1. Genetics and Genomics
Download icon

Genetic Variation: Searching for solutions to the missing heritability problem

  1. Luisa F Pallares  Is a corresponding author
  1. Princeton University, United States
Insight
  • Cited 5
  • Views 2,385
  • Annotations
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. Marouli E
    2. Graff M
    3. Medina-Gomez C
    4. Lo KS
    5. Wood AR
    6. Kjaer TR
    7. Fine RS
    8. Lu Y
    9. Schurmann C
    10. Highland HM
    11. Rüeger S
    12. Thorleifsson G
    13. Justice AE
    14. Lamparter D
    15. Stirrups KE
    16. Turcot V
    17. Young KL
    18. Winkler TW
    19. Esko T
    20. Karaderi T
    21. Locke AE
    22. Masca NG
    23. Ng MC
    24. Mudgal P
    25. Rivas MA
    26. Vedantam S
    27. Mahajan A
    28. Guo X
    29. Abecasis G
    30. Aben KK
    31. Adair LS
    32. Alam DS
    33. Albrecht E
    34. Allin KH
    35. Allison M
    36. Amouyel P
    37. Appel EV
    38. Arveiler D
    39. Asselbergs FW
    40. Auer PL
    41. Balkau B
    42. Banas B
    43. Bang LE
    44. Benn M
    45. Bergmann S
    46. Bielak LF
    47. Blüher M
    48. Boeing H
    49. Boerwinkle E
    50. Böger CA
    51. Bonnycastle LL
    52. Bork-Jensen J
    53. Bots ML
    54. Bottinger EP
    55. Bowden DW
    56. Brandslund I
    57. Breen G
    58. Brilliant MH
    59. Broer L
    60. Burt AA
    61. Butterworth AS
    62. Carey DJ
    63. Caulfield MJ
    64. Chambers JC
    65. Chasman DI
    66. Chen YI
    67. Chowdhury R
    68. Christensen C
    69. Chu AY
    70. Cocca M
    71. Collins FS
    72. Cook JP
    73. Corley J
    74. Galbany JC
    75. Cox AJ
    76. Cuellar-Partida G
    77. Danesh J
    78. Davies G
    79. de Bakker PI
    80. de Borst GJ
    81. de Denus S
    82. de Groot MC
    83. de Mutsert R
    84. Deary IJ
    85. Dedoussis G
    86. Demerath EW
    87. den Hollander AI
    88. Dennis JG
    89. Di Angelantonio E
    90. Drenos F
    91. Du M
    92. Dunning AM
    93. Easton DF
    94. Ebeling T
    95. Edwards TL
    96. Ellinor PT
    97. Elliott P
    98. Evangelou E
    99. Farmaki AE
    100. Faul JD
    101. Feitosa MF
    102. Feng S
    103. Ferrannini E
    104. Ferrario MM
    105. Ferrieres J
    106. Florez JC
    107. Ford I
    108. Fornage M
    109. Franks PW
    110. Frikke-Schmidt R
    111. Galesloot TE
    112. Gan W
    113. Gandin I
    114. Gasparini P
    115. Giedraitis V
    116. Giri A
    117. Girotto G
    118. Gordon SD
    119. Gordon-Larsen P
    120. Gorski M
    121. Grarup N
    122. Grove ML
    123. Gudnason V
    124. Gustafsson S
    125. Hansen T
    126. Harris KM
    127. Harris TB
    128. Hattersley AT
    129. Hayward C
    130. He L
    131. Heid IM
    132. Heikkilä K
    133. Helgeland Ø
    134. Hernesniemi J
    135. Hewitt AW
    136. Hocking LJ
    137. Hollensted M
    138. Holmen OL
    139. Hovingh GK
    140. Howson JM
    141. Hoyng CB
    142. Huang PL
    143. Hveem K
    144. Ikram MA
    145. Ingelsson E
    146. Jackson AU
    147. Jansson JH
    148. Jarvik GP
    149. Jensen GB
    150. Jhun MA
    151. Jia Y
    152. Jiang X
    153. Johansson S
    154. Jørgensen ME
    155. Jørgensen T
    156. Jousilahti P
    157. Jukema JW
    158. Kahali B
    159. Kahn RS
    160. Kähönen M
    161. Kamstrup PR
    162. Kanoni S
    163. Kaprio J
    164. Karaleftheri M
    165. Kardia SL
    166. Karpe F
    167. Kee F
    168. Keeman R
    169. Kiemeney LA
    170. Kitajima H
    171. Kluivers KB
    172. Kocher T
