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Population Genetics: Why structure matters

  1. Nick Barton  Is a corresponding author
  2. Joachim Hermisson  Is a corresponding author
  3. Magnus Nordborg  Is a corresponding author
  1. IST Austria, Austria
  2. University of Vienna, Austria
  3. Austrian Academy of Sciences, Vienna BioCenter, Austria
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Cite this article as: eLife 2019;8:e45380 doi: 10.7554/eLife.45380

Abstract

Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.

Main text

Human height is the classic example of a quantitative trait: its distribution is continuous, presumably because it is influenced by variation at a very large number of genes, most with a small effect (Fisher, 1918). Yet height is also strongly affected by the environment: average height in many countries increased during the last century and the children of immigrants are often taller than relatives in their country of origin – in both cases presumably due to changing diet and other environmental factors (Cavalli-Sforza and Bodmer, 1971; Grasgruber et al., 2016; NCD Risk Factor Collaboration, 2016). This makes it very difficult to determine the cause of geographic patterns for height, such as the ‘latitudinal cline’ seen in Europe (Figure 1).

Distribution of average male height in Europe, calculated from studies performed between 1999–2013.

In general, southern Europeans tend to be shorter than northern Europeans. Image reproduced from Grasgruber et al., 2014 (CC BY 3.0).

Are such patterns caused by environmental or genetic differences – or by a complex combination of both? And to the extent that genetic differences are involved, do they reflect selection or simply random history? A number of recent papers have relied on so-called Genome-Wide Association Studies (GWAS) to address these questions, and reported strong evidence for both genetics and selection. Now, in eLife, two papers – one by Jeremy Berg, Arbel Harpak, Nasa Sinnott-Armstrong and colleagues (Berg et al., 2019); the other by Mashaal Sohail, Robert Maier and colleagues (Sohail et al., 2019) – independently reject these conclusions. Even more importantly, they identify problems with GWAS that have broader implications for human genetics.

As the name suggests, GWAS scan the genome for variants – typically single nucleotide polymorphisms (SNPs) – that are associated with a particular condition or trait (phenotype). The first GWAS for height found a small number of SNPs that jointly explained only a tiny fraction of the variation. Because this was in contrast with the high heritability seen in twin studies, it was dubbed ‘the missing heritability problem’ (reviewed in Yang et al., 2010). It was suggested that the problem was simply due to a lack of statistical power to detect polymorphisms of small effect. Subsequent studies with larger sample sizes have supported this explanation: more and more loci have been identified although most of the variation remains ‘unmappable’, presumably because sample sizes on the order of a million are still not large enough (Yengo et al., 2018).

One way in which the unmappable component of genetic variation can be included in a statistical measure is via so-called polygenic scores. These scores sum the estimated contributions to the trait across many SNPs, including those whose effects, on their own, are not statistically significant. Polygenic scores thus represent a shift from the goal of identifying major genes to predicting phenotype from genotype. Originally designed for plant and animal breeding purposes, polygenic scores can, in principle, also be used to study the genetic basis of differences between individuals and groups.

This, however, requires accurate and unbiased estimation of the effects of all SNPs included in the score, which is difficult in a structured (non-homogeneous) population when environmental differences cannot be controlled. To see why this is a problem, consider the classic example of chopstick-eating skills (Lander and Schork, 1994). While there surely are genetic variants affecting our ability to handle chopsticks, most of the variation for this trait across the globe is due to environmental differences (cultural background), and a GWAS would mostly identify variants that had nothing to do with chopstick skills, but simply happened to differ in frequency between East Asia and the rest of the world.

Several methods for dealing with this problem have been proposed. When a GWAS is carried out to identify major genes, it is relatively simple to avoid false positives by eliminating associations outside major loci regardless of whether they are due to population structure confounding or an unmappable polygenic background (Vilhjálmsson and Nordborg, 2013). However, if the goal is to make predictions, or to understand differences among populations (such as the latitudinal cline in height), we need accurate and unbiased estimates for all SNPs. Accomplishing this is extremely challenging, and it is also difficult to know whether one has succeeded.

