture 9+10.docx

7 Pages
Unlock Document

University of Toronto St. George
Ecology & Evolutionary Biology
Stephen Wright

Lecture 9+10: Human mutation load  Mutations happen, most are deleterious and thus all populations contain deleterious mutations  At mutation-selection balance:  Frequency of deleterious allele (q) = u/s  The mean fitness depend on mutation rate: equilibrium frequency of deleterious allele = u/hs. If mutation is really deleterious there will not be many at equilibrium, its average effect is rather small. If a minor deleterious allele will have more copies.  Thus selection will either make the effect really big and amount of alleles really small, or the bigger effect on the mean fitness due to the large amount of copies but small individual effect  W=1-2u, W is almost equal to 1. Only 1 gene and if multiple by all genes then the result is rather big.  Selection decreases the number of mutations and mutation increases the number of mutations  Haldane: Weq = e^-U, U is the average number of new deleterious mutation rate each time the offspring is produced, per generation  Mutation load: extent by which mean fitness is reduced due to mutation. L=1-e^-U  If there was 1 new bad mutation per generation, then the Weq = 0.37. The accumulative effect of rare alleles in the entire population  If there were 3 bad mutations per generation, then Weq will be 0.05  If we have such a high mutation load, how are we a thriving species? 1. The prediction is right: we are a lot less fit than we could be 2. The prediction is right; however it does not really affect our productivity as a species. We are mainly limited by competition with each other, others have bad alleles also! 3. The prediction is wrong as some of the simplifying assumptions used in the model are violated, however he gives a decent estimate and the simplifying assumptions might not even make the load smaller  Phenomena that may be influenced by deleterious mutations: inbreeding depression, variation in fitness (health) and genome size and complexity  Evidence of deleterious alleles: 1. Explicit genetic disease  Type 2 diabetes, breast cancer, myopia and migraine etc.  Perhaps 50% of the highest death rates (heart disease, cancer etc.) have genetic factor, 42.5% of deaths due to deleterious mutations 2. Inbreeding depression  Inbreed individuals tend to be less fit than outbreed individuals  Most deleterious alleles are at least partially recessive. The a allele may be rare in the population but common in the family, thus when 2 close members mate then the deleterious recessive allele will be passed on, increase the chances of producing homozygous offspring  Fruit fly: 71% less fit  Mouse: 100% less fit  Beetle: 37% ID  Flower: 83% less fit  Humans: 70% ID, usually first cousin mating and extrapolate them into full inbreeding 3. Loss of Function  Typical individual have: 190-210 in-frame, 80-100 nonsense, 40-50 splice site disrupting, 220-250 off-frame deletions  340-400 assumed LOF mutations per individual  Estimated that each genome is heterozygous for 50-100 variants classified by the database as causing inherited disorders.   However a later (2012) study estimated 100 high confidence LOFs, after eliminating false positives  LOF genes: less evolutionary conserved, thus higher than average values of Ka/Ks.  High Ka/Ks can be due to adaptive evolution or relaxed selection  Lower than average conservation in promoter regions  LOFs have more closely related genes (paralogs) than other genes: perhaps more functional redundancy  Compared to derived variants (from ancestral) at synonymous sites, high confidence LOFs should be rarer,  In 10% of synonymous sites variation, the derived allele is at a frequency of 1% or less. 20% the DA is at 1-10%. High confidence LOF has more than 40% exist at a frequency of less than 1%, another 30% of LOF exist at 30% and less. We have more of the rare LOFs and less of the common LOFs  Compared to derived synonymous sites, a much greater proportion LOFs are rare  Total Mutation Rate in Humans:  Recent estimates of mutation rate in humans come from direct sequencing of mother-father-offspring trios  On average there is around 60 new mutations per offspring  On average, a mother contributes around 14 new mutations to each offspring, regardless of her age  A 20 year old dad contributes 25 new mutations to each offspring; a 40 year old dad contributes 65 new mutations to each offspring. Average increase of 2 new mutations to each offspring  U=1.8 deleterious mutations (M times F times C)  M=60m total amount of new mutations per offspring, which ones are deleterious??  F=0.55, fraction of genome composed of sequence other than TE remnants and pseudogenes (neutral reference)  C=5.4%, constraint in non-synonymous sites, although constraint is 77% most of the genome is non-coding, and thus smaller constraint overall  25% of new mutations are non-synonymous mutations, the other 75% are probably regulatory elements  Most of the genome consists of something other than replacement sites and there is evidence of some constraint on these sites too, but weaker  5% of non-coding sequence is constrained by selection.  There is at least 3 times as many functionally important noncoding sites as coding sites (including synonymous sites)  Thus if U =1.8, then Weq = e^-1.8 = 0.16, a mutation load of over 84%, 84% less fit!!   Deleterious SNPs per person:  The equilibrium frequency of a deleterious allele at a single locus is q=u/hs  Genome-wide mean number of a deleterious allele is X=U/hs, U is the genome wide rate of deleterious mutation  If we just consider NS
More Less

Related notes for EHJ352H1

Log In


Don't have an account?

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.