The biological distance and genetic evidence for long-range migration in the prehistoric Midwest Lyle W. Konigsberg Susan R. Frankenberg.

Slides:



Advertisements
Similar presentations
Background The demographic events experienced by populations influence their genealogical history and therefore the pattern of neutral polymorphism observable.
Advertisements

Inference for Regression
1 ANCIENT AND MODERN DNA IN THE AMERICAS: IMPLICATIONS FOR THE PEOPLING OF THE NEW WORLD Frederika A. Kaestle, Ripan S. Malhi, Jason A. Eshleman, David.
Modeling Populations forces that act on allelic frequencies.
Lecture 23: Introduction to Coalescence April 7, 2014.
Plant of the day! Pebble plants, Lithops, dwarf xerophytes Aizoaceae
© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
 Once you know the correlation coefficient for your sample, you might want to determine whether this correlation occurred by chance.  Or does the relationship.
EPIDEMIOLOGY AND BIOSTATISTICS DEPT Esimating Population Value with Hypothesis Testing.
Genetica per Scienze Naturali a.a prof S. Presciuttini Human and chimpanzee genomes The human and chimpanzee genomes—with their 5-million-year history.
Tracing the dispersal of human populations By analysis of polymorphisms in the Non-recombining region of the Human Y Chromosome Underhill et al 2000 Nature.
From population genetics to variation among species: Computing the rate of fixations.
Chapter 7 Sampling and Sampling Distributions
Lecture 24: Thurs. Dec. 4 Extra sum of squares F-tests (10.3) R-squared statistic (10.4.1) Residual plots (11.2) Influential observations (11.3,
TESTING A HYPOTHESIS RELATING TO THE POPULATION MEAN 1 This sequence describes the testing of a hypothesis at the 5% and 1% significance levels. It also.
EC220 - Introduction to econometrics (review chapter)
Hypothesis Testing II The Two-Sample Case.
Chapter 9.3 (323) A Test of the Mean of a Normal Distribution: Population Variance Unknown Given a random sample of n observations from a normal population.
Statistical Analysis A Quick Overview. The Scientific Method Establishing a hypothesis (idea) Collecting evidence (often in the form of numerical data)
F OUNDATIONS OF S TATISTICAL I NFERENCE. D EFINITIONS Statistical inference is the process of reaching conclusions about characteristics of an entire.
Chapter 10: Comparing Two Populations or Groups
Various topics Petter Mostad Overview Epidemiology Study types / data types Econometrics Time series data More about sampling –Estimation.
HYPOTHESIS TESTING. Statistical Methods Estimation Hypothesis Testing Inferential Statistics Descriptive Statistics Statistical Methods.
Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models Mike West Computing Science and Statistics, Vol. 24, pp , 1993.
Week 71 Hypothesis Testing Suppose that we want to assess the evidence in the observed data, concerning the hypothesis. There are two approaches to assessing.
Statistical Hypotheses & Hypothesis Testing. Statistical Hypotheses There are two types of statistical hypotheses. Null Hypothesis The null hypothesis,
Statistics 101 Chapter 10 Section 2. How to run a significance test Step 1: Identify the population of interest and the parameter you want to draw conclusions.
Correlation Assume you have two measurements, x and y, on a set of objects, and would like to know if x and y are related. If they are directly related,
