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Genetic Variation: Searching for solutions to the missing heritability problem

  1. Luisa F Pallares  Is a corresponding author
  1. Princeton University, United States
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Cite this article as: eLife 2019;8:e53018 doi: 10.7554/eLife.53018

Abstract

Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth.

Main text

Although most of the 3000 million nucleotides in the human genome are the same in every person on the planet, there are about 90 million sites that can vary between individuals. The source of all phenotypic variation in humans lies in these 90 million genetic variants, and in their interactions with each other and with the environment. Identifying the genetic variants that are involved in a specific trait (such as height or disease status) is a long-standing goal in biology.

Today, researchers rely on genome-wide association studies (GWAS) to find the genetic variants that are relevant to a specific trait. In GWAS the genomes of individuals are analyzed to see if particular genetic variants are correlated with variation in traits of interest. GWAS results have identified hundreds of variants underlying phenotypic variation in humans, mice, fruit flies, rice, maize, and many other taxa. Yet, despite the large number of alleles that have been identified using this technique, the amount of phenotypic variation they explain is just a fraction of what twin and pedigree studies predict is heritable. For example, twin studies have shown that approximately 80% of variation in human height can be explained by genetic factors (Silventoinen et al., 2012). However, the results of the best powered GWAS only explain around 20% of such variation (Wood et al., 2014). This gap is known as the ‘missing heritability problem’.

Rare and low-frequency genetic variants (which have allele frequencies of <1% and <5% respectively) have been proposed as one explanation for the missing heritability problem (reviewed in Gibson, 2012). Such variants are routinely excluded from GWAS studies because when an allele is present in few individuals, the statistical analysis used to draw correlations between traits and alleles is not powerful enough to obtain significant results. As a consequence around 90% of genetic variation in humans and other organisms like yeast has so far gone unexplored (Figure 1AAuton et al., 2015; Peter et al., 2018). The missing heritability might be hiding in plain sight, but until now, studying the effect of rare alleles on the variation of traits influenced by more than one gene was extremely challenging. Now, in eLife, two independent groups report the results of experiments on yeast which show that rare variants have a fundamental role in phenotypic variation at the population level.

Allele frequency in natural isolates of yeast and in the experimental populations.

(A) Based on a study of 1011 genomes it is known that 93% of the genetic variants in the yeast Saccharomyces cerevisiae are rare (that is, they have a frequency <1%; blue). Moreover, just over 508000 variants (31% of the total; dotted blue) were found in just 1 of the 1011 genomes studied. However, genome-wide association studies (GWAS) tend to focus on the 7% of genetic variants that are common (that is, have a frequency >1%; pink). (B) The frequency of a rare allele can be increased by crossing a yeast isolate carrying the rare variant with an isolate with the alternative (more common) variant. To obtain a variety of isolates with a specific rare allele in different genetic backgrounds, the isolate carrying the rare variant (allele A, dark red) can be crossed with several different isolates with the alternative allele (allele G, pink, yellow, blue, grey). As a result, allele A is more frequent in the experimental panel than in the parental isolates, making it suitable for GWAS analysis. Importantly, regardless of the frequency that any allele reaches in the experimental panel, the real natural frequency can be looked up in the collection of 1011 yeast genomes (panel A).

In a monumental effort, the two groups independently selected a set of wild and domesticated yeast isolates from all over the world and crossed them to generate a genetically diverse panel of thousands of strains (Figure 1B). They then exposed each cross to more than 35 different media conditions and quantified their growth by measuring colony size. As a result of the crossing scheme, genetic variants that were present in just one or a few yeast isolates were now present in hundreds of samples in the experimental panels (Figure 1B). This allowed the groups to include a large number of rare variants (up to 28% of the total) in the GWAS analysis: many of these variants would have been excluded from traditional GWAS studies due to their low allele frequency.

Both groups independently identified thousands of genetic variants associated with growth, and estimated that over half of growth variance can be attributed to additive effects. To determine how variants with different frequencies contributed to phenotypic effects, variants were classified into either rare (<1%) and common (>1%) (Bloom et al., 2019), or rare (<1%), low frequency (1–5%) and common (>5%) (Fournier et al., 2019). This classification was based on 1011 yeast genomes that represent global yeast diversity (Figure 1A; Peter et al., 2018). Strikingly, rare variants contributed a disproportionate amount to phenotypic variation in both studies.

