Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies

Davide Piffer

doi:10.12688/f1000research.6002.1

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Research Note

Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies

[version 1; peer review: 1 approved with reservations]

Davide Piffer

PUBLISHED 16 Jan 2015

Author details Author details

Ulster Insitute for Social Research, London, UK

OPEN PEER REVIEW

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Abstract

Spatial patterns of allele frequencies reveal a clear signal of natural (or sexual) selection on human height. The average frequency of 66 common genetic variants for 26 populations belonging to 5 sub-continental human groups was significantly correlated to average phenotypic population height. The method of correlated vectors provided additional evidence for a signal of natural selection in SNPs with higher significance. Factor analysis of the five top genome-wide association study (GWAS) hits revealed a clear factor indicating selection pressures on human height, peaking among northern Europeans and some African groups (Esan Nigeria) whilst reaching a nadir among South-East Asians.

Keywords

Height; Evolution;Polygenic Selection; Height

Corresponding author: Davide Piffer

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2015 Piffer D. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

How to cite: Piffer D. Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies [version 1; peer review: 1 approved with reservations]. F1000Research 2015, 4:15 (https://doi.org/10.12688/f1000research.6002.1) First published: 16 Jan 2015, 4:15 (https://doi.org/10.12688/f1000research.6002.1) Latest published: 25 Jan 2016, 4:15 (https://doi.org/10.12688/f1000research.6002.3)

Introduction

A recent GWAS (Wood et al., 2014) based on a very large sample (N=250K) identified common variants responsible for normal variation in human height within populations.

Over the last few years, researchers have started moving away from the study of genetic evolution using a single-gene, Mendelian approach towards models that examine many genes together (polygenic). The more genes are involved in a given phenotype, the more the signal of natural selection will be “diluted” across different genomic regions (because each gene accounts for a tiny effect) making it difficult to detect it using approaches focused on a single gene (Pritchard et al., 2010; Piffer, 2014). A first attempt at empirically identifying polygenic selection was made by Turchin et al., (2012) on two populations (Northern and Southern Europeans) and evidence for higher frequency of height increasing alleles (obtained from GWAS studies) among Northern Europeans was provided. A drawback of that study was the reliance on populations from a single continent and that crude pairwise comparisons (e.g. French vs. Italian) were used without correlating frequency differences to average population height. Moreover, the strength of selection was not determined.

Two different approaches to identify selection based on the correlation of allele frequencies across different populations have been recently developed by Piffer (2013) and Berg & Coop (2014).

Piffer’s method uses factor analysis of trait increasing alleles (found by GWA studies) as a tool for finding a factor that represent the strength of selection on a phenotype and the underlying genetic variation (Piffer, 2014a). An additional methodology consists of computing the correlation between genetic frequencies and the average phenotypes of different populations; then, the resulting correlation coefficients are correlated with the corresponding alleles’ genome-wide significance (p value). If the alleles contain selection signals, a positive correlation will be found, as alleles with high p value (more likely to be false positives) have a weaker correlation to average population phenotype (Piffer, 2014a).

Piffer’s method (Piffer, 2013; Piffer, 2014a) to identify signals of polygenic selection was used in this study and applied to the top five GWAS hits (ranked according to p value). Piffer (2014b) carried out a study on height SNPs but it was based on a smaller GWAS sample and an older version (phase 1) of the 1000 Genomes data, containing data for only 14 populations. This paper uses the phase 3 1000 Genomes data and the GWAS meta-analysis was carried out on a much larger sample size, which produces more hits with better significance. The aim of this paper is to test the hypothesis that stature has undergone natural or sexual selection in populations after humans dispersed in different continents giving rise to distinct genetic clusters.

Methods

Frequencies of alleles with a positive effect (height increasing) were obtained from 1000 Genomes (phase 3): http://browser.1000genomes.org/index.html comprising 26 populations belonging to five racial groups.

Average population height was obtained from the references listed at: http://en.wikipedia.org/wiki/Human_height, considering only statistics published after 2000 and young age groups (18–40). Only 11 populations met these criteria (see references in Table 1).

Table 1. Polygenic score and height.

Population	Polygenic score (%)	Height (cm)	Reference (Height)
Afr.Car.Barbados	48.94
US Blacks	48.71	178.00	McDowell et al., 2008
Esan Nigeria	49.50
Gambian	48.97
Luhya Kenya	48.42
Mende Sierra Leo	49.03
Yoruba	48.52
Colombian	46.05	170.60	Meisel & Vega, 2004
Mexican LA	46.95	170.6	McDowell et al., 2008
Peruvian	46.48
Puerto Rican	46.79
Chinese Dai	44.88
HanChineseBejing	44.76	170.2	Yang et al., 2005
HanChineseSouth	45.70	170.2	Yang et al., 2005
Japanese	44.85	172.00	Ministry of Ed., 2004
Vietnam	44.76	165.70	Hung & Park, 2008
UtahWhites	47.62	178.9	McDowell et al., 2008
Finns	48.09	180.70	National Institute for Health and Welfare, 2011
British	46.80	177.80	Moody (2013). Health Survey for England
Spanish	46.77
TuscanItaly	47.11	177.00	Cacciari et al., 2002*
Bengali Banglade	46.09
Gujarati Ind. Tx	47.12
Indian Telegu UK	47.62
Punjabi Pakistan	47.21
SriLankanUK	46.98

*Not on Wikipedia. Region of a country, more specific statistics found elsewhere.

