1) The researchers used whole genome sequence data from 43 stickleback genomes to choose SNP markers for genotyping candidate genes involved in body size divergence. 2) Their criteria for choosing SNPs included a minor allele frequency over 20%, spacing of at least 30 base pairs from other SNPs, and preferentially choosing conserved and size-divergent SNPs. 3) They selected 30 SNPs across 14 candidate genes to genotype 500 stickleback specimens using TaqMan OpenArray. They will analyze associations between genotypes and growth phenotypes to identify genes underlying natural body size variation.

1. Identification of SNP Markers to Study the Genetics of Body Size Divergence
Among Natural Populations of the Threespine Stickleback (Gasterosteus aculeatus)
Jia Cheong, Yuleisy Lanns, Jennifer L. Rollins, and Michael A. Bell
We used a browser, provided by collaborators at Stanford
University, showing SNP calls from whole genome re-sequencing
of 43 stickleback genomes from 23 populations to choose SNP
markers for genotyping (Fig. 3).
Our criteria were as follows for choosing markers:
• The rare allele at each SNP had to be present in at least 20%
of the populations (minor allele frequency ≥ 0.20) to ensure
we have enough power to detect a genotype-phenotype
association if one exists.
• The SNP had to be at least 30 base pairs (bp) from other
polymorphic sites to ensure we can make appropriate
forward and reverse primers, as well as hybridization probes,
for TaqMan OpenArray genotyping.
• The number of SNPs we chose to place within a gene was
based on information about linkage disequilibrium and
recombination rates18,19, and SNPs within the same gene had
to be spaced approximately equidistant across the gene
(length of gene ÷ number of SNPs).
• Ideally, some SNPs were to be within exons, some within
introns, and some within flanking regions of each gene. SNPs
with greater conservation among species (medaka,
tetraodon, fugu, zebrafish, chicken, human, and mouse) were
given preference.
• Most SNPs were to be representative of the pattern seen
across the local interval surrounding them, but at least some
in each gene were chosen specifically because they showed
patterns indicative of size divergence across populations (i.e.,
large-bodied populations have a different allele from small-
bodied ones).
Methods
Introduction
Body size differs across species and among and within populations in all organisms, and these differences have genetic and
environmental determinants. Body size and growth rate have heritabilities between 17-42% among fish species1. Homologs
of genes in the somatrotropic growth pathway (Fig. 1) contribute to growth, development, and cell proliferation in all
vertebrates2-5. Polymorphisms in somatotropic genes are associated with growth differences in agriculturally important
species that have undergone artificial selection for enhanced muscle growth, including pigs6, chickens7, and fishes8.
Mutations in somatotropic genes cause dwarfism9,10, contribute to obesity11, and affect other growth traits in humans5,12-14.
However, there are few studies elucidating the genetic architecture of size divergence among populations in nature15,16.
Marine or anadromous (sea-run) threespine stickleback (Gasterosteus aculeatus) have colonized numerous freshwater
habitats throughout the northern hemisphere and subsequently adapted to those environments, offering an exceptional
opportunity to study parallel evolution of growth phenotypes17. Body size divergence is conspicuous between marine and
freshwater, as well as among freshwater, stickleback populations (Fig. 2). To determine the underlying genes involved in
body size divergence in natural stickleback populations, we have chosen candidate genes from the somatotropic growth
pathway (Fig. 1) and will determine which of these genes is associated with growth phenotypes. This summer, we used
available whole-genome sequence data from collaborators at Stanford University to choose appropriate single nucleotide
polymorphism (SNP) markers for genotyping. Our list of SNP markers, along with DNA extracts from stickleback specimens
collected from large- and small-bodied stickleback populations, will be sent to a facility for TaqMan OpenArray (Applied
Biosytems, USA) genotyping.
Figure 5. Mean (± error) growth phenotype for genotypes
at a hypothetical SNP marker, showing expected results if
SNP genotypes at our candidate growth genes (Fig. 1) are
associated with growth phenotypes within or among
stickleback populations. A and B are alternative alleles at
the hypothetical SNP marker. AA is homozygous for the A
allele, BB is homozygous for B, and AB is heterozygous.
Figure 4. TaqMan OpenArray genotyping. Hybridization probes for alternative alleles at a SNP hybridize to the SNP
location. Probes contain both a reporter dye and a quencher, which inhibits fluorescence of the reporter dye. If the
hybridization probe correctly matches its corresponding SNP allele, DNA polymerase (during PCR) will release the
reporter dye from the quencher. If hybridization probes are a mismatch to the SNP allele, DNA polymerase will
displace the probe instead of cleaving it, and reporters will remain attached to quenchers. Hybridization probes for
alternative alleles have dyes that fluoresce at different wavelengths, and fluorescent emissions peaks are detected
on the OpenArray imaging machine. If fluorescence peaks are present from only one dye in the sample, the
specimen is homozygous for the allele corresponding to that dye. If fluorescence peaks are present from both dyes,
the specimen is heterozygous. Figure is from the TaqMan OpenArray Getting Started Guide (Applied Biosystems,
USA).