In one study Joseph Schacherer and co-workers at the University of Strasbourg – including Téo Fournier as first author – found that 16% of the GWAS results were rare alleles even when they made up just 4% of all the variants used in the experiments (Fournier et al., 2019). In the other study Joshua Bloom, Leonid Kruglyak and colleagues at UCLA estimated that over half of the observed growth variation can be explained by rare variants, even when they represented only 28% of the variants used (Bloom et al., 2019). The UCLA team also found that the rare variants detected in GWAS tend to have larger effect sizes than common variants, tend to reduce growth ability, and tend to have arisen more recently in evolutionary time.

These results join recent efforts exploring the effect of rare variants on complex traits. For human height it has been shown that rare variants have effect sizes ten times larger than common variants (Marouli et al., 2017), and that together they account for most of the missing heritability in this trait (Wainschtein et al., 2019). In parallel, it was estimated that at least a quarter of gene expression heritability in humans is accounted for by rare variants (Hernandez et al., 2019). The fact that in humans, as well as yeast, the contribution of rare variants to complex traits is now beyond doubt suggests that it may be the same in other species. However, addressing this question in organisms with larger genomes and not amenable to crossing schemes remains challenging. But rest assured, researchers will find a way.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
    Rare and low-frequency coding variants alter human adult height
    1. E Marouli
    2. M Graff
    3. C Medina-Gomez
    4. KS Lo
    5. AR Wood
    6. TR Kjaer
    7. RS Fine
    8. Y Lu
    9. C Schurmann
    10. HM Highland
    11. S Rüeger
    12. G Thorleifsson
    13. AE Justice
    14. D Lamparter
    15. KE Stirrups
    16. V Turcot
    17. KL Young
    18. TW Winkler
    19. T Esko
    20. T Karaderi
    21. AE Locke
    22. NG Masca
    23. MC Ng
    24. P Mudgal
    25. MA Rivas
    26. S Vedantam
    27. A Mahajan
    28. X Guo
    29. G Abecasis
    30. KK Aben
    31. LS Adair
    32. DS Alam
    33. E Albrecht
    34. KH Allin
    35. M Allison
    36. P Amouyel
    37. EV Appel
    38. D Arveiler
    39. FW Asselbergs
    40. PL Auer
    41. B Balkau
    42. B Banas
    43. LE Bang
    44. M Benn
    45. S Bergmann
    46. LF Bielak
    47. M Blüher
    48. H Boeing
    49. E Boerwinkle
    50. CA Böger
    51. LL Bonnycastle
    52. J Bork-Jensen
    53. ML Bots
    54. EP Bottinger
    55. DW Bowden
    56. I Brandslund
    57. G Breen
    58. MH Brilliant
    59. L Broer
    60. AA Burt
    61. AS Butterworth
    62. DJ Carey
    63. MJ Caulfield
    64. JC Chambers
    65. DI Chasman
    66. YI Chen
    67. R Chowdhury
    68. C Christensen
    69. AY Chu
    70. M Cocca
    71. FS Collins
    72. JP Cook
    73. J Corley
    74. JC Galbany
    75. AJ Cox
    76. G Cuellar-Partida
    77. J Danesh
    78. G Davies
    79. PI de Bakker
    80. GJ de Borst
    81. S de Denus
    82. MC de Groot
    83. R de Mutsert
    84. IJ Deary
    85. G Dedoussis
    86. EW Demerath
    87. AI den Hollander
    88. JG Dennis
    89. E Di Angelantonio
    90. F Drenos
    91. M Du
    92. AM Dunning
    93. DF Easton
    94. T Ebeling
    95. TL Edwards
    96. PT Ellinor
    97. P Elliott
    98. E Evangelou
    99. AE Farmaki
    100. JD Faul
    101. MF Feitosa
    102. S Feng
    103. E Ferrannini
    104. MM Ferrario
    105. J Ferrieres