For each chromosome, the three alleles with the highest p values were selected, and these were all unlinked (>500Kb apart from each other). Only unlinked alleles were used to avoid the confounding influence of linkage on cross-population allele frequency. Selection was restricted only to the alleles with the highest significance because these are less likely to be false positives. The same number of SNPs (3) from each chromosome was used to get a representative sample of the entire genome, to avoid bias due to chromosome location. The conventional nominal p-value < 5×10^-8 was used as significance threshold (Barsh et al., 2012).

A polygenic score was calculated as the mean frequency of height increasing alleles (defined as those with a positive Beta coefficient in the meta-analysis).

Analyses were carried out using R.

Results

Population	rs3814333 T	rs3791679 A	rs724016 G	rs1812175 G	rs9292468 T	rs4896582 G	rs42039 T	rs4733724 A	rs817300 G	chr.10 rs1923367 G	rs606452 A	rs3825199 G	rs3118905 G	rs2093210 C	rs7162542 G	rs26868 A	rs2079795 T	rs4369779 C	rs11880992 A	rs143384 G	rs2834442 A	rs738288 G	rs2284746 G	rs2289195 A	rs7652177 G	rs7692995 T	rs7701414 G	rs806794 A	rs798497 A	rs10958476 C	rs7870753 G	rs12779328 C	rs1681630 T	rs8756 C	rs7334755 C	rs1950500 T	rs2280470 A	rs1659127 A	rs2854207 G	rs9967417 G	rs2074977 C	rs1884897 A	rs2211866 A	rs5757318 T	rs9428104 G	rs3116168 C	rs2581830 T	rs17556750 A	rs4868126 G	rs12214804 C	rs552707 T	rs4735677 T	rs7849585 T	rs7899004 T	rs2237886 T	rs10748128 T	rs7319045 A	rs862034 G	rs2871865 C	rs11648796 G	rs3760318 G	rs11152213 C	rs4803468 A	rs1074683 C	rs9977276 G	rs7284476 A	Average	Height cm

AFR	21	97	82	70	34	4	9	16	100	18	42	24	93	97	76	2	60	81	23	97	64	28	14	36	92	56	25	62	86	4	65	94	77	42	88	37	19	28	47	14	71	48	37	2	73	80	78	2	35	46	12	6	80	58	7	63	60	68	52	60	55	25	15	77	75	16	48.86363636
AMR	29	76	42	78	42	48	17	61	97	34	28	26	81	34	74	45	44	62	35	34	75	39	45	51	56	83	31	55	74	11	14	77	30	33	81	46	53	24	20	26	60	30	45	10	70	57	40	24	66	33	16	24	46	46	10	36	38	65	86	50	63	13	51	67	85	29	46.53030303
ASN	44	23	32	61	34	26	2	28	100	34	48	28	98	79	95	51	35	86	31	29	51	1	25	25	55	84	0	24	77	19	13	77	34	9	100	30	72	56	5	0	72	11	65	49	66	24	26	17	62	12	1	19	34	49	19	64	54	73	89	77	77	19	57	76	92	58	45.1969697
EUR	30	77	42	81	42	67	27	77	92	47	16	21	72	41	56	42	32	79	43	41	65	40	50	43	52	87	42	70	71	20	20	70	34	45	81	28	32	34	26	43	34	39	46	14	75	71	38	30	64	8	26	29	33	61	10	35	38	63	87	27	61	25	41	76	73	39	47.28787879
SAS	32	75	27	88	39	66	11	50	100	53	14	13	87	60	70	49	39	84	34	59	74	33	43	29	55	70	11	55	83	15	13	90	22	23	88	33	44	34	15	23	46	44	65	19	67	47	46	19	79	5	24	30	34	47	11	55	37	60	85	52	61	32	40	84	84	31	47.07575758

