Musa_ABBA_BBAA_Introgession

Introduction

Hybridization between species represents a major force of evolution, influenced by the external element, as glacial period, migration species or climatic changes, The genome can evolved by providing material for adaptation by natural and or sexual selection. Hybridization can decrease the difference between two species by sharing alleles across the genome, but can also act as a source of variation, impacting adaptation, aiding in evolutionary rescue, promoting range expansion, leading to species divergence, and finally fueling adaptative radiation.

Since the beginning of next generation sequencing, and because of the decreasing cost, the number of whole-genome sequencing increase. Associated to new statistical methods to detect the signature of hybridization at the whole genome or chromosome level, genome sequencing technic provide an information patterns (SNP) across a tree as markers of hybridization.

Purpose of Musa_ABBA_BBAA_Introgession

Due to the explosive expansion in genomic resources, scientist have developed several statistical tests to detect introgression. Patterson’s D-statistic also known as the ABBA-BABA test was developed to quantify the amount of genetic exchange. It considers of an ancestral “A” allele and derived “B” alleles via mutation across the genome of four taxa. Under the hypothesis “without introgression” the two allelic patterns “ABBA” or “BABA” occurred with equal frequency (((A,B))B)A) = (((B,A))B)A). An excess of “ABBA” or “BABA” shown by a D-statistic significantly different from zero indicate a gene flow between two taxa. A D-Statistic > 0 means an excess of ABBA indicates an introgression between population P2 to population P3, provided that P1 and P3 are not exchanging gene flow. Whereas D-Statistic < 0 which is an excess of BABA indicate an introgression between P1 and P3. To detect potential past hybridization, we used the ABBA-BABA test ( Martin et al., 2015) Patterson’s D in https://github.com/simonhmartin/genomics_general.  

Patterson’s D test is D = [sum(ABBA) – sum(BABA)] / [sum(ABBA) + sum(BABA)] with ABBA = (1- p1 ) x p2 x p3 x (1- pO ), and BABA = p1 x (1- p2 ) x p3 x (1- pO ). To compute the standard error (Green et al 2010), we used the block jackknife approach. The number of ABBA, BBAA BABA sites was calculate with the workflow suite of Martin described in http://evomics.org/learning/population-and-speciation-genomics/2018-population-and-speciation-genomics/abba-baba-statistics.

Workflow - Calculate ABBA_BBA Python test Simon Martin Ref

**Workflow - Obtain a vcf file **

Input files : vcf file

The procedure to obtain the vcf file is described here https://github.com/CathyBreton/Genomic_Evolution.

 

 

The vcf file previously obtained with GATK version 4 was transformed in a geno file with the script parseVCF.py, some filters --minQual=20 and flag=DP min=5 were applied. On the genotype file, freq.py was used to calculate de frequency of each allele of each population. All the population P1, P2, P3 and outgroup were defined to test the hypothesis on banksii introgression in Papouasie New Guinea (PNG). To calculate the ABBA-BBAA number per windows the script ABBABABAwindows.py was applied with a windows of 10Mb. To compute the variance of D despite non-independence among site we used the jackknife. The block size needs to exceed the distance at which autocorrelation occurs, then we choose 10Mb. and then to calculate the error and Z score, we used the script calculate_abba_baba_Musa.r adapted to our data.

Dependencies

The tools are developed in Perl, bash, Python3, Java and work on the Linux system and require:

Tools	Website	Version
Bamtools	https://github.com/pezmaster31/bamtools	bamtools/2.4.0
BWA	http://bio-bwa.sourceforge.net	bwa/0.7.12
Cutadapt	https://cutadapt.readthedocs.io/en/stable/	cutadapt/2.10
FastQC	https://www.bioinformatics.babraham.ac.uk/projects/fastqc/	FastQC/0.11.7
GATK V4	https://software.broadinstitute.org/gatk/	GenomeAnalysisTK/4.1.9.0
GATK V3	https://software.broadinstitute.org/gatk/	GenomeAnalysisTK/3.7-0
Picard Tools	https://broadinstitute.github.io/picard/	picard-tools/2.7.0
sambamba	https://lomereiter.github.io/sambamba/	sambamba/0.6.6
Samtools	https://github.com/samtools/samtools	samtools/1.2
STAR	https://github.com/alexdobin/STAR	STAR/2.5.0b
VCFHunter	https://github.com/SouthGreenPlatform/VcfHunter
Vcftools	https://vcftools.github.io/index.html	vcftools/0.1.14

Ref

Martin, S. H., Davey, J. W., and Jiggins, C. D. (2015). Evaluating the Use of ABBA–BABA Statistics to Locate Introgressed Loci. Molecular Biology and Evolution 32, 244–257. doi: 10.1093/molbev/msu269. https://github.com/simonhmartin/genomics_general.

How to cite

Sardos J, Breton C, Perrier X, Van den Houwe I, Carpentier S, Paofa J, Rouard M and Roux N (2022) Hybridization, missing wild ancestors and the domestication of cultivated diploid bananas. Front. Plant Sci. 13:969220. https://doi.org/10.3389/fpls.2022.969220

 

 

If you use the second workflow.

Breton Catherine, Cenci Alberto, Sardos Julie, Chase Rachel, Ruas Max, Rouard Mathieu & Roux Nicolas (2022). A Protocol for Detection of Large Chromosome Variations in Banana Using Next Generation Sequencing. In: Jankowicz-Cieslak, J., Ingelbrecht, I.L. (eds) Efficient Screening Techniques to Identify Mutants with TR4 Resistance in Banana. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64915-2_9

 

Authors and acknowledgments

This work is a collaborative work between Catherine Breton, Mathieu Rouard with the Julie Sardos, .

Contact

Catherine Breton, Alliance of Bioversity International and CIAT Europe (c.breton@cgiar.org)

The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve people’s lives in a climate crisis. The Alliance is part of CGIAR, a global research partnership for a food-secure future. https://www.bioversityinternational.org/
https://www.ciat.cgiar.org