    106. JC Florez
    107. I Ford
    108. M Fornage
    109. PW Franks
    110. R Frikke-Schmidt
    111. TE Galesloot
    112. W Gan
    113. I Gandin
    114. P Gasparini
    115. V Giedraitis
    116. A Giri
    117. G Girotto
    118. SD Gordon
    119. P Gordon-Larsen
    120. M Gorski
    121. N Grarup
    122. ML Grove
    123. V Gudnason
    124. S Gustafsson
    125. T Hansen
    126. KM Harris
    127. TB Harris
    128. AT Hattersley
    129. C Hayward
    130. L He
    131. IM Heid
    132. K Heikkilä
    133. Ø Helgeland
    134. J Hernesniemi
    135. AW Hewitt
    136. LJ Hocking
    137. M Hollensted
    138. OL Holmen
    139. GK Hovingh
    140. JM Howson
    141. CB Hoyng
    142. PL Huang
    143. K Hveem
    144. MA Ikram
    145. E Ingelsson
    146. AU Jackson
    147. JH Jansson
    148. GP Jarvik
    149. GB Jensen
    150. MA Jhun
    151. Y Jia
    152. X Jiang
    153. S Johansson
    154. ME Jørgensen
    155. T Jørgensen
    156. P Jousilahti
    157. JW Jukema
    158. B Kahali
    159. RS Kahn
    160. M Kähönen
    161. PR Kamstrup
    162. S Kanoni
    163. J Kaprio
    164. M Karaleftheri
    165. SL Kardia
    166. F Karpe
    167. F Kee
    168. R Keeman
    169. LA Kiemeney
    170. H Kitajima
    171. KB Kluivers
    172. T Kocher
    173. P Komulainen
    174. J Kontto
    175. JS Kooner
    176. C Kooperberg
    177. P Kovacs
    178. J Kriebel
    179. H Kuivaniemi
    180. S Küry
    181. J Kuusisto
    182. M La Bianca
    183. M Laakso
    184. TA Lakka
    185. EM Lange
    186. LA Lange
    187. CD Langefeld
    188. C Langenberg
    189. EB Larson
    190. IT Lee
    191. T Lehtimäki
    192. CE Lewis
    193. H Li
    194. J Li
    195. R Li-Gao
    196. H Lin
    197. LA Lin
    198. X Lin
    199. L Lind
    200. J Lindström
    201. A Linneberg
    202. Y Liu
    203. Y Liu
    204. A Lophatananon
    205. J Luan
    206. SA Lubitz
    207. LP Lyytikäinen
    208. DA Mackey
    209. PA Madden
    210. AK Manning
    211. S Männistö
    212. G Marenne
    213. J Marten
    214. NG Martin
    215. AL Mazul
    216. K Meidtner
    217. A Metspalu
    218. P Mitchell
    219. KL Mohlke
    220. DO Mook-Kanamori
    221. A Morgan
    222. AD Morris
    223. AP Morris
    224. M Müller-Nurasyid
    225. PB Munroe
    226. MA Nalls
    227. M Nauck
    228. CP Nelson
    229. M Neville
    230. SF Nielsen
    231. K Nikus
    232. PR Njølstad
    233. BG Nordestgaard
    234. I Ntalla
    235. JR O'Connel
    236. H Oksa
    237. LM Loohuis
    238. RA Ophoff
    239. KR Owen
    240. CJ Packard
    241. S Padmanabhan
    242. CN Palmer
    243. G Pasterkamp
    244. AP Patel
    245. A Pattie
    246. O Pedersen
    247. PL Peissig
    248. GM Peloso
    249. CE Pennell
    250. M Perola
    251. JA Perry
    252. JR Perry
    253. TN Person
    254. A Pirie
    255. O Polasek
    256. D Posthuma
    257. OT Raitakari
    258. A Rasheed
    259. R Rauramaa
    260. DF Reilly
    261. AP Reiner
    262. F Renström
    263. PM Ridker
    264. JD Rioux
    265. N Robertson
    266. A Robino
    267. O Rolandsson
    268. I Rudan
    269. KS Ruth
    270. D Saleheen
    271. V Salomaa
    272. NJ Samani
    273. K Sandow
    274. Y Sapkota
    275. N Sattar
    276. MK Schmidt
    277. PJ Schreiner
    278. MB Schulze
    279. RA Scott
    280. MP Segura-Lepe
    281. S Shah
    282. X Sim
    283. S Sivapalaratnam
    284. KS Small
    285. AV Smith
    286. JA Smith
    287. L Southam
    288. TD Spector
    289. EK Speliotes
    290. JM Starr
    291. V Steinthorsdottir
    292. HM Stringham
