CUrriculum

Monday – Classes from 2-8 pm Berlin time

 


Part 1: Introduce the group, study design, data collection methods


Lecture:

  • Go around the room, introduce ourselves and research interests
  • Discuss study design: DNA sample sources, quality of DNA, sample sizes
  • Discuss data collection methods: whole genome sequencing, capture (exon or mtDNA), RADseq, SNP panel
  • Introduce various file types (fastq, fasta, SAM/BAM, vcf, BED, program specific inputs)

 

Part 2: Data assembly (to reference genome), SNP and haplotype calling


Lecture:

  • Accessing reference genomes, understanding their quality, “in-group” reference bais
  • General introduction to de novo approaches
  • General introduction to the idea of SNP calling

 

Practical:

  • Looking at the files associated with a reference genome, indexing it
  • Use BWA to assemble some reads to the genome
  • Check quality of assembly, filter BAM
  • Call SNPs using BCFtools mpileup/call, output VCF

 

 

 

 Tuesday– Classes from 2-8 pm Berlin time

 

 

 Part 1: Data filtering (depth, quality, repetitive regions of the genome, linkage, HWE)


    Lecture:

  • SNP filtering: basic filters, importance of ordering filters, iterative process
  • Selecting thresholds for missing data per individual, per population, per locus

    Practical:

  • Filtering dataset using VCFtools
  • LD with PLINK
  • Generating summaries of missingness, depth etc.


    Part 2: Population structure analyses


    Lecture:

  • Data quality assessment
  • Importance of population structure in conservation studies
  • Pros/cons of the different methods
  • Best practices for analysis and reporting for each of the methods

    Practical:

  • Examine test data using PCA & DAPC (compare clear structuring with weak structuring)
  • Population structure analsysis

 

 

 

 Wednesday– Classes from 2-8 pm Berlin time

 

Part 1: Adaptation


Lecture:

  • Why conservation studies may want to assess signals of adaptation
  • Outlier detection, environmental correlations

    Practical:

  • Run test data using outflank, RDA and LFMM
  • Interpret outputs, compare neutral vs. adaptive population structure

 
Part 2: Estimating effective population size

 

 

 

 

 Thursday– Classes from 2-8 pm Berlin time

 

Part 1: Inbreeding estimates (ROH, Fis)


Part 2: Relatedness


Lecture:

  • Introduce the variety of estimators
  • Importance of having an independent estimate of allele frequencies.
  • Uses for relatedness (relatedness by distance, checking power of SNP panel, captive breeding).

Practical:

  • Using the R package “related” https://rdrr.io/rforge/related/man/related-package.html
  • Analyze test data (estimate relatedness, selecting the best estimator, simulating relatedness categories)
  • Plotting relatedness distributions