ACB	21	96	78	71	36	11	7	18	98	21	39	24	91	92	74	4	54	81	23	95	61	33	19	34	86	61	24	68	88	6	62	91	73	46	86	37	19	29	39	22	70	47	33	4	72	79	79	7	40	44	15	9	77	61	9	62	53	74	50	56	52	28	17	80	75	19	48.93939394		Afr.Car.Barbados
ASW	31	94	79	74	30	16	13	27	98	26	39	27	93	89	76	15	51	75	27	85	63	31	15	34	83	68	30	60	86	7	51	89	70	34	89	41	21	25	48	17	61	48	42	4	70	80	70	7	47	39	16	5	66	61	9	68	54	61	61	46	54	23	16	79	77	24	48.71212121	178.00	US Blacks
ESN	16	98	88	70	29	2	8	14	100	15	38	29	94	100	78	1	61	85	25	100	65	29	12	36	93	58	24	60	89	4	68	97	77	49	88	39	14	32	46	15	74	55	43	0	70	81	82	1	32	52	10	6	81	59	10	60	58	66	50	59	60	25	14	80	74	19	49.5		Esan Nigeria
GWD	20	94	77	59	34	0	8	14	100	20	45	14	96	99	80	0	75	81	22	99	65	26	19	35	97	50	31	65	83	0	65	95	85	41	92	33	22	34	50	18	78	39	34	0	80	77	84	0	26	47	15	4	86	60	7	55	58	66	57	63	58	23	14	76	74	8	48.96969697		Gambian
LWK	17	97	84	81	29	2	11	11	100	15	50	25	93	98	79	2	55	85	16	98	56	26	4	51	91	45	23	55	84	4	63	95	68	36	90	37	24	21	51	9	66	55	33	2	67	90	76	2	31	46	10	5	77	53	7	71	61	76	43	69	62	25	13	77	80	18	48.42424242		Luhya Kenya
MSL	26	99	81	69	42	2	9	16	100	16	35	24	95	99	74	0	61	81	24	99	72	22	15	32	98	52	25	68	82	4	68	94	90	36	89	30	18	25	51	9	73	38	44	1	78	77	78	1	36	51	11	8	88	55	5	65	66	61	51	68	48	22	14	77	74	14	49.03030303		Mende Sierra Leo
YRI	19	97	86	71	37	1	7	15	100	18	44	24	93	98	74	0	57	77	24	100	68	31	13	31	94	60	22	56	92	3	74	94	73	46	82	39	16	28	46	11	70	50	36	1	72	79	75	1	40	39	11	8	81	56	7	64	65	68	52	55	54	29	14	71	70	13	48.51515152		Yoruba
CLM	29	69	39	73	39	45	22	57	99	35	22	21	80	38	70	38	45	66	31	35	72	43	46	51	57	81	32	56	80	14	22	72	35	32	82	35	46	24	20	32	54	35	39	12	80	60	40	21	68	33	15	26	41	54	9	39	43	60	81	37	65	11	48	70	78	35	46.04545455	170.60	Colombian
MXL	26	74	41	82	44	64	13	69	95	38	36	26	84	34	74	47	50	64	35	27	73	45	45	48	57	86	24	63	67	11	5	79	18	27	83	62	59	23	20	25	56	22	51	5	70	59	37	27	71	40	11	23	48	38	14	34	34	61	91	61	61	14	43	60	91	34	46.95454545	170.60	Mexican LA
PEL	27	80	49	84	46	34	7	64	99	29	39	31	90	19	89	55	44	45	19	15	86	37	46	58	55	93	12	42	77	8	8	88	18	33	88	61	81	14	23	10	85	21	49	4	59	46	31	27	71	44	8	25	44	30	10	30	29	66	88	80	66	6	74	54	95	23	46.48484848		Peruvian
PUR	33	80	41	73	38	52	24	59	95	36	24	25	72	43	65	40	39	73	50	54	71	35	44	46	57	75	49	62	70	13	18	71	41	37	74	34	34	32	19	34	48	37	43	15	70	61	50	24	57	21	28	24	39	56	10	38	43	71	87	31	59	22	41	78	78	25	46.78787879		Puerto Rican
CDX	41	26	33	61	28	28	1	22	100	33	52	20	97	89	95	43	39	91	24	34	54	1	19	25	59	82	0	21	76	23	3	77	30	6	100	22	64	61	3	1	76	8	75	55	73	28	26	16	63	15	1	18	32	58	19	61	56	62	85	74	74	13	64	73	90	63	44.87878788		Chinese Dai
CHB	48	21	32	60	36	28	2	32	100	35	46	31	99	69	97	47	34	82	34	23	49	0	26	23	50	86	0	25	77	15	1	79	34	11	100	29	76	50	6	0	64	12	62	40	67	17	32	12	62	13	0	21	33	54	25	60	53	82	91	80	81	18	53	74	92	63	44.75757576	170.20	HanChineseBejing