One possibility is to compare the population estimates with estimates taken from sibling data, which should be relatively unbiased by environmental differences. In one of many examples of this, Robinson et al. used data from the GIANT Consortium (Wood et al., 2014) together with sibling data to estimate that genetic variation contributes significantly to height variation across Europe (Robinson et al., 2015). They also argued that selection must have occurred, because the differences were too large to have arisen by chance. Using estimated effect sizes provided by Robinson et al., a more sophisticated analysis by Field et al. found extremely strong evidence for selection for height across Europe (p=10−74; Field et al., 2016). Several other studies reached the same conclusion based on the GIANT data (reviewed in Berg et al., 2019; Sohail et al., 2019).

Berg et al. (who are based at Columbia University, Stanford University, UC Davis and the University of Copenhagen) and Sohail et al. (who are based at Harvard Medical School, the Broad Institute, and other institutes in the US, Finland and Sweden) now re-examine these conclusions using the recently released data from the UK Biobank (Sudlow et al., 2015). Estimating effect sizes from these data allows possible biases due to population structure confounding to be investigated, because the UK Biobank data comes from a (supposedly) more homogenous population than the GIANT data.

Using these new estimates, Berg et al. and Sohail et al. independently found that evidence for selection vanishes – along with evidence for a genetic cline in height across Europe. Instead, they show that the previously published results were due to the cumulative effects of slight biases in the effect-size estimates in the GIANT data. Surprisingly, they also found evidence for confounding in the sibling data used as a control by Robinson et al. and Field et al. This turned out to be due to a technical error in the data distributed by Robinson et al. after they published their paper.

This means we still do not know whether genetics and selection are responsible for the pattern of height differences seen across Europe. That genetics plays a major role in height differences between individuals is not in doubt, and it is also clear that the signal from GWAS is mostly real. The issue is that there is no perfect way to control for complex population structure and environmental heterogeneity. Biases at individual loci may be tiny, but they become highly significant when summed across thousands of loci – as is done in polygenic scores. Standard methods to control for these biases, such as principal component analysis, may work well in simulations but are often insufficient when confronted with real data. Importantly, no natural population is unstructured: indeed, even the data in the UK Biobank seems to contain significant structure (Haworth et al., 2019).

Berg et al. and Sohail et al. demonstrate the potential for population structure to create spurious results, especially when using methods that rely on large numbers of small effects, such as polygenic scores. Caution is clearly needed when interpreting and using the results of such studies. For clinical predictions, risks must be weighed against benefits (Rosenberg et al., 2019). In some cases, such as recommendations for more frequent medical checkups for patients found at higher ‘genetic’ risk of a condition, it may not matter greatly whether predictors are confounded as long as they work. By contrast, the results of behavioral studies of traits such as IQ and educational attainment (Plomin and von Stumm, 2018) must be presented carefully, because while the benefits are far from obvious, the risks of such results being misinterpreted and misused are quite clear. The problem is worsened by the tendency of popular media to ignore caveats and uncertainties of estimates.

Finally, although quantitative genetics has proved highly successful in plant and animal breeding, it should be remembered that this success has been based on large pedigrees, well-controlled environments, and short-term prediction. When these methods have been applied to natural populations, even the most basic predictions fail, in large part due to poorly understood environmental factors (Charmantier et al., 2014). Natural populations are never homogeneous, and it is therefore misleading to imply there is a qualitative difference between ‘within-population’ and ‘between-population’ comparisons – as was recently done in connection with James Watson’s statements about race and IQ (Harmon, 2019). With respect to confounding by population structure, the key qualitative difference is between controlling the environment experimentally, and not doing so. Once we leave an experimental setting, we are effectively skating on thin ice, and whether the ice will hold depends on how far out we skate.