    293. M Stumvoll
    294. P Surendran
    295. LM 't Hart
    296. KE Tansey
    297. JC Tardif
    298. KD Taylor
    299. A Teumer
    300. DJ Thompson
    301. U Thorsteinsdottir
    302. BH Thuesen
    303. A Tönjes
    304. G Tromp
    305. S Trompet
    306. E Tsafantakis
    307. J Tuomilehto
    308. A Tybjaerg-Hansen
    309. JP Tyrer
    310. R Uher
    311. AG Uitterlinden
    312. S Ulivi
    313. SW van der Laan
    314. AR Van Der Leij
    315. CM van Duijn
    316. NM van Schoor
    317. J van Setten
    318. A Varbo
    319. TV Varga
    320. R Varma
    321. DR Edwards
    322. SH Vermeulen
    323. H Vestergaard
    324. V Vitart
    325. TF Vogt
    326. D Vozzi
    327. M Walker
    328. F Wang
    329. CA Wang
    330. S Wang
    331. Y Wang
    332. NJ Wareham
    333. HR Warren
    334. J Wessel
    335. SM Willems
    336. JG Wilson
    337. DR Witte
    338. MO Woods
    339. Y Wu
    340. H Yaghootkar
    341. J Yao
    342. P Yao
    343. LM Yerges-Armstrong
    344. R Young
    345. E Zeggini
    346. X Zhan
    347. W Zhang
    348. JH Zhao
    349. W Zhao
    350. W Zhao
    351. H Zheng
    352. W Zhou
    353. EPIC-InterAct Consortium, CHD Exome+ Consortium, ExomeBP Consortium, T2D-Genes Consortium, GoT2D Genes Consortium, Global Lipids Genetics Consortium, ReproGen Consortium, MAGIC Investigators
    354. JI Rotter
    355. M Boehnke
    356. S Kathiresan
    357. MI McCarthy
    358. CJ Willer
    359. K Stefansson
    360. IB Borecki
    361. DJ Liu
    362. KE North
    363. NL Heard-Costa
    364. TH Pers
    365. CM Lindgren
    366. C Oxvig
    367. Z Kutalik
    368. F Rivadeneira
    369. RJ Loos
    370. TM Frayling
    371. JN Hirschhorn
    372. P Deloukas
    373. G Lettre
    (2017)
    Nature 542:186–190.
    https://doi.org/10.1038/nature21039
  7. 7
  8. 8
  9. 9
  10. 10
    Defining the role of common variation in the genomic and biological architecture of adult human height
    1. AR Wood
    2. T Esko
    3. J Yang
    4. S Vedantam
    5. TH Pers
    6. S Gustafsson
    7. AY Chu
    8. K Estrada
    9. Jian'an Luan
    10. Z Kutalik
    11. N Amin
    12. ML Buchkovich
    13. DC Croteau-Chonka
    14. FR Day
    15. Y Duan
    16. T Fall
    17. R Fehrmann
    18. T Ferreira
    19. AU Jackson
    20. J Karjalainen
    21. KS Lo
    22. AE Locke
    23. R Mägi
    24. E Mihailov
    25. E Porcu
    26. JC Randall
    27. A Scherag
    28. AAE Vinkhuyzen
    29. H-J Westra
    30. TW Winkler
    31. T Workalemahu
    32. JH Zhao
    33. D Absher
    34. E Albrecht
    35. D Anderson
    36. J Baron
    37. M Beekman
    38. A Demirkan
    39. GB Ehret
    40. B Feenstra
    41. MF Feitosa
    42. K Fischer
    43. RM Fraser
    44. A Goel
    45. J Gong
    46. AE Justice
    47. S Kanoni
    48. ME Kleber
    49. K Kristiansson
    50. U Lim
    51. V Lotay
    52. JC Lui
    53. M Mangino
    54. IM Leach
    55. C Medina-Gomez
    56. MA Nalls
    57. DR Nyholt
    58. CD Palmer
    59. D Pasko
    60. S Pechlivanis
    61. I Prokopenko
    62. JS Ried
    63. S Ripke
    64. D Shungin
    65. A Stancáková
    66. RJ Strawbridge
    67. YJ Sung
    68. T Tanaka
    69. A Teumer
    70. S Trompet
    71. SW van der Laan
    72. J van Setten
    73. JV Van Vliet-Ostaptchouk
    74. Z Wang
    75. L Yengo
    76. W Zhang
    77. U Afzal
    78. J Ärnlöv
    79. GM Arscott
    80. S Bandinelli
    81. A Barrett
    82. C Bellis
    83. AJ Bennett
    84. C Berne
    85. M Blüher
    86. JL Bolton
    87. Y Böttcher
    88. HA Boyd
    89. M Bruinenberg
    90. BM Buckley