CHS	48	21	33	61	30	20	2	24	100	40	52	35	99	83	93	50	29	83	30	28	52	1	26	29	61	85	0	21	79	23	4	77	40	8	100	25	82	59	6	0	75	13	61	52	66	26	19	15	62	10	3	18	33	50	18	67	55	77	89	85	77	16	59	78	91	62	45.6969697	170.20	HanChineseSouth
JPT	42	25	35	60	42	24	1	37	100	30	46	33	97	75	97	58	38	87	45	23	48	0	29	20	45	94	0	17	80	16	5	73	32	11	100	38	72	47	5	0	65	10	63	53	52	24	27	28	54	15	1	21	37	35	18	68	55	73	92	73	74	26	50	76	94	49	44.84848485	172.00	Japanese
KHV	41	22	28	61	32	28	2	26	99	32	42	22	96	79	92	53	35	88	21	37	53	3	25	27	63	72	1	36	74	18	4	77	32	7	100	35	64	63	6	1	72	13	66	47	75	23	24	16	69	8	2	18	35	48	15	64	50	70	85	72	81	19	59	77	92	57	44.75757576	165.70	Vietnam
CEU	33	77	41	84	50	72	29	77	90	50	16	21	74	42	56	42	30	75	41	35	64	49	57	40	53	89	44	69	73	17	19	67	31	51	79	27	29	29	24	43	33	44	40	13	72	69	39	30	65	9	30	28	37	62	11	36	38	63	87	27	63	26	42	75	70	45	47.62121212	178.90	UtahWhites
FIN	35	76	51	84	40	73	23	83	92	40	22	21	69	31	59	46	25	78	38	44	74	41	50	51	57	86	28	61	65	17	21	77	36	49	88	34	38	32	24	29	37	34	51	13	76	82	36	39	62	5	30	25	30	62	13	40	43	67	93	41	66	16	39	74	74	38	48.09090909	180.70	Finns
GBR	34	81	51	80	38	58	25	81	90	52	17	23	71	37	52	47	29	77	46	36	59	46	50	39	49	88	44	67	68	21	15	73	35	45	79	28	30	26	29	46	36	39	32	12	76	72	40	30	68	8	25	30	31	52	8	38	41	65	84	21	62	25	39	71	74	48	46.8030303	177.80	British
IBS	28	80	37	78	41	63	32	76	91	51	15	21	72	46	55	40	38	77	43	43	65	31	47	40	53	88	51	72	72	20	27	65	32	39	80	25	31	40	31	44	33	34	53	15	75	68	37	28	62	11	21	30	36	64	10	28	35	61	81	21	52	26	38	79	74	35	46.77272727		Spanish
TSI	22	72	34	81	41	60	26	72	95	43	12	21	74	46	58	42	36	87	49	47	63	37	47	45	47	86	42	80	76	23	17	67	34	41	79	28	33	41	23	51	31	42	50	14	76	66	39	22	63	7	25	33	31	66	7	35	33	60	90	23	61	29	45	79	75	29	47.10606061	177.00	TuscanItaly
BEB	30	69	28	88	42	54	9	50	99	56	15	13	87	66	72	56	42	85	29	56	72	31	40	28	62	66	10	48	85	14	9	91	17	25	87	31	40	37	11	19	56	37	58	22	73	44	44	23	76	6	22	26	31	47	14	53	35	60	86	48	54	26	40	81	81	30	46.09090909		Bengali Banglade
GIH	35	81	29	87	37	71	10	44	100	51	13	11	84	55	71	53	41	81	33	65	74	40	48	32	52	73	12	61	82	19	15	88	19	25	83	35	44	37	17	25	34	43	64	15	72	49	44	12	77	5	26	37	41	39	10	54	40	54	84	50	60	28	43	83	84	29	47.12121212		Gujarati Ind. Tx
ITU	31	74	25	91	39	67	12	47	100	52	16	12	92	63	68	46	40	89	36	63	73	30	39	27	59	72	10	50	86	19	10	93	22	21	89	30	43	35	16	23	43	46	68	18	65	45	50	20	79	5	24	26	31	50	11	56	32	68	88	57	65	37	40	86	87	36	47.62121212		Indian Telegu UK
PJL	30	76	32	89	39	65	14	58	100	45	13	11	84	55	77	46	34	82	34	59	69	33	52	28	52	73	9	58	79	11	13	87	30	24	89	35	48	32	15	24	47	50	69	20	64	47	41	19	84	4	29	32	32	49	11	49	36	55	83	53	59	36	40	86	83	34	47.21212121		Punjabi Pakistan
STU	34	74	22	86	38	69	11	50	100	60	14	16	89	62	61	46	37	83	36	54	79	31	36	31	52	68	11	59	83	11	16	89	22	21	92	31	43	29	14	22	51	44	67	20	64	49	51	20	78	3	20	30	32	49	8	62	40	63	83	53	64	32	38	86	87	25	46.98484848		SriLankanUK