References

  1. Book
    1. Cavalli-Sforza LL
    2. Bodmer WF
    (1971)
    The Genetics of Human Populations
    San Francisco: WH Freeman.
    1. Wood AR
    2. Esko T
    3. Yang J
    4. Vedantam S
    5. Pers TH
    6. Gustafsson S
    7. Chu AY
    8. Estrada K
    9. Luan J
    10. Kutalik Z
    11. Amin N
    12. Buchkovich ML
    13. Croteau-Chonka DC
    14. Day FR
    15. Duan Y
    16. Fall T
    17. Fehrmann R
    18. Ferreira T
    19. Jackson AU
    20. Karjalainen J
    21. Lo KS
    22. Locke AE
    23. Mägi R
    24. Mihailov E
    25. Porcu E
    26. Randall JC
    27. Scherag A
    28. Vinkhuyzen AA
    29. Westra HJ
    30. Winkler TW
    31. Workalemahu T
    32. Zhao JH
    33. Absher D
    34. Albrecht E
    35. Anderson D
    36. Baron J
    37. Beekman M
    38. Demirkan A
    39. Ehret GB
    40. Feenstra B
    41. Feitosa MF
    42. Fischer K
    43. Fraser RM
    44. Goel A
    45. Gong J
    46. Justice AE
    47. Kanoni S
    48. Kleber ME
    49. Kristiansson K
    50. Lim U
    51. Lotay V
    52. Lui JC
    53. Mangino M
    54. Mateo Leach I
    55. Medina-Gomez C
    56. Nalls MA
    57. Nyholt DR
    58. Palmer CD
    59. Pasko D
    60. Pechlivanis S
    61. Prokopenko I
    62. Ried JS
    63. Ripke S
    64. Shungin D
    65. Stancáková A
    66. Strawbridge RJ
    67. Sung YJ
    68. Tanaka T
    69. Teumer A
    70. Trompet S
    71. van der Laan SW
    72. van Setten J
    73. Van Vliet-Ostaptchouk JV
    74. Wang Z
    75. Yengo L
    76. Zhang W
    77. Afzal U
    78. Arnlöv J
    79. Arscott GM
    80. Bandinelli S
    81. Barrett A
    82. Bellis C
    83. Bennett AJ
    84. Berne C
    85. Blüher M
    86. Bolton JL
    87. Böttcher Y
    88. Boyd HA
    89. Bruinenberg M
    90. Buckley BM
    91. Buyske S
    92. Caspersen IH
    93. Chines PS
    94. Clarke R
    95. Claudi-Boehm S
    96. Cooper M
    97. Daw EW
    98. De Jong PA
    99. Deelen J
    100. Delgado G
    101. Denny JC
    102. Dhonukshe-Rutten R
    103. Dimitriou M
    104. Doney AS
    105. Dörr M
    106. Eklund N
    107. Eury E
    108. Folkersen L
    109. Garcia ME
    110. Geller F
    111. Giedraitis V
    112. Go AS
    113. Grallert H
    114. Grammer TB
    115. Gräßler J
    116. Grönberg H
    117. de Groot LC
    118. Groves CJ
    119. Haessler J
    120. Hall P
    121. Haller T
    122. Hallmans G
    123. Hannemann A
    124. Hartman CA
    125. Hassinen M
    126. Hayward C
    127. Heard-Costa NL
    128. Helmer Q
    129. Hemani G
    130. Henders AK
    131. Hillege HL
    132. Hlatky MA
    133. Hoffmann W
    134. Hoffmann P
    135. Holmen O
    136. Houwing-Duistermaat JJ
    137. Illig T
    138. Isaacs A
    139. James AL
    140. Jeff J
    141. Johansen B
    142. Johansson Å
    143. Jolley J
    144. Juliusdottir T
    145. Junttila J
    146. Kho AN
    147. Kinnunen L
    148. Klopp N
    149. Kocher T
    150. Kratzer W
    151. Lichtner P
    152. Lind L
    153. Lindström J
    154. Lobbens S
    155. Lorentzon M
    156. Lu Y
    157. Lyssenko V
    158. Magnusson PK
    159. Mahajan A
    160. Maillard M
    161. McArdle WL
    162. McKenzie CA
    163. McLachlan S
    164. McLaren PJ
    165. Menni C
    166. Merger S
    167. Milani L
    168. Moayyeri A
    169. Monda KL
    170. Morken MA
    171. Müller G
    172. Müller-Nurasyid M
    173. Musk AW
    174. Narisu N