    91. S Buyske
    92. IH Caspersen
    93. PS Chines
    94. R Clarke
    95. S Claudi-Boehm
    96. M Cooper
    97. EW Daw
    98. PA De Jong
    99. J Deelen
    100. G Delgado
    101. JC Denny
    102. R Dhonukshe-Rutten
    103. M Dimitriou
    104. ASF Doney
    105. M Dörr
    106. N Eklund
    107. E Eury
    108. L Folkersen
    109. ME Garcia
    110. F Geller
    111. V Giedraitis
    112. AS Go
    113. H Grallert
    114. TB Grammer
    115. J Gräßler
    116. H Grönberg
    117. LCPGM de Groot
    118. CJ Groves
    119. J Haessler
    120. P Hall
    121. T Haller
    122. G Hallmans
    123. A Hannemann
    124. CA Hartman
    125. M Hassinen
    126. C Hayward
    127. NL Heard-Costa
    128. Q Helmer
    129. G Hemani
    130. AK Henders
    131. HL Hillege
    132. MA Hlatky
    133. W Hoffmann
    134. P Hoffmann
    135. O Holmen
    136. JJ Houwing-Duistermaat
    137. T Illig
    138. A Isaacs
    139. AL James
    140. J Jeff
    141. B Johansen
    142. Åsa Johansson
    143. J Jolley
    144. T Juliusdottir
    145. J Junttila
    146. AN Kho
    147. L Kinnunen
    148. N Klopp
    149. T Kocher
    150. W Kratzer
    151. P Lichtner
    152. L Lind
    153. J Lindström
    154. S Lobbens
    155. M Lorentzon
    156. Y Lu
    157. V Lyssenko
    158. PKE Magnusson
    159. A Mahajan
    160. M Maillard
    161. WL McArdle
    162. CA McKenzie
    163. S McLachlan
    164. PJ McLaren
    165. C Menni
    166. S Merger
    167. L Milani
    168. A Moayyeri
    169. KL Monda
    170. MA Morken
    171. G Müller
    172. M Müller-Nurasyid
    173. AW Musk
    174. N Narisu
    175. M Nauck
    176. IM Nolte
    177. MM Nöthen
    178. L Oozageer
    179. S Pilz
    180. NW Rayner
    181. F Renstrom
    182. NR Robertson
    183. LM Rose
    184. R Roussel
    185. S Sanna
    186. H Scharnagl
    187. S Scholtens
    188. FR Schumacher
    189. H Schunkert
    190. RA Scott
    191. J Sehmi
    192. T Seufferlein
    193. J Shi
    194. K Silventoinen
    195. JH Smit
    196. AV Smith
    197. J Smolonska
    198. AV Stanton
    199. K Stirrups
    200. DJ Stott
    201. HM Stringham
    202. J Sundström
    203. MA Swertz
    204. A-C Syvänen
    205. BO Tayo
    206. G Thorleifsson
    207. JP Tyrer
    208. S van Dijk
    209. NM van Schoor
    210. N van der Velde
    211. D van Heemst
    212. FVA van Oort
    213. SH Vermeulen
    214. N Verweij
    215. JM Vonk
    216. LL Waite
    217. M Waldenberger
    218. R Wennauer
    219. LR Wilkens
    220. C Willenborg
    221. T Wilsgaard
    222. MK Wojczynski
    223. A Wong
    224. AF Wright
    225. Q Zhang
    226. D Arveiler
    227. SJL Bakker
    228. J Beilby
    229. RN Bergman
    230. S Bergmann
    231. R Biffar
    232. J Blangero
    233. DI Boomsma
    234. SR Bornstein
    235. P Bovet
    236. P Brambilla
    237. MJ Brown
    238. H Campbell
    239. MJ Caulfield
    240. A Chakravarti
    241. R Collins
    242. FS Collins
    243. DC Crawford
    244. LA Cupples
    245. J Danesh
    246. U de Faire
    247. HM den Ruijter
    248. R Erbel
    249. J Erdmann
    250. JG Eriksson
    251. M Farrall
    252. E Ferrannini
    253. J Ferrières
    254. I Ford
    255. NG Forouhi
    256. T Forrester
    257. RT Gansevoort
    258. PV Gejman
    259. C Gieger
    260. A Golay
    261. O Gottesman
    262. V Gudnason
    263. U Gyllensten
    264. DW Haas
    265. AS Hall
    266. TB Harris
    267. AT Hattersley
    268. AC Heath
    269. C Hengstenberg
    270. AA Hicks
    271. LA Hindorff
    272. AD Hingorani
    273. A Hofman