Statistical Inference for the Mean Objectives: (Chapter 9, DeCoursey) -To understand the terms: Null Hypothesis, Rejection Region, and Type I and II errors.
Section 3.3: The Story of Statistical Inference Section 4.1: Testing Where a Proportion Is.
Section A Confidence Interval for the Difference of Two Proportions Objectives: 1.To find the mean and standard error of the sampling distribution.
Lab 7. Estimating Population Structure. Goals 1.Estimate and interpret statistics (AMOVA + Bayesian) that characterize population structure. 2.Demonstrate.
CHAPTER 9 Testing a Claim
Ex St 801 Statistical Methods Inference about a Single Population Mean.
Logic and Vocabulary of Hypothesis Tests Chapter 13.
Retain H o Refute hypothesis and model MODELS Explanations or Theories OBSERVATIONS Pattern in Space or Time HYPOTHESIS Predictions based on model NULL.
1 URBDP 591 A Lecture 12: Statistical Inference Objectives Sampling Distribution Principles of Hypothesis Testing Statistical Significance.
CHAPTER OVERVIEW Say Hello to Inferential Statistics The Idea of Statistical Significance Significance Versus Meaningfulness Meta-analysis.
- We have samples for each of two conditions. We provide an answer for “Are the two sample means significantly different from each other, or could both.
NEW TOPIC: MOLECULAR EVOLUTION.
Education 793 Class Notes Inference and Hypothesis Testing Using the Normal Distribution 8 October 2003.
By Mireya Diaz Department of Epidemiology and Biostatistics for EECS 458.
Lecture 3: MLE, Bayes Learning, and Maximum Entropy
ECE 8443 – Pattern Recognition ECE 8527 – Introduction to Machine Learning and Pattern Recognition Objectives: Statistical Significance Hypothesis Testing.
URBDP 591 A Lecture 16: Research Validity and Replication Objectives Guidelines for Writing Final Paper Statistical Conclusion Validity Montecarlo Simulation/Randomization.
Hypothesis Testing. Statistical Inference – dealing with parameter and model uncertainty  Confidence Intervals (credible intervals)  Hypothesis Tests.
Hypothesis Tests. An Hypothesis is a guess about a situation that can be tested, and the test outcome can be either true or false. –The Null Hypothesis.
Statistical Inference for the Mean Objectives: (Chapter 8&9, DeCoursey) -To understand the terms variance and standard error of a sample mean, Null Hypothesis,
Chapter 8 Introducing Inferential Statistics.
Chapter 5 STATISTICAL INFERENCE: ESTIMATION AND HYPOTHESES TESTING
LECTURE 33: STATISTICAL SIGNIFICANCE AND CONFIDENCE (CONT.)
P-values.
CHAPTER 9 Testing a Claim
Warm Up Check your understanding p. 541
Neutrality Test First suggested by Kimura (1968) and King and Jukes (1969) Shift to using neutrality as a null hypothesis in positive selection and selection.
CHAPTER 9 Testing a Claim
When we free ourselves of desire,
Chapter 9 Hypothesis Testing.
CHAPTER 9 Testing a Claim
Gerald Dyer, Jr., MPH October 20, 2016
Daniela Stan Raicu School of CTI, DePaul University
CHAPTER 9 Testing a Claim
PSY 626: Bayesian Statistics for Psychological Science
CHAPTER 9 Testing a Claim
Lecture 4 Section Wed, Jan 19, 2005
Chapter 7: The Normality Assumption and Inference with OLS
CHAPTER 10 Comparing Two Populations or Groups
CHAPTER 9 Testing a Claim
CHAPTER 9 Testing a Claim
CHAPTER 9 Testing a Claim
Presentation transcript:

The biological distance and genetic evidence for long-range migration in the prehistoric Midwest Lyle W. Konigsberg Susan R. Frankenberg

Goals of Paper 1. Summarize biological distance and genetic evidence for long-range migration in the prehistoric Midwest 2. Address the role of ancient DNA in answering questions about long-range migration

Previous biological distance studies 1. Buikstra (1976) – “The results of the population comparisons suggest that Middle Woodland communities involve relatively stable, long term occupations within a local region.” 2. Reichs (1984) – for Ohio and Illinois Hopewell, “Explanations involving population migrations or significant biological interaction are not indicated.” 3. Sciulli and Mahaney (1986) – “…the present results argue against the hypothesis of large-scale migrations of Hopewell populations from Illinois to Ohio.”

Previous biological distance studies, cont. 4. Konigsberg (1987) – A cowardly approach that only looked at within-site variation 5. Steadman (2001) – “…intraregional population movement was a more significant contributor to Mississippian population structure than interregional gene flow…” 6. Pennefather-O’Brien (2006) – “…biological relatedness could be one aspect of widespread participation in the phenomenon referred to as Hopewell.”

“Block o’ cheese model” (Konigsberg 1990) After removing three northern sites and removing temporal trends Correlation biological distance with river distance= (p=0.006) Correlation biological distance with “time distance” = (p=0.092)

Tiles in upper 40% vector magnitude and divergence no more than 6 degrees Konigsberg & Buikstra (1995)

Oft forgotten problems with quantitative traits 1. One completely heritable quantitative trait is only “worth” one biallelic locus (Rogers and Harpending, 1983). 2. The trace of P -1 G (the “pig matrix”?) gives the equivalence in numbers of biallelic loci (Williams-Blangero and Blangero, 1989). 3. We often assume environmental variance is random with respect to population structure, but…

Benefits of aDNA (mtDNA) 1. From sequence data have one polymorphic locus (e.g., 40 haplotypes from Pete Klunk MW and Hopewell Site) 2. There is no environmental variance to be concerned with.

From Cabana, Hunley, and Kaestle (2008): Population Continuity or Replacement? 1.Unnecessarily complicated because it is couched in a statistical hypothesis testing framework rather than being framed as an estimation problem. 2.Spatial model is probably inappropriate for a river valley (Konigsberg 1987 used a finite linear stepping-stone model)

Lee (2012) Bayesian Statistics: An Introduction “The nub of the argument here is that in drawing any conclusion from an experiment only the actual observation x made (and not other possible outcomes that might have occurred) is relevant. This is in contrast to methods, by which, for example, a null hypothesis is rejected because the probability of a value as large or larger than that actually observed is small…”

What is the migration rate estimated from aDNA data? 1. mtDNA haplogroup data sampled from an ancestral population – Bolnick’s (2005) data on 39 individuals from the Pete Klunk Middle Woodland site. 2. Comparable data from a descendant population – Raff’s (2008) data on 47 individuals from the Schild Mississippian site. 3. Assume a fixed (female) effective population size (of 50) and number of generations (30).

What is the migration rate estimated from aDNA data?, cont. In the infinite island model: After 30 iterations (for 30 generations) check for closeness of model F st to actual F st (0.0492) from aDNA and estimate m (female) = “These go to eleven.” Nigel Tufnel (1984)

Approximate Bayesian Computation (ABC) 1. Draw the migration rate from a uniform prior (0 – 1) 2. Simulate 30 generations of genetic drift (N e = 50) and migration at the sampled migration rate. 3. If the absolute difference between the simulated F st and the actual F st is less than , accept the simulated F st as a draw from the posterior density.

And it works!

And it works! - HORRIBLY

… because there are not enough data

Bolnick and Smith (2007) “...gene flow did accompany the cultural exchange between Middle Woodland communities in the Ohio and Illinois Valleys...not the result of a mass population movement between Ohio and Illinois; rather, it most likely reflected the movement of a small number of individuals each generation.”

Bolnick and Smith (2007), cont. “...the genetic data indicate migration and gene flow primarily in one direction, from Ohio to Illinois. This finding is surprising since no archaeological or morphological studies have proposed this pattern of migration, and Prufer (1964) actually interpreted the archaeological evidence as indicating the opposite...” NemNem MigrateIM Klunk to Hopewell Hopewell to Klunk Total

A D C B X Pete Klunk MW (Bolnick 2005) Hopewell Site (Mills 2003)

From LAMARC

Whither now? 1. Biological distance studies of past populations are likely to become a thing of the past if they are not integrated with aDNA analytical methods. 2. Single locus aDNA studies (mtDNA) may not have the resolution desired for some studies of interest in archaeology. 3. Need better communication between aDNA practitioners and the programmers / population geneticists who develop program packages.