p value	4.80E-51	2.40E-67	3.20E-158	2.10E-86	1.50E-33	2.60E-55	3.80E-88	6.00E-30	4.30E-34	4.90E-24	1.90E-23	3.90E-49	1.10E-69	3.00E-35	8.20E-55	3.20E-18	1.70E-46	1.50E-53	6.90E-28	1.20E-121	4.40E-15	5.50E-11	1.20E-40	2.40E-37	2.70E-39	1.10E-71	1.30E-34	4.60E-74	2.20E-71	1.70E-40	3.50E-33	1.70E-17	2.40E-20	4.50E-90	9.10E-15	3.20E-22	2.80E-44	2.80E-19	1.30E-42	2.20E-40	1.90E-20	1.30E-48	3.50E-13	1.90E-09	2.80E-36	1.40E-31	4.40E-25	8.30E-48	2.80E-29	1.50E-49	9.30E-46	6.00E-30	1.10E-29	7.00E-17	5.30E-18	4.40E-29	8.40E-15	6.40E-20	1.70E-34	1.40E-18	3.00E-41	6.90E-13	1.70E-21	7.70E-38	2.90E-10	6.10E-09	0.832945202

r with pol.score	-0.790331762	0.899579663	0.782908171	0.260514643	-0.056466468	-0.280513839	0.224436571	-0.215307661	-0.054084056	-0.409053397	-0.08379395	-0.169657534	-0.088977579	0.316524532	-0.431162869	-0.797577694	0.626854356	-0.053813274	-0.25150588	0.836417304	0.371484927	0.5248369	-0.287897068	0.287371972	0.728064201	-0.653099364	0.476170804	0.709416032	0.407274032	-0.677208816	0.847869832	0.5730371	0.706675587	0.779231214	-0.425362117	0.154407311	-0.842688347	-0.643771536	0.889401824	0.26005088	-0.003611539	0.824961094	-0.634425385	-0.852992768	0.289966862	0.887755343	0.875186477	-0.472216002	-0.593011472	0.576588768	0.381759561	-0.487482005	0.750331279	0.41740887	-0.749460843	0.078561852	0.253547933	-0.180683834	-0.747553706	-0.234098265	-0.755983086	0.351941269	-0.860906364	0.173435412	-0.718700559	-0.819276807	0.033352981
r with height	-0.435574129	0.752712603	0.625345218	0.747309326	0.358180653	0.546884332	0.748286087	0.649129375	-0.74540432	0.449843435	-0.660145189	-0.386511072	-0.719016657	-0.418467523	-0.774281084	-0.436582276	-0.256672674	-0.126150998	0.565492039	0.494092018	0.438201703	0.647355153	0.479661027	0.452172598	0.016916402	0.182012107	0.744962767	0.653452155	-0.228867164	0.005344638	0.618527966	-0.131187811	0.331784401	0.878927022	-0.614234797	-0.214447917	-0.796947232	-0.600654335	0.716628864	0.679022114	-0.800535995	0.794086517	-0.643388598	-0.631208242	0.218294576	0.826357092	0.561816211	0.4696176	-0.326641511	-0.166332929	0.871097928	0.229508215	0.053868348	0.636421819	-0.546016073	-0.436207714	-0.364379151	-0.441890046	-0.179750584	-0.740673452	-0.70368641	0.466718891	-0.709600552	0.171517023	-0.844235975	-0.533004426	0.037406839

Dataset 1.Hits 1+2+3.

This dataset reports the frequencies of 66 height increasing alleles, 3 from each autosomal chromosome. Data derived from derived from 1000 Genomes, phase 3 data.

SNP	correlation pol.score	p value	correlation height
rs3814333 T	-0.790331762	4.80E-51	-0.435185412
rs3791679 A	0.899579663	2.40E-67	0.752462577
rs724016 G	0.782908171	3.20E-158	0.623793405
rs1812175 G	0.260514643	2.10E-86	0.747921959
rs9292468 T	-0.056466468	1.50E-33	0.361312015
rs4896582 G	-0.280513839	2.60E-55	0.548300183
rs42039 T	0.224436571	3.80E-88	0.749356619
rs4733724 A	-0.215307661	6.00E-30	0.649735752
rs817300 G	-0.054084056	4.30E-34	-0.746958959
chr.10 rs1923367 G	-0.409053397	4.90E-24	0.451896188
rs606452 A	-0.08379395	1.90E-23	-0.660979914
rs3825199 G	-0.169657534	3.90E-49	-0.387438596
rs3118905 G	-0.088977579	1.10E-69	-0.719340499
rs2093210 C	0.316524532	3.00E-35	-0.418902705
rs7162542 G	-0.431162869	8.20E-55	-0.77481394
rs26868 A	-0.797577694	3.20E-18	-0.436008702
rs2079795 T	0.626854356	1.70E-46	-0.25765619
rs4369779 C	-0.053813274	1.50E-53	-0.126728505
rs11880992 A	-0.25150588	6.90E-28	0.56541549
rs143384 G	0.836417304	1.20E-121	0.492622706
rs2834442 A	0.371484927	4.40E-15	0.437903524
rs738288 G	0.5248369	5.50E-11	0.648092852
rs2284746 G	-0.287897068	1.20E-40	0.48147312
rs2289195 A	0.287371972	2.40E-37	0.45172992
rs7652177 G	0.728064201	2.70E-39	0.016179505
rs7692995 T	-0.653099364	1.10E-71	0.183059635
rs7701414 G	0.476170804	1.30E-34	0.745732761
rs806794 A	0.709416032	4.60E-74	0.653774382
rs798497 A	0.407274032	2.20E-71	-0.229007987
rs10958476 C	-0.677208816	1.70E-40	0.005526059
rs7870753 G	0.847869832	3.50E-33	0.617755225
rs12779328 C	0.5730371	1.70E-17	-0.133724693
rs1681630 T	0.706675587	2.40E-20	0.330209833
rs8756 C	0.779231214	4.50E-90	0.8796947
rs7334755 C	-0.425362117	9.10E-15	-0.615096355
rs1950500 T	0.154407311	3.20E-22	-0.215518
rs2280470 A	-0.842688347	2.80E-44	-0.797214943
rs1659127 A	-0.643771536	2.80E-19	-0.600610521
rs2854207 G	0.889401824	1.30E-42	0.715808727
rs9967417 G	0.26005088	2.20E-40	0.679694216
rs2074977 C	-0.003611539	1.90E-20	-0.801339563
rs1884897 A	0.824961094	1.30E-48	0.794553472
rs2211866 A	-0.634425385	3.50E-13	-0.643900391
rs5757318 T	-0.852992768	1.90E-09	-0.630991547
rs9428104 G	0.289966862	2.80E-36	0.218109784
rs3116168 C	0.887755343	1.40E-31	0.825899289
rs2581830 T	0.875186477	4.40E-25	0.560947504
rs17556750 A	-0.472216002	8.30E-48	0.470238255
rs4868126 G	-0.593011472	2.80E-29	-0.325278253
rs12214804 C	0.576588768	1.50E-49	-0.167196297
rs552707 T	0.381759561	9.30E-46	0.871911706
rs4735677 T	-0.487482005	6.00E-30	0.230500158
rs7849585 T	0.750331279	1.10E-29	0.0534823
rs7899004 T	0.41740887	7.00E-17	0.636915448
rs2237886 T	-0.749460843	5.30E-18	-0.545711591
rs10748128 T	0.078561852	4.40E-29	-0.437152144
rs7319045 A	0.253547933	8.40E-15	-0.36538669
rs862034 G	-0.180683834	6.40E-20	-0.442070794
rs2871865 C	-0.747553706	1.70E-34	-0.179083175
rs11648796 G	-0.234098265	1.40E-18	-0.741175702
rs3760318 G	-0.755983086	3.00E-41	-0.703211417
rs11152213 C	0.351941269	6.90E-13	0.4677196
rs4803468 A	-0.860906364	1.70E-21	-0.708524126
rs1074683 C	0.173435412	7.70E-38	0.17154974
rs9977276 G	-0.718700559	2.90E-10	-0.845162738