    274. GK Hovingh
    275. SE Humphries
    276. SC Hunt
    277. E Hypponen
    278. KB Jacobs
    279. M-R Jarvelin
    280. P Jousilahti
    281. AM Jula
    282. J Kaprio
    283. JJP Kastelein
    284. M Kayser
    285. F Kee
    286. SM Keinanen-Kiukaanniemi
    287. LA Kiemeney
    288. JS Kooner
    289. C Kooperberg
    290. S Koskinen
    291. P Kovacs
    292. AT Kraja
    293. M Kumari
    294. J Kuusisto
    295. TA Lakka
    296. C Langenberg
    297. L Le Marchand
    298. T Lehtimäki
    299. S Lupoli
    300. PAF Madden
    301. S Männistö
    302. P Manunta
    303. A Marette
    304. TC Matise
    305. B McKnight
    306. T Meitinger
    307. FL Moll
    308. GW Montgomery
    309. AD Morris
    310. AP Morris
    311. JC Murray
    312. M Nelis
    313. C Ohlsson
    314. AJ Oldehinkel
    315. KK Ong
    316. WH Ouwehand
    317. G Pasterkamp
    318. A Peters
    319. PP Pramstaller
    320. JF Price
    321. L Qi
    322. OT Raitakari
    323. T Rankinen
    324. DC Rao
    325. TK Rice
    326. M Ritchie
    327. I Rudan
    328. V Salomaa
    329. NJ Samani
    330. J Saramies
    331. MA Sarzynski
    332. PEH Schwarz
    333. S Sebert
    334. P Sever
    335. AR Shuldiner
    336. J Sinisalo
    337. V Steinthorsdottir
    338. RP Stolk
    339. J-C Tardif
    340. A Tönjes
    341. A Tremblay
    342. E Tremoli
    343. J Virtamo
    344. M-C Vohl
    345. Electronic Medical Records and Genomics (eMEMERGEGE) Consortium, MIGen Consortium, PAGEGE Consortium, LifeLines Cohort Study
    346. P Amouyel
    347. FW Asselbergs
    348. TL Assimes
    349. M Bochud
    350. BO Boehm
    351. E Boerwinkle
    352. EP Bottinger
    353. C Bouchard
    354. S Cauchi
    355. JC Chambers
    356. SJ Chanock
    357. RS Cooper
    358. PIW de Bakker
    359. G Dedoussis
    360. L Ferrucci
    361. PW Franks
    362. P Froguel
    363. LC Groop
    364. CA Haiman
    365. A Hamsten
    366. MG Hayes
    367. J Hui
    368. DJ Hunter
    369. K Hveem
    370. JW Jukema
    371. RC Kaplan
    372. M Kivimaki
    373. D Kuh
    374. M Laakso
    375. Y Liu
    376. NG Martin
    377. W März
    378. M Melbye
    379. S Moebus
    380. PB Munroe
    381. I Njølstad
    382. BA Oostra
    383. CNA Palmer
    384. NL Pedersen
    385. M Perola
    386. L Pérusse
    387. U Peters
    388. JE Powell
    389. C Power
    390. T Quertermous
    391. R Rauramaa
    392. E Reinmaa
    393. PM Ridker
    394. F Rivadeneira
    395. JI Rotter
    396. TE Saaristo
    397. D Saleheen
    398. D Schlessinger
    399. PE Slagboom
    400. H Snieder
    401. TD Spector
    402. K Strauch
    403. M Stumvoll
    404. J Tuomilehto
    405. M Uusitupa
    406. P van der Harst
    407. H Völzke
    408. M Walker
    409. NJ Wareham
    410. H Watkins
    411. H-E Wichmann
    412. JF Wilson
    413. P Zanen
    414. P Deloukas
    415. IM Heid
    416. CM Lindgren
    417. KL Mohlke
    418. EK Speliotes
    419. U Thorsteinsdottir
    420. I Barroso
    421. CS Fox
    422. KE North
    423. DP Strachan
    424. JS Beckmann
    425. SI Berndt
    426. M Boehnke
    427. IB Borecki
    428. MI McCarthy
    429. A Metspalu
    430. K Stefansson
    431. AG Uitterlinden
    432. CM van Duijn
    433. L Franke
    434. CJ Willer
    435. AL Price
    436. G Lettre
    437. RJF Loos
    438. MN Weedon
    439. E Ingelsson
    440. JR O'Connell
    441. GR Abecasis
    442. DI Chasman
    443. ME Goddard
    444. PM Visscher
    445. JN Hirschhorn
    446. TM Frayling
    (2014)
    Nature Genetics 46:1173–1186.
    https://doi.org/10.1038/ng.3097

