ppt for

Did Dodging Disease Keep
Pygmies Small?
Patterns of Ancestry, Signatures of Natural
Selection, and Genetic Association with
Stature in Western African Pygmies
Joseph P. Jarvis, Laura B. Scheinfeldt., Sameer Soi.,
Charla Lambert., Larsson Omberg., Bart Ferwerda,
Alain Froment, Jean-Marie Bodo, William Beggs,
Gabriel Hoffman, Jason Mezey, Sarah A. Tishkoff*
Pygmy: any group in whom adult males are under 1.52 meters
live and hunt in tropical forests, and they die young (17~25y);
infectious diseases
WHY are they small/short?
Move nimbly through dense forest
Fast heat-dissipating
Trade-off between growth and reproduction before and after puberty
Short stature is a byproduct!!
Brief history of Pygmy
~60-70 kya
African Pygmies
Eastern Pygmies
West African
Bantu groups
~10-27 kya
African mtDNA
Western Pygmies
~4-5 kya
Previous studies documented human growth hormone (HGH) and IGF-1
contribute to short stature of Pygmies.
Western Pygmy (Baka, Bakola, and Bedzan): 67 (57 have phenotype data for height)
Bantu-speaking (Tikar, Ngumba, and Lemande): 58 (39 have phenotype data for height)
Platform: Illumina 1M SNP array
Population Structure
Proportion of Bantu ancestry
Bakola: 27%
Baka: 35%
Bedzan: 49%
Correlation between ancestry proportion and height
Local Ancestry Inference
Challenge to common methods:
1. The admixture is both ancient and on-going, thus a wide distribution
of haplotype sizes including many that are relatively short is
2. Only one of the two ancestral populations is still available and
sampled (Bantu).
 Method: SupportMix
SupportMix is a two-step process that classifies the ancestral origin of small
regions of the genome using a support vector machine (SVM) trained on the
ancestral population(s). This is followed by a classification of ancestry with
the aid of a Markov model.
The SVM learns the parameters of the Markov-process and the most
probable ancestral state is solved by the forward-backward algorithm over
the observed states.
LAI results
the average ancestry block
sizes in Pygmies to be
1.7+/22.4 Mb for Pygmy
and 3.1+/24.6 Mb for Bantu
A region with reduced
levels of switching
between ancestry blocks.
Chr3: 46Mb-53Mb
Scan for Selection: Fst/LSBL analysis; XP-EHH and his Analyses
Using samples estimated to have the lowest levels of admixture in the STRUCTURE analysis.
Locus-Specific Branch Length (LSBL): diversity of each SNP
Using three-way LSBL analysis helps to find the lineage specific differentiated SNPs
• Adding a new population: Hapmap Maasai
• Identifying SNPs that are differentiated
specifically on the Pygmy lineage
Lineage specific
X-axis: x/(x+y+z)
Y-axia: x
high signal regions
Fst > 0.33, (the top 0.1 percentile)
947 SNPs
LSBL > 0.2215, (the top 0.1 percentile)
912 SNPs
Most striking signal, Chr3: 45-60 Mb
Several top signals from XP-EHH and iHS
were also found in region Chr3: 45-60 Mb
Visualization of high density blocks
Pathway Enrichment Analysis of Scans for Positive Selection
Region: all protein coding genes within 100kb up- and downstream of SNPs
showing signatures of selection.
Neuro-endocrine signaling and pathways potentially involved in reproduction
and thyroid (甲状腺) function, including oxytocin (催产素) receptor mediated
signaling and thyrotropin (甲状腺刺激激素)-releasing hormone receptor (TRHR)
signaling. (genes near high Pygmy LSBL SNPs)
• Oxytocin plays a role in lactation, parturition (分娩), and pair bonding.
• TRHR signaling pathway plays a role in regulation of the hypothalamicpituitary-thyroid (下丘脑-脑垂体-甲状腺) axis and influences growth,
thermo-regulation, reproduction, and immune response.
Signatures of Selection and Association at GWAS Significant SNPs for
Height in Non-Africans
• 112 (of 211) SNPs significantly associated with height variation in recent
GWAS in non-Africans are tested.
• However, little evidence for selection at these loci were re-found.
• Then, comparing the positional candidate genes reported in non-African GWAS
to a list of all genes within 100 kb windows up- and downstream of signatures
of selection in this study.
• 69 overlapped, including DOCK3.
• However, this overlap may be by chance
• These results suggest that the genetic architecture of height in Pygmies differs
from that observed in Europeans.
Targeted Association Analysis for Height
• First in Pygmy individuals only, 2288 SNPs.
• Several suggestive associations with height, though not significant after
multiple tests correction.
• Next, combining Pygmy and Bantu dataset, for SNPs showing Pygmy-specific
signatures of selection.
• 128 SNPs showing significant associations after correction for multiple tests
and for population structure.
• Several genes found to be involved, say NDUFA4, DOCK3.
• Multiple signals were again found in the region on Chr3: 45-60Mb.
Pathway Enrichment Analysis in Extreme Genome-Wide Association
• Pathway enrichment for genes 100 kb up- and downstream of markers in the
extreme tail of the empirical distribution of genome-wide p-values (the
lowest 0.1%)
• Top two pathways that are significantly enriched in the combined PygmyBantu association analysis are the protein kinase B signaling and the mitogen
activated protein kinase/MAP kinase cascades of the Insulin/IGF pathway.
Conclusions on the region of Chromosome 3: 45-60 Mb
Three promising
candidates: DOCK3,
CISH plays an import role
in infectious disease
susceptibility, and it also
directly inhibits HGHR
action and its expression is
highly regulated by levels
of HGH expression.
Transgenic mice that overexpress CISH show reduced
growth and overall small
body size.
Showing overlapping pattern of selection and association signals
Signatures of selection in other population were also
found in this region, which indicates its adaptation in
human populations.
It is possible that the adaptive process that produced small body size in Pygmies
may be the result of selection for traits other than stature, including early
reproduction, metabolism, and immunity, and that the functional variants that
are targets of selection may have pleiotropic effects.
It is also possible that there have been multiple selective events in the history of
the pygmy populations, at different time periods, that may have contributed to
the co-adaptive evolution of loci that play a role in immunity, metabolism, and
neuro-endocrine function.

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