Dataset 2.Method of correlated vectors (MCV).

This dataset reports SNP names, p value and the correlation between p value with poylgenic score (col.B) and average height (col.D). Data derived from 1000 Genomes, phase 3 data.

Polygenic score

Polygenic scores and average country height are reported in Table 1. The Pearson correlation between polygenic score and average country height was r=0.83 (N=11, p=0.002). Table 2 reports average frequencies by sub-continental populations.

Table 2. Frequencies of height increasing alleles for sub-continental populations.

Continent	Polygenic score (%)
AFR	47.69
AMR	45.92
ASN	45.52
EUR	46.65
SAS	46.549

Frequencies in descending order are: 1) Africans (AFR); 2) Europeans (EUR); 3) South Asians (SAS); 4) Latin Americans/Hispanics (AMR); 5) East Asians (ASN).

Method of correlated vectors (MCV)

Spearman’s rank order correlation between each allele’s p value and its correlation with the polygenic score and with height were respectively -0.26 and -0.34 (N=66, p=0.037 and 0.0053). The “rcorr” and “cor” functions in R produced slightly different results due to differences in dealing with ties (equal values). “cor” produced slightly stronger coefficients (-0.28 and -0.37).

This provides evidence for the hypothesis that more significant GWAS hits (alleles) are enriched with natural selection signal. A similar phenomenon was observed in a previous analysis of genes affecting human height (Piffer, 2014b).

Factor analysis of the top 5 hits

Factor analysis requires a satisfying cases to variable ratio, thus only a handful of SNPs could be used and these had necessarily to be those with the lowest p value, as they are more likely to be genuine hits (see previous section, MCV).

The top 5 alleles (i.e. those with the lowest p value) all correlated with the polygenic score and with average height in the expected direction (positively), as shown in Table 3 (see Dataset 2).The average correlations were 0.58 and 0.69, respectively, which is a significant improvement compared to the average of the correlations with polygenic score and height of all the 66 alleles (r=0.03 and 0.04, respectively; see Dataset 1, cells BP38–39).

Table 3. Top five SNPs

(p value and r with polygenic (pol) score).

SNP	rs724016.G	rs1812175.G	rs42039.T	rs143384.G	rs8756.C
GWAS p value	3.2E-158	2.1E-86	3.8E-88	1.2E-121	4.5E-90
r with pol. score	0.78	0.26	0.22	0.84	0.78
r with average pop. height	0.62	0.75	0.75	0.49	0.88

A factor analysis using minimum residuals was carried out. A single factor was extracted that explained 42% of the variance. Factor loadings are displayed in Table 4. These are all positive (in the expected direction).

Table 4. Top 5 SNPs

Standardized loadings (pattern matrix) based upon correlation matrix.

Gen.coordinate	SNP ID	Factor loading
142.588.260 (Chr.3)	rs724016.G	0.62
145.794.294 (Chr.4)	rs1812175.G	0.33
92.082.358 (Chr. 7)	rs42039.T	0.62
33.489.170 (Chr.20)	rs143384.G	0.48
64.646.019 (Chr.12)	rs8756.C	1

Factor scores were extracted with the Thurstone method (Thurstone, 1947), and are reported in Table 5.

Table 5. Factor scores.

Population	Height Top 5 SNP factor	Height (cm)
Afr.Car.Barbados	1.08
US Blacks	0.24	178.00
Esan Nigeria	1.29
Gambian	0.73
Luhya Kenya	0.38
Mende Sierra Leo	0.38
Yoruba	1.08
Colombian	0.08	170.60
Mexican LA	-0.27	172.00
Peruvian	0.15
Puerto Rican	0.44
Chinese Dai	-1.76
HanChineseBejing	-1.41	172.10
HanChineseSouth	-1.62	172.10
Japanese	-1.41	172.00
Vietnam	-1.69	165.70
UtahWhites	1.43	179.00
Finns	1.29	180.70
British	1.01	177.80
Spanish	0.58
TuscanItaly	0.72	177.00
Bengali Banglade	-0.41
Gujarati Ind. Tx	-0.41
Indian Telegu UK	-0.69
Punjabi Pakistan	-0.48
SriLankanUK	-0.70

The Pearson correlation between average country height and the factor score was strongly positive (r=0.88, N=11, p=0.001). This factor was also significantly correlated to the polygenic score (r=0.78, N=26, p<0.001).