Article and author information

Author details

  1. Luisa F Pallares

    Luisa F Pallares is at the Lewis-Sigler Institute for Integrative Genomics and the Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States

    For correspondence
    pallares@princeton.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6547-1901

Publication history

  1. Version of Record published: December 4, 2019 (version 1)

Copyright

© 2019, Pallares

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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Further reading

    1. Genetics and Genomics
    Téo Fournier et al.
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    Genome-wide association studies (GWAS) allow to dissect complex traits and map genetic variants, which often explain relatively little of the heritability. One potential reason is the preponderance of undetected low-frequency variants. To increase their allele frequency and assess their phenotypic impact in a population, we generated a diallel panel of 3025 yeast hybrids, derived from pairwise crosses between natural isolates and examined a large number of traits. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a third is governed by non-additive effects, with complete dominance having a key role. By performing GWAS on the diallel panel, we found that associated variants with low frequency in the initial population are overrepresented and explain a fraction of the phenotypic variance as well as an effect size similar to common variants. Overall, we highlighted the relevance of low-frequency variants on the phenotypic variation.

    1. Developmental Biology
    2. Genetics and Genomics
    Theodora Koromila et al.
    Research Article Updated

    Pioneer factors such as Zelda (Zld) help initiate zygotic transcription in Drosophila early embryos, but whether other factors support this dynamic process is unclear. Odd-paired (Opa), a zinc-finger transcription factor expressed at cellularization, controls the transition of genes from pair-rule to segmental patterns along the anterior-posterior axis. Finding that Opa also regulates expression through enhancer sog_Distal along the dorso-ventral axis, we hypothesized Opa’s role is more general. Chromatin-immunoprecipitation (ChIP-seq) confirmed its in vivo binding to sog_Distal but also identified widespread binding throughout the genome, comparable to Zld. Furthermore, chromatin assays (ATAC-seq) demonstrate that Opa, like Zld, influences chromatin accessibility genome-wide at cellularization, suggesting both are pioneer factors with common as well as distinct targets. Lastly, embryos lacking opa exhibit widespread, late patterning defects spanning both axes. Collectively, these data suggest Opa is a general timing factor and likely late-acting pioneer factor that drives a secondary wave of zygotic gene expression.