Figure 2. Size divergence among stickleback populations. Fish
are putative 2-year-olds from Barley Lake (top), Rabbit Slough
(middle), and Herkimer Lake (bottom). The Rabbit Slough
anadromous population represents the body size of the
putative ancestor of the lake populations. Barley and
Herkimer lake fish have evolved larger and smaller body size
relative to the ancestral size.
Figure 1. PIT1 and PROP1 control the differentiation of somatotroph cells, which
synthesize growth hormone (GH) in the pituitary gland. GH is released into the
bloodstream by the actions of GHRH and/or GHRL and their receptors (GHRHR
and GHSR). GH binds to its receptor (GHR), which triggers the release of insulin-
like growth factors (IGF-I and IGF-II). IGFs bind their receptors (IGF-1R and IGF-
IIR), IGF binding proteins (IGFBPs), or INSR, triggering numerous downstream
reactions that promote growth, development, and cell proliferation. The proteins
circled in purple are those we have chosen to target in our genotype-phenotype
association study. FGF2, MYOD1, and NPYP are not in the somatotropic growth
pathway, but were found to be under directional selection in stickleback
populations and are known to play a role in growth. Modified from Rodriguez et
al. (2007). Gene names are given in Table 1.
Table 1. List of SNP markers we chose for the genotype-phenotype
association study.
Gene (acronym; chromosome
location)
SNP
location
Intron/
Exon
Conservation Pattern
Growth hormone (GH2; XI) 16069693 intron not cons none
16070261 intron not cons none
Growth hormone receptor 1
(GHr1; XIII)
5675328 intron highly cons size div
5677516 exon not cons none*
5678701 intron not cons none
Insulin-like growth factor 1
(IGF1; IV)
22098554 intron not cons none
32103581 intron highly cons mar-fresh div
32108005 intron not cons size div
Insulin-like growth factor 2
(IGF2; XIX)
13287332 intron not cons mar-fresh div
13289778 intron highly cons mar-fresh div
13290781 intron highly cons mar-fresh div
Insulin-like growth factor 1
receptor (IGF1r; XIX)
16907938 exon highly cons mar-fresh div
16962799 intron highly cons mar-fresh div
Insulin-like growth factor 1
receptor (IGF1r; II)
4581834 intron no data mar-fresh div
Insulin-like growth factor
binding protein 2, paralog 1
(IGFBP2; I)
21589495 exon not cons mar-fresh div
21593750 intron not cons none
Insulin-like growth factor
binding protein 2, paralog 2
(IGFBP2; XVI)
5908893 intron no data none
Pituitary specific transcription
factor 1 (PIT1; XVI)
13151323 intron not cons none
13152653 intron not cons none
13154316 intron not cons none
Somatostatin 5 (STAT; XI) 5772720 intron not cons mar-fresh div
Fibroblast growth factor
(FGF2; IV)
3332048 intron near cons reg size div
3333874 intron no data none
myogenic differentiation 1,
paralog 1 (MYOD1; XIX)
9371313 intron not cons size div
9371918 exon highly cons size div
myogenic differentiation 1,
paralog 2 (MYOD1; II)
21931109 exon highly cons none
21931600 intron not cons none
21932462 exon highly cons none
neuropeptide Y precursor
(NPYP; X)
9526657 intron semi-cons none
9527397 exon highly cons none
* Barley Lake has a unique allele
cons = conserved; div = divergence, mar = marine, fresh = freshwater, reg = region
Figure 3. Screen shot of the Stanford Stickleback Genome Browser, showing a 100 bp region in the 5’ flank
of one of the paralogs of IGFBP2 (chromosome XVI: 5901577-5901677). The SNP indicated by the black
arrow may be associated with growth phenotypes, since the large-bodied Barley Lake and marine
populations have a thymine (black) at this position, while most other, average-bodied populations have
the alternative allele (adenine; blue).
Results and Future Work
Our SNP list comprises 30 bi-allelic markers across 14 genes and their paralogs (Table 1).
We have extracted DNA from 30-35 individuals from each of 16 populations (n = 500 specimens) and measured growth
phenotypes (body length and weight). We will send our list of SNP markers (Table 1) and extracted DNA from stickleback
specimens to a facility for TaqMan OpenArray genotyping (Fig. 4), which allows for simultaneous determination of genotypes at all
SNPs for individual specimens. We will determine whether genotypes of specimens at each of our SNPs are associated with
growth phenotypes within and among populations after accounting for age, sex, and degree of relatedness (Fig. 5).
References
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We thank the Kingsley Lab at Stanford University for providing
the SNP calls from whole-genome resequencing of stickleback
populations. We also thank Howard Sirotkin and our families
for making this work possible and for their support. This
research was supported by the Chancellor’s Education Pipeline.