    175. Nauck M
    176. Nolte IM
    177. Nöthen MM
    178. Oozageer L
    179. Pilz S
    180. Rayner NW
    181. Renstrom F
    182. Robertson NR
    183. Rose LM
    184. Roussel R
    185. Sanna S
    186. Scharnagl H
    187. Scholtens S
    188. Schumacher FR
    189. Schunkert H
    190. Scott RA
    191. Sehmi J
    192. Seufferlein T
    193. Shi J
    194. Silventoinen K
    195. Smit JH
    196. Smith AV
    197. Smolonska J
    198. Stanton AV
    199. Stirrups K
    200. Stott DJ
    201. Stringham HM
    202. Sundström J
    203. Swertz MA
    204. Syvänen AC
    205. Tayo BO
    206. Thorleifsson G
    207. Tyrer JP
    208. van Dijk S
    209. van Schoor NM
    210. van der Velde N
    211. van Heemst D
    212. van Oort FV
    213. Vermeulen SH
    214. Verweij N
    215. Vonk JM
    216. Waite LL
    217. Waldenberger M
    218. Wennauer R
    219. Wilkens LR
    220. Willenborg C
    221. Wilsgaard T
    222. Wojczynski MK
    223. Wong A
    224. Wright AF
    225. Zhang Q
    226. Arveiler D
    227. Bakker SJ
    228. Beilby J
    229. Bergman RN
    230. Bergmann S
    231. Biffar R
    232. Blangero J
    233. Boomsma DI
    234. Bornstein SR
    235. Bovet P
    236. Brambilla P
    237. Brown MJ
    238. Campbell H
    239. Caulfield MJ
    240. Chakravarti A
    241. Collins R
    242. Collins FS
    243. Crawford DC
    244. Cupples LA
    245. Danesh J
    246. de Faire U
    247. den Ruijter HM
    248. Erbel R
    249. Erdmann J
    250. Eriksson JG
    251. Farrall M
    252. Ferrannini E
    253. Ferrières J
    254. Ford I
    255. Forouhi NG
    256. Forrester T
    257. Gansevoort RT
    258. Gejman PV
    259. Gieger C
    260. Golay A
    261. Gottesman O
    262. Gudnason V
    263. Gyllensten U
    264. Haas DW
    265. Hall AS
    266. Harris TB
    267. Hattersley AT
    268. Heath AC
    269. Hengstenberg C
    270. Hicks AA
    271. Hindorff LA
    272. Hingorani AD
    273. Hofman A
    274. Hovingh GK
    275. Humphries SE
    276. Hunt SC
    277. Hypponen E
    278. Jacobs KB
    279. Jarvelin MR
    280. Jousilahti P
    281. Jula AM
    282. Kaprio J
    283. Kastelein JJ
    284. Kayser M
    285. Kee F
    286. Keinanen-Kiukaanniemi SM
    287. Kiemeney LA
    288. Kooner JS
    289. Kooperberg C
    290. Koskinen S
    291. Kovacs P
    292. Kraja AT
    293. Kumari M
    294. Kuusisto J
    295. Lakka TA
    296. Langenberg C
    297. Le Marchand L
    298. Lehtimäki T
    299. Lupoli S
    300. Madden PA
    301. Männistö S
    302. Manunta P
    303. Marette A
    304. Matise TC
    305. McKnight B
    306. Meitinger T
    307. Moll FL
    308. Montgomery GW
    309. Morris AD
    310. Morris AP
    311. Murray JC
    312. Nelis M
    313. Ohlsson C
    314. Oldehinkel AJ
    315. Ong KK
    316. Ouwehand WH
    317. Pasterkamp G
    318. Peters A
    319. Pramstaller PP
    320. Price JF
    321. Qi L
    322. Raitakari OT
    323. Rankinen T
    324. Rao DC
    325. Rice TK
    326. Ritchie M
    327. Rudan I
    328. Salomaa V
    329. Samani NJ
    330. Saramies J
    331. Sarzynski MA
    332. Schwarz PE
    333. Sebert S
    334. Sever P
    335. Shuldiner AR
    336. Sinisalo J
    337. Steinthorsdottir V
    338. Stolk RP
    339. Tardif JC
    340. Tönjes A
    341. Tremblay A
    342. Tremoli E
    343. Virtamo J
    344. Vohl MC
    345. Amouyel P
    346. Asselbergs FW
    347. Assimes TL
    348. Bochud M
    349. Boehm BO
    350. Boerwinkle E
    351. Bottinger EP
    352. Bouchard C
    353. Cauchi S
    354. Chambers JC
    355. Chanock SJ
    356. Cooper RS
    357. de Bakker PI
    358. Dedoussis G
    359. Ferrucci L
    360. Franks PW
    361. Froguel P
    362. Groop LC
    363. Haiman CA
    364. Hamsten A
    365. Hayes MG
    366. Hui J
    367. Hunter DJ
    368. Hveem K
    369. Jukema JW
    370. Kaplan RC
    371. Kivimaki M
    372. Kuh D
    373. Laakso M
    374. Liu Y
    375. Martin NG
    376. März W
    377. Melbye M
    378. Moebus S
    379. Munroe PB
    380. Njølstad I
    381. Oostra BA
    382. Palmer CN
    383. Pedersen NL
    384. Perola M
    385. Pérusse L
    386. Peters U
    387. Powell JE
    388. Power C
    389. Quertermous T
    390. Rauramaa R
    391. Reinmaa E
    392. Ridker PM
    393. Rivadeneira F
    394. Rotter JI
    395. Saaristo TE
    396. Saleheen D
    397. Schlessinger D
    398. Slagboom PE
    399. Snieder H
    400. Spector TD
    401. Strauch K
    402. Stumvoll M
    403. Tuomilehto J
    404. Uusitupa M
    405. van der Harst P
    406. Völzke H
    407. Walker M
    408. Wareham NJ
    409. Watkins H
    410. Wichmann HE
    411. Wilson JF
    412. Zanen P
    413. Deloukas P
    414. Heid IM
    415. Lindgren CM
    416. Mohlke KL
    417. Speliotes EK
    418. Thorsteinsdottir U
    419. Barroso I
    420. Fox CS
    421. North KE
    422. Strachan DP
    423. Beckmann JS
    424. Berndt SI
    425. Boehnke M
    426. Borecki IB
    427. McCarthy MI
    428. Metspalu A
    429. Stefansson K
    430. Uitterlinden AG
    431. van Duijn CM
    432. Franke L
    433. Willer CJ
    434. Price AL
    435. Lettre G
    436. Loos RJ
    437. Weedon MN
    438. Ingelsson E
    439. O'Connell JR
    440. Abecasis GR
    441. Chasman DI
    442. Goddard ME
    443. Visscher PM
    444. Hirschhorn JN
    445. Frayling TM
    446. Electronic Medical Records and Genomics (eMEMERGEGE) Consortium MIGen Consortium PAGEGE Consortium LifeLines Cohort Study
    (2014) Defining the role of common variation in the genomic and biological architecture of adult human height
    Nature Genetics 46:1173–1186.
    https://doi.org/10.1038/ng.3097

Article and author information

Author details

  1. Nick Barton

    Nick Barton is at IST Austria, Klosterneuburg, Austria

    For correspondence
    nick.barton@ist-austria.ac.at
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8548-5240
  2. Joachim Hermisson

    Joachim Hermisson is at the Department of Mathematics and at the Max F. Perutz Laboratories, University of Vienna, Vienna, Austria

    For correspondence
    joachim.hermisson@univie.ac.at
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7476-9283
  3. Magnus Nordborg

    Magnus Nordborg is at the Gregor Mendel Institute, Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria

    For correspondence
    magnus.nordborg@gmi.oeaw.ac.at
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7178-9748

Acknowledgements

We thank Jeremy Berg and Peter Visscher for answering our questions, and Molly Przeworski for helpful discussions.

Publication history

  1. Version of Record published: March 21, 2019 (version 1)

Copyright

© 2019, Barton et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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