Discussion

A polygenic score, created by averaging frequencies from 26 populations of 66 height increasing alleles by the largest and most recent human height GWAS, was positively correlated with the average height of 11 populations. The method of correlated vectors revealed that alleles with lower p values had a higher correlation with phenotypic height and polygenic score, suggesting that they tend to be enriched with signal of natural selection. A factor analysis of the top five GWAS hits produced a factor (whose loadings are all in the expected direction) which is significantly and strongly correlated both to population average height and to polygenic score. This showed an improvement over the correlation of the five single alleles with population height (Table 3, last row) which averaged 0.66, which in turn improved over the average correlation of the 66 alleles, which was near zero.

The rankings of polygenic scores match with the folk perception on the stature of various racial groups: Africans> Europeans> South/Central Asians> Hispanics> East Asians (Table 2).

South East Asians had the lowest scores, a result which matches with their anthropometric description.

Within Europe, northern Europeans (Finns and White Americans) had a higher genotypic stature than their southern counterparts (Italians and Spaniards), confirming the results from a previous study on GWAS loci which compared northern vs southern Europeans (Turchin et al., 2010).

A limitation was the unavailability of sound statistics on the average height of many populations. Moreover, although human height is largely heritable, it is also heavily influenced by nutrition and living conditions. The importance of environment is suggested by the dramatic secular trend which took place in the 20th century in developed countries (e.g. Arcaleni, 2006; Webb et al., 2008); an association with dietary intakes (i.e. milk consumption) and socioeconomic status has also been observed (Mamidi et al., 2011; Webb et al., 2008). Most of the missing data were for developing countries which likely have not reached their full growth potential or ethnic groups living in Western societies (Indian Telegu or Gujarati) for which anthropometric statistics are not easily available. If the allele frequency factor represents a genuine signal of natural selection, then the difference between it and current phenotypic height could be used as an indicator of the quality of diet and living conditions in general.

Conclusion

Factor analysis of allele frequencies is a promising method for detecting signals of recent selection on polygenic traits.

Data availability

F1000Research: Dataset 1. Hits 1+2+3. 10.5256/f1000research.6002.d41833 (Piffer, 2014c).

F1000Research: Dataset 2. Method of correlated vectors (MCV). 10.5256/f1000research.6002.d41834 (Piffer, 2014d).

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

References

Arcaleni E: Secular trend and regional differences in the stature of Italians, 1854–1980. Econ Hum Biol. 2006; 4(1): 24–38. PubMed Abstract | Publisher Full Text
Barsh GS, Copenhaver GP, Gibson G, et al.: Guidelines for genome-wide association studies. PLoS Genet. 2012; 8(7): e1002812. PubMed Abstract | Publisher Full Text | Free Full Text
Berg JJ, Coop G: A population genetic signal of polygenic adaptation. PLoS Genet. 2014; 10(8): e1004412. PubMed Abstract | Publisher Full Text | Free Full Text
Cacciari E, Milani S, Balsamo A, et al.: Italian cross-sectional growth charts for height, weight and BMI (6–20 y). Eur J Clin Nutr. 2002; 56(2): 171–80. PubMed Abstract
Hung MV, Pak S: The impact of environment on morphological and physical indexes of Vietnamese and South Korean students. VNU Journal of Science, Natural Science and Technology. 2008; 24: 50–55. Reference Source
Mamidi RS, Kulkarni B, Singh A: Secular trends in height in different states of India in relation to socioeconomic characteristics and dietary intakes. Food Nutr Bull. 2011; 32(1): 23–34. PubMed Abstract
McDowell MA, Fryar CD, Hirsch R, et al.: “Anthropometric Reference Data for Children and Adults: United States, 2003–2006”. National Health Statistics Reports. 2008; 10. Reference Source
Meisel A, Vega M: “A tropical success story: a century of improvements in the biological standard of living, Colombia 1910–2002”. Paper prepared for The Fifth World Congress of Cliometrics, Venice International University, Venice, Italy, July 8–11, 2004. Reference Source
Ministry of Education, Culture, Sports, Science and Technology. Japan, 2004. Reference Source
Moody A: 10: Adult anthropometric measures, overweight and obesity. In Craig, Rachel; Mindell, Jennifer. Health Survey for England – 2012. 2013. Reference Source
National Institute for Health and Welfare: Lasten kasvunseurannan uudistaminen, Asiantuntijaryhmän raportti. 2011. Reference Source
Piffer D: Factor Analysis of Population Allele Frequencies as a Simple, Novel Method of Detecting Signals of Recent Polygenic Selection: The Example of Educational Attainment and IQ. Mankind Quarterly. 2013; 54(2): 168200. Reference Source
Piffer D: Simple statistical tools to detect signals of recent polygenic selection. IBC. 2014a; 6(1); 1–6. Reference Source
Piffer D: Opposite selection pressure on stature and intelligence across human populations. Open Behavioral Genetics. 2014b. Reference Source
Piffer D: Hits 1+2+3. F1000Research. 2014c. Data Source
Piffer D: Method of correlated vectors (MCV). F1000Research. 2014d. Data Source
Thurstone LL: Multiple Factor Analysis. University of Chicago Press. 1947. Publisher Full Text
Turchin MC, Chiang CW, Palmer CD, et al.: Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nat Genet. 2012; 44(9): 1015–1019. PubMed Abstract | Publisher Full Text | Free Full Text
Webb EA, Kuh D, Pajak A, et al.: Estimation of secular trends in adult height, and childhood socioeconomic circumstances in three Eastern European populations. Econ Hum Biol. 2008; 6(2): 228–236. PubMed Abstract | Publisher Full Text
Wood AR, Esko T, Yang J, et al.: Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014; 46(11): 1173–86. PubMed Abstract | Publisher Full Text | Free Full Text
Yang XG, Li YP, Ma GS, et al.: [Study on weight and height of the Chinese people and the differences between 1992 and 2002]. Zhonghua Liu Xing Bing Xue Za Zhi. 2005; 26(7): 489–93. PubMed Abstract

