1、人类群体遗传学基本原理和分析方法,中科院-马普学会计算生物学伙伴研究所,中国科学院上海生命科学研究院研究生课程 人类群体遗传学,徐书华 金 力,20082009学年第二学期人类群体遗传学分析方法课程表上课时间：每周四上午10:00-11:50 上课地点：中科大厦4楼403室第7教室,第二讲,遗传多态性统计量,第二讲,遗传多态性的概念遗传多态性的种类描述遗传多态性的统计量群体遗传多态性参数（）的估计利用群体遗传多态性数据进行统计检验Tajima test,Polymorphism,Light-morph Jaguar (typical),Dark-morph or melanistic Jagu
2、ar (about 6% of the South American population),http:/en.wikipedia.org/,Polymorphism,56 ethnic groups in China,Human Genetic Diversity,Science 319:1100 (2008),Polymorphism,Greek: poly = many, and morph = formPolymorphism is often defined as the presence of more than one genetically distinct type in a
3、 single population.Rare variations are not classified as polymorphisms; and mutations by themselves do not constitute polymorphisms.,Sexual dimorphism,Why is the ratio 50/50?,DNA polymorphism,RFLP (Restriction Fragment Length Polymorphism) AFLP (Amplified Fragment Length Polymorphism) RAPD (Random A
4、mplification of Polymorphic DNA) VNTR (Variable Number Tandem Repeat, or Minisatellite) STR (Short Tandem Repeat, or Microsatellite) SNP (Single Nucleotide Polymorphism) SFP (Single Feature Polymorphism)CNV (Copy Number Variation),Intuitive statistics,Number of allelesMore alleles, larger diversity;
5、Minor allele frequency (MAF) is the frequency of the less (or least) frequent allele in a given locus and a given population.,Human SNP data,A Single Nucleotide Polymorphism (SNP) (snip) is a single base variant in DNA. Mutation: minor allele frequency (MAF) 1%SNP: MAF 1%SNPs are the most simple for
6、m and most common source of genetic polymorphism in the human genome (90% of all human DNA polymorphisms).,Heterozygosity,The fraction of individuals in a population that are heterozygous for a particular locus. It can also refer to the fraction of loci within an individual that are heterozygous.,wh
7、ere n is the number of individuals in the population, and ai1, ai2 are the alleles of individual i at the target locus.,Observed,where m is the number of alleles at the target locus, and fi is the allele frequency of the ith allele at the target locus.,Expected,Heterozygosity related issues,Heterozy
8、gosity and HWDComparison of Ho and HeGene diversity,Population Mutation Rate (q ),Under mutation-drift equilibrium:q = 4Nem for autosomeq = Nem for Y and mtDNAq = 3Nem for X chromosome,qautosome qX qY,Estimators of ,Number of segregating sites (K);Average pairwise differences ();Number of alleles (E
9、);Mean number of mutations since the MRCA ();Singleton.,Under the infinite site model, K is equal to the number of mutations since the most recent common ancestor of the sequences in the sample. Therefore, K has a clear biological meaning.However, K depends on the sample size.,Number of segregating
10、sites (K),Normalized K,Under the neutral Wright-Fisher model with constant effective population size,The properties of K,K is independent of sample size.However, the usefulness of K is not clear under other population genetic models, such as those with natural selection.K is sensitive to the number
11、of rare alleles, or mutants of low frequency.,How many common SNPs in human genome?,Common SNPs: minor allele frequency (MAF) 0.05;Suppose we have 50 samples of African, European, Asian respectively;Theta=1.2/kb for African population;Theta=0.8/kb for European and Asian population;Autosome length (L
12、)=2.68 billion bp;,We expect 9.8 million common SNPs in 50 African samples;We expect 6.5 million common SNPs in 50 European samples;We expect 6.5 million common SNPs in 50 Asian samples;,where,ThetaK=1.2/kb,ThetaK=0.8/kb,Average pairwise differences (),Also known as sequence diversity mean number of
13、 nucleotide differences between two sequences.,The properties of , as a measure of genetic variation has clear biological meanings which do not depend on the underlying evolutionary process.In comparison to K, it is insensitive to the rare alleles, or mutants of low frequency. is an useful measure o
14、f persistent genetic variation, and neutral genetic variation when purifying selection is operating.However, because its variance is considerably larger than that of K, it is not as good as K for neutral locus.,Locus (length)p(x10-4)q(x10-4)m(x10-9) Ne ReferenceAPOE (5.5kb) 5.36.87(S) 23.5 7,300Full
15、erton et al. 2000Chr.1 (10kb) 5.89.51(S) 14.816,000Yu et al. 2001Chr.22 (10kb) 8.8 13.2 (S) 2314,400Zhao et al. 2000X chr. (10.2kb) 3.66.8 (S) 18.412,300Kaessmann et al. 1999X chr. (4.2kb) -4.41(ML) 19.2 7,700Harris & Hey 1999Y chr. (64kb) 0.742.01(S) 24.8 8,100Thomson et al. 2000mtDNA (15.4kb) 28 2
16、8(p) 340 8,200Ingman et al. 2000Alu insertions - -17,500Sherry et al. 1997,Nucleotide Diversity,Number of alleles,Ewens (1972) shows that under the infinite allele model,An estimate of can be obtained by resolving the above equation for with E(k) replaced by k. The estimate is known as Ewenss estima