    173. Komulainen P
    174. Kontto J
    175. Kooner JS
    176. Kooperberg C
    177. Kovacs P
    178. Kriebel J
    179. Kuivaniemi H
    180. Küry S
    181. Kuusisto J
    182. La Bianca M
    183. Laakso M
    184. Lakka TA
    185. Lange EM
    186. Lange LA
    187. Langefeld CD
    188. Langenberg C
    189. Larson EB
    190. Lee IT
    191. Lehtimäki T
    192. Lewis CE
    193. Li H
    194. Li J
    195. Li-Gao R
    196. Lin H
    197. Lin LA
    198. Lin X
    199. Lind L
    200. Lindström J
    201. Linneberg A
    202. Liu Y
    203. Liu Y
    204. Lophatananon A
    205. Luan J
    206. Lubitz SA
    207. Lyytikäinen LP
    208. Mackey DA
    209. Madden PA
    210. Manning AK
    211. Männistö S
    212. Marenne G
    213. Marten J
    214. Martin NG
    215. Mazul AL
    216. Meidtner K
    217. Metspalu A
    218. Mitchell P
    219. Mohlke KL
    220. Mook-Kanamori DO
    221. Morgan A
    222. Morris AD
    223. Morris AP
    224. Müller-Nurasyid M
    225. Munroe PB
    226. Nalls MA
    227. Nauck M
    228. Nelson CP
    229. Neville M
    230. Nielsen SF
    231. Nikus K
    232. Njølstad PR
    233. Nordestgaard BG
    234. Ntalla I
    235. O'Connel JR
    236. Oksa H
    237. Loohuis LM
    238. Ophoff RA
    239. Owen KR
    240. Packard CJ
    241. Padmanabhan S
    242. Palmer CN
    243. Pasterkamp G
    244. Patel AP
    245. Pattie A
    246. Pedersen O
    247. Peissig PL
    248. Peloso GM
    249. Pennell CE
    250. Perola M
    251. Perry JA
    252. Perry JR
    253. Person TN
    254. Pirie A
    255. Polasek O
    256. Posthuma D
    257. Raitakari OT
    258. Rasheed A
    259. Rauramaa R
    260. Reilly DF
    261. Reiner AP
    262. Renström F
    263. Ridker PM
    264. Rioux JD
    265. Robertson N
    266. Robino A
    267. Rolandsson O
    268. Rudan I
    269. Ruth KS
    270. Saleheen D
    271. Salomaa V
    272. Samani NJ
    273. Sandow K
    274. Sapkota Y
    275. Sattar N
    276. Schmidt MK
    277. Schreiner PJ
    278. Schulze MB
    279. Scott RA
    280. Segura-Lepe MP
    281. Shah S
    282. Sim X
    283. Sivapalaratnam S
    284. Small KS
    285. Smith AV
    286. Smith JA
    287. Southam L
    288. Spector TD
    289. Speliotes EK
    290. Starr JM
    291. Steinthorsdottir V
    292. Stringham HM
    293. Stumvoll M
    294. Surendran P
    295. 't Hart LM
    296. Tansey KE
    297. Tardif JC
    298. Taylor KD
    299. Teumer A
    300. Thompson DJ
    301. Thorsteinsdottir U
    302. Thuesen BH
    303. Tönjes A
    304. Tromp G
    305. Trompet S
    306. Tsafantakis E
    307. Tuomilehto J
    308. Tybjaerg-Hansen A
    309. Tyrer JP
    310. Uher R
    311. Uitterlinden AG
    312. Ulivi S
    313. van der Laan SW
    314. Van Der Leij AR
    315. van Duijn CM
    316. van Schoor NM
    317. van Setten J
    318. Varbo A
    319. Varga TV
    320. Varma R
    321. Edwards DR
    322. Vermeulen SH
    323. Vestergaard H
    324. Vitart V
    325. Vogt TF
    326. Vozzi D
    327. Walker M
    328. Wang F
    329. Wang CA
    330. Wang S
    331. Wang Y
    332. Wareham NJ
    333. Warren HR
    334. Wessel J
    335. Willems SM
    336. Wilson JG
    337. Witte DR
    338. Woods MO
    339. Wu Y
    340. Yaghootkar H
    341. Yao J
    342. Yao P
    343. Yerges-Armstrong LM
    344. Young R
    345. Zeggini E
    346. Zhan X
    347. Zhang W
    348. Zhao JH
    349. Zhao W
    350. Zhao W
    351. Zheng H
    352. Zhou W
    353. EPIC-InterAct Consortium, CHD Exome+ Consortium, ExomeