DOI: 10.5061/dryad.4r7b826
title: Data from: Broad geographic sampling reveals predictable, pervasive, and strong seasonal adaptation in Drosophila

description: To advance our understanding of adaptation to temporally varying selection
 pressures, we identified signatures of seasonal adaptation occurring in
 parallel among Drosophila melanogaster populations. Specifically, we
 estimated allele frequencies genome-wide from flies sampled early and late
 in the growing season from 20 widely dispersed populations. We identified
 parallel seasonal allele frequency shifts across North America and Europe,
 demonstrating that seasonal adaptation is a general phenomenon of
 temperate fly populations. Seasonally fluctuating polymorphisms are
 enriched at large chromosomal inversions and we find a broad concordance
 between seasonal and spatial allele frequency change. The direction of
 allele frequency change at seasonally variable polymorphisms can be
 predicted by weather conditions in the weeks prior to sampling, linking
 the environment and the genomic response to selection. Our results suggest
 that fluctuating selection is an important evolutionary force affecting
 patterns of genetic variation in Drosophila.
description: VCF of all SNPs (limited
 filtering): chrX We called SNPs using the
 program VarScan v2.3.8 using a p-value of 0.05, minimum variant frequency
 of 0.005, minimum average quality of 20, and minimum coverage of 10
 (Koboldt et al. 2012). We filtered out SNPs within 10bp of an indel (they
 are more likely to be spurious), variants in repetitive regions
 (identified by RepeatMasker and downloaded from the UCSC Genome browser)
 and nucleotides with more than two alleles. See Supplemental Table 1 for
 mapping of column headers to population information. mel_X_mapping2016.varscanSNP_noindel_dpfilter_biallelic_repeatmask.recode.vcf.gz   VCF of all SNPs (limited filtering): chr2L We called SNPs using the program VarScan v2.3.8 using a p-value of 0.05, minimum variant frequency of 0.005, minimum average quality of 20, and minimum coverage of 10 (Koboldt et al. 2012). We filtered out SNPs within 10bp of an indel (they are more likely to be spurious), variants in repetitive regions (identified by RepeatMasker and downloaded from the UCSC Genome browser) and nucleotides with more than two alleles. See Supplemental Table 1 for mapping of column headers to population information. mel_2L_mapping2016.varscanSNP_noindel_dpfilter_biallelic_repeatmask.recode.vcf.gz VCF of all SNPs (limited filtering): chr3L We called SNPs using the program VarScan v2.3.8 using a p-value of 0.05, minimum variant frequency of 0.005, minimum average quality of 20, and minimum coverage of 10 (Koboldt et al. 2012). We filtered out SNPs within 10bp of an indel (they are more likely to be spurious), variants in repetitive regions (identified by RepeatMasker and downloaded from the UCSC Genome browser) and nucleotides with more than two alleles. See Supplemental Table 1 for mapping of column headers to population information. mel_3L_mapping2016.varscanSNP_noindel_dpfilter_biallelic_repeatmask.recode.vcf.gz VCF of all SNPs (limited filtering): chr3R We called SNPs using the program VarScan v2.3.8 using a p-value of 0.05, minimum variant frequency of 0.005, minimum average quality of 20, and minimum coverage of 10 (Koboldt et al. 2012). We filtered out SNPs within 10bp of an indel (they are more likely to be spurious), variants in repetitive regions (identified by RepeatMasker and downloaded from the UCSC Genome browser) and nucleotides with more than two alleles. See Supplemental Table 1 for mapping of column headers to population information. mel_3R_mapping2016.varscanSNP_noindel_dpfilter_biallelic_repeatmask.recode.vcf.gz VCF of all SNPs (limited filtering): chr2R We called SNPs using the program VarScan v2.3.8 using a p-value of 0.05, minimum variant frequency of 0.005, minimum average quality of 20, and minimum coverage of 10 (Koboldt et al. 2012). We filtered out SNPs within 10bp of an indel (they are more likely to be spurious), variants in repetitive regions (identified by RepeatMasker and downloaded from the UCSC Genome browser) and nucleotides with more than two alleles. See Supplemental Table 1 for mapping of column headers to population information. mel_2R_mapping2016.varscanSNP_noindel_dpfilter_biallelic_repeatmask.recode.vcf.gz Allele frequency and dp per SNP Allele frequency and read depth (adjusted for number of individuals in pool) for each sample and SNP. This R object is used in downstream analyses. mel_freqdp_042016_Ne_fixed_correctBAVI.Rdata Matched controls Matched controls, with each SNP matched by chromosome, inversion status, recombination rate, and spring allele frequency. 100 sets of matched controls. bootstrap_fmean_dp.mel.medfreq01_RRgrt0.recRate.polymorphic.txt  
description: We assembled 73 samples of <i>D. melanogaster</i>, 61
 representing newly collected and sequenced samples and 12 representing
 previously published samples (Bergland et al., 2014; Kapun et al., 2016).
 Locations, collection dates, number of individuals sampled, and depth of
 sequencing for all samples are listed in Supplemental Table 1 (Machado et
 al., 2021). For each sample, members of the Drosophila Real-Time Evolution
 Consortium collected an average of 75 male flies using direct aspiration
 from substrate, netting, or trapping at orchards and residential areas.
 Flies were confirmed to be <i>D. melanogaster</i> by
 examination of the male genital arch. We extracted DNA by first pooling
 all individuals from a sample, grinding the tissue together in extraction
 buffer, and using a lithium chloride – potassium acetate extraction
 protocol (see Bergland et al. 2014 for details on buffers and solutions).
 We prepared sequencing libraries using a modified Illumina protocol
 (Bergland et al. 2014) and Illumina TrueSeq adapters. Paired-end 125bp
 libraries were sequenced to an average of 94x coverage either at the
 Stanford Sequencing Service Center on an Illumina HiSeq 2000, or at the
 Stanford Functional Genomics facility on an Illumina HiSeq 4000.
   The following sequence data processing was
 performed on both the new and the previously published data. We trimmed
 low-quality 3’ and 5’ read ends (sequence quality &lt; 20) using the
 program cutadapt v1.8.1 (Martin 2011). We mapped the raw reads to the
 <i>D. melanogaster</i> genome v5.5 (and for <i>D.
 simulans</i> genome v2.01, flybase.org) using bwa v0.7.12 mem
 algorithms, with default parameters (Li & Durbin 2009), and used
 the program SAMtools v1.2 for bam file manipulation (functions index,
 sort, and mpileup) (Li et al. 2009). We used the program picard v2.0.1 to
 remove PCR duplicates (http://picard.sourceforge.net) and the program GATK
 v3.2-2 for indel realignment (McKenna et al. 2010). We called SNPs and
 indels using the program VarScan v2.3.8 using a <i>p</i>-value
 of 0.05, minimum variant frequency of 0.005, minimum average quality of
 20, and minimum coverage of 10 (Koboldt et al. 2012). We filtered out SNPs
 within 10bp of an indel (they are more likely to be spurious), variants in
 repetitive regions (identified by RepeatMasker and downloaded from the
 UCSC Genome browser), and nucleotides with more than two alleles. Because
 we sequenced only male individuals, the X chromosome had lower coverage
 and was not used in our analysis.