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Ulster Insitute for Social Research, London, UK

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© 2015 Piffer D. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

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Piffer D. Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies [version 1; peer review: 1 approved with reservations]. F1000Research 2015, 4:15 (https://doi.org/10.12688/f1000research.6002.1)

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Reviewer Report 30 Oct 2015

Ben Busby, The School of Biochemistry, NCBI/NLM/NIH, Bethesda, MD, USA

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https://doi.org/10.5256/f1000research.6422.r9015

The only major thing that, in my opinion, stands between this manuscript is the availability of the R scripts used to produce the data tables (especially given the difference between corr and rcorr). I looked for any linking to or ... Continue reading

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Andrew D Kern, Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ, USA

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Not Approved

This paper is light on new results, so I'll focus my review purely on the conclusions mentioned by the author.

The author makes a single statement in the Conclusion section, that "Factor analysis of allele frequencies is a promising method for detecting signals of recent selection on polygenic traits." I don't see how that can be gleaned from this paper at all. The author has provided no justification for the method that he is using here, or in his other papers that he has cited in this manuscript which also use factor analysis. I was amazed by this but realize that the journal "Mankind Quarterly" isn't exactly known for its rigorous review of technical methods in genetics.

For this present manuscript to have any utility to the community I would suggest that the author back up and demonstrate that his ad hoc method has adequate power for use in empirical studies. In particular the author should compare the power of his method to the approach of Berg and Coop (2014)¹, which is been both mathematically justified and well characterized by simulation data.

References

1. Berg JJ, Coop G: A population genetic signal of polygenic adaptation.PLoS Genet. 2014; 10 (8): e1004412 PubMed Abstract | Publisher Full Text

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I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Ben Busby, The School of Biochemistry, NCBI/NLM/NIH, Bethesda, MD, USA

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Approved With Reservations

The only major thing that, in my opinion, stands between this manuscript is the availability of the R scripts used to produce the data tables (especially given the difference between corr and rcorr). I looked for any linking to or availability of these scripts in in Piffer 2013 or 2014a-d, and could not find it. Without these scripts, I do not think this work can be considered reproducible.

Two additional (minor) revisions are as follows.

First, the phenotypic data in table one seems to reply solely on Wikipedia. It seems likely that the author could find additional height information beyond Wikipedia.

Second, the last four paragraphs of the article could be combined into one.

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I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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[1] Arcaleni E: Secular trend and regional differences in the stature of Italians, 1854–1980. Econ Hum Biol. 2006; 4(1): 24–38. PubMed Abstract | Publisher Full Text

[2] Barsh GS, Copenhaver GP, Gibson G, et al.: Guidelines for genome-wide association studies. PLoS Genet. 2012; 8(7): e1002812. PubMed Abstract | Publisher Full Text | Free Full Text

[3] Berg JJ, Coop G: A population genetic signal of polygenic adaptation. PLoS Genet. 2014; 10(8): e1004412. PubMed Abstract | Publisher Full Text | Free Full Text

[4] Cacciari E, Milani S, Balsamo A, et al.: Italian cross-sectional growth charts for height, weight and BMI (6–20 y). Eur J Clin Nutr. 2002; 56(2): 171–80. PubMed Abstract

[5] Hung MV, Pak S: The impact of environment on morphological and physical indexes of Vietnamese and South Korean students. VNU Journal of Science, Natural Science and Technology. 2008; 24: 50–55. Reference Source

[6] Mamidi RS, Kulkarni B, Singh A: Secular trends in height in different states of India in relation to socioeconomic characteristics and dietary intakes. Food Nutr Bull. 2011; 32(1): 23–34. PubMed Abstract

[7] McDowell MA, Fryar CD, Hirsch R, et al.: “Anthropometric Reference Data for Children and Adults: United States, 2003–2006”. National Health Statistics Reports. 2008; 10. Reference Source

[8] Meisel A, Vega M: “A tropical success story: a century of improvements in the biological standard of living, Colombia 1910–2002”. Paper prepared for The Fifth World Congress of Cliometrics, Venice International University, Venice, Italy, July 8–11, 2004. Reference Source

[9] Ministry of Education, Culture, Sports, Science and Technology. Japan, 2004. Reference Source

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Evidence of polygenic selection on human stature inferred from spatial distribution of allele frequencies

Abstract

Keywords

Introduction

Methods

Table 1. Polygenic score and height.

Results

Polygenic score

Table 2. Frequencies of height increasing alleles for sub-continental populations.

Method of correlated vectors (MCV)

Factor analysis of the top 5 hits

Table 3. Top five SNPs

Table 4. Top 5 SNPs

Table 5. Factor scores.

Discussion

Conclusion

Data availability

Competing interests

Grant information

References

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