17、tor E.,The properties of E,Under the infinite allele model, E is about the best estimator one can devise.However, E is slightly upward biased estimator particularly when is large.,Mean number of mutations since the MRCA (),The mean number of mutations since the most recent common ancestor (MRCA) of
18、a sample is another intuitive summary statistic, but seldom used in practice.This is probably partly due to that its use requires knowing for each segregating site the ancestral nucleotide, and partly because its because its statistical properties are not well understood.,Let l be the number of muta
19、tions in sequence l since MRCA.Then the average is given by,Note that a mutation of size i is counted as one mutation in i of n sequences, we therefore have,It follows that,Singleton mutations,The number i of mutations of size 1 in a sample is of special interest because it captures mostly the recen
20、t mutations in a sample.According to Fu and Li (1993),Classify the above summary statistics,0,0 = K1,1 =1,0 =,Weight of k,l statistics,Distribution of ,A sample of 100 from a population with =5.,Neutral hypothesis as the null model,Whether a locus has been evolving under natural selection is often o
21、f interest if the locus represent a gene or linked to one. As typical in many branches of sciences, a simpler explanation of phenomenon is often preferred unless there is strong evidence to suggest otherwise. In population genetics study, the neutral hypothesis of evolution is arguably simpler than
22、any other hypotheses and is much better understood statistically. As a result, it is now generally used as the null model for analyzing polymorphism. A significant deviation from the null model may signal the presence of forces that are absent or factors that are over-simplified in the null model.,S
23、tatistical tests usingestimators of ,There are several ways statistical tests can be constructed to see if the null model is adequate for explaining the observed amount and pattern of polymorphism.Many summary statistics (estimators of ) have quite different expectation when the null model is violat
24、ed, this offer an opportunity of testing by considering the difference between two measures of polymorphism.,Suppose L1 and L2 are two different summary statistics such that E(L1) =E(L2) under the hypothesis of strict neutrality. Then one way to test the null hypothesis of strict neutrality is to us
25、e the normalized difference,as test statistic. Normalization is intended to minimize the effect of unknown parameter(s) so that the resulting test is more rigorous.Note that V ar(L1L2) is a function of so its value needs to be estimated.,Although every pair of statistics L1 and L2 can be used to con
26、struct a test as long as E(L1) = E(L2) and V ar(L1L2) can be computed, such a test is useful only if the values of L1 and L2 are likely different when the locus under study depart from neutrality.Unfortunately the distribution of a test of the form above is not well approximated by any standard dist
27、ribution, so that obtaining critical values from a large number of simulated samples is commonly used, which means that the best way to apply such tests is to use a computer package that implement the test. Therefore, we will focus on discussing the rational of several tests rather than detail of th
28、eir computations.,Tajima test,the parameter required for computing the variance is estimated by K/an.,Rational of Tajima test,Since K ignores the frequency of mutants, it is strongly affected by the existence of deleterious alleles, which are usually kept in low frequencies. In contrast, is not much
29、 affected by the existence of deleterious alleles because it takes the frequency of mutants into consideration. Therefore, a D value that is significantly different from 0 suggests that the null hypothesis should be rejected.,Indication of Tajimas D,When a population has been under selective sweeps
30、(and population growth), K/an will likely be larger than , resulting in negative value of D. When a population has been under balance selection (or population structure with sampling from many populations), K/an will likely be smaller than , resulting in positive value of D.,Tajimas D Expectations,N
31、eutrality: D=0Balancing Selection: D0Divergence of alleles () increasesPurifying or Positive Selection: D0 (S decreases)Population expansion: D0 (Divergence of alleles decreases: many low frequency alleles),常用软件,DnaSphttp:/www.ub.es/dnasp/PAMLhttp:/abacus.gene.ucl.ac.uk/software/paml.htmlArlequinhttp:/anthro.unige.ch/software/arlequin/,
