Download Text S2 Selection on GWAS SNPs and Traits As GWAS SNPs are

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Selection on GWAS SNPs and Traits
As GWAS SNPs are associated with a phenotype either by affecting it directly or marking a
polymorphism that does, they may be subject to the influence of selective forces. Selection on an
individual SNP can be either positive, negative, or balancing. The type of selection we are likely to detect
in our analyses varies somewhat between the four types of measures we have used. For delta, Fst, and
LLC measures, scenarios could be imagined whereby positive, negative, or balancing selection could
create large allele frequency differences between populations or gradients across groups of populations
provided the selection varies to some degree from location to location or population to population.
With these three types of measures, we are then likely to detect spatially varying selection in general. It
does seem, though, that positive selection would be the most likely selective force to produce extreme
delta, Fst, or LLC measures as its action is to directly push a rare allele to higher frequencies. Unlike delta,
Fst, and LLC measures, elevated iHS scores could be observed at a locus that is under spatially uniform
selection (although empirically the human populations of different continents tend to produce elevated
iHS scores in different regions of the genome [1]). This is because rather than comparing populations or
groups of populations, iHS compares one allele of a SNP to the other. Thus, any selective process that
“favors” one allele over another for a long period of time could theoretically produce an elevated iHS
score. However, iHS was developed, and is generally used, to detect positive selection [2]; its efficacy in
detecting other types of selection has not yet been explored [3]. In a genomic region under negative
selection, the ancestral allele of a SNP would be expected to sit within a relatively short haplotype. The
derived minor allele would not be expected to remain in the population for long before being
eliminated, so it would sit on a young, long haplotype. This scenario, in theory, may produce an elevated
iHS score. It should be mentioned, though, that if negative selection were widespread in the genome, no
one individual variant experiencing it would be likely to stand out in the empirical distribution. However,
it is possible that when we observe a SNP with a high iHS score, this is due to negative selection rather
than positive selection. Variants in regions under balancing selection seem less likely to produce high iHS
scores as selection favors both alleles under some circumstances. It is worth noting that iHS is not
elevated in Africans at the HBB locus, the region of the genome thought to be under balancing selection
due to malaria and sickle cell anemia [2].
Having characterized the types of selection we may be able to detect at GWAS SNPs, it is useful
to speculate on what types of selection may be acting on GWAS traits and how this may affect
underlying genetic variation. First of all, it is certainly possible that many GWAS traits are effectively
neutral. In this case, allele frequencies at associated SNPs should generally be determined by drift and
demographic forces, and should not be different from those of random SNPs in the Li et al. [4] dataset.
Alternatively, the majority of GWAS traits may have fitness effects. In stable environments, non-neutral
quantitative traits (we assume most GWAS traits can be treated as quantitative traits, with the
pathological state representing one end of the phenotypic spectrum for disease traits) are generally
assumed to be under stabilizing selection, where extreme phenotypes have lower fitness than more
moderate phenotypes [5]. Although selection to maintain a population’s phenotype for a trait at an
optimum would generally seem to eliminate variation, quantitative traits often have large amounts of
underlying genetic variation [5]. The same appears to be true for GWAS (quantitative) traits in humans,
given the large number of trait-associated loci that have been discovered. Two primary mechanisms
have been proposed which might maintain genetic variation at quantitative trait loci (QTLs) under such
conditions. The first involves a balance between the introduction of new, mildly deleterious alleles at
QTLs by mutation and their removal by negative selection (when different populations are adapted to
their local environments, these new alleles may also be introduced by migration). Causative variants
under this scenario tend to be rare and traits must by necessity be quite polygenic for genetic variation
to be maintained [3, 5, 6]. Under the second mechanism, causative variants are held at intermediate
frequencies by balancing selection [3, 5, 6]. In addition to the types of selection acting in a stable
environment, there may be positive selection for a new optimal phenotype following a shift in
environmental conditions. Depending on the amount of standing variation associated with a trait at the
time of the change in selective forces, the response to this may proceed through an allele frequency
change at one locus or though allele frequency changes at many loci simultaneously [3, 7]. In all
likelihood, negative, positive, and balancing selection as well as neutral forces are all important
influences of allele frequency patterns at and haplotypic structure surrounding GWAS SNPs. Our results,
though, may provide clues as to the relative importance of these forces in shaping the genetic variation
underlying GWAS traits.
References
1.
Pickrell JK, Coop G, Novembre J, Kudaravalli S, Li JZ, et al. (2009) Signals of recent positive
selection in a worldwide sample of human populations. Genome Research 19: 826-837.
2.
Voight BF, Kudaravalli S, Wen X, Pritchard JK (2006) A map of recent positive selection in the
human genome. PLOS Biology 4: 446-458.
3.
Mitchell-Olds T, Willis JH, Goldstein DB (2007) Which evolutionary processes influence natural
genetic variation for phenotypic traits? Nature Reviews Genetics 8: 845-856.
4.
Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, et al. (2008) Worldwide human
relationships inferred from genome-wide patterns of variation. Science 2008, 319: 1100-1104.
5.
Barton NH, Keightley PD (2002) Understanding quantitative genetic variation. Nature Reviews
Genetics 3: 11-21.
6.
Strum RA (2009) Molecular genetics of human pigmentation diversity. Human Molecular
Genetics 18: R9-R17.
7.
Pritchard JK, Pickrell JK, Coop G (2010) The Genetics of Human Adaptation: Hard Sweeps, Soft
Sweeps, and Polygenic Adaptation. Current Biology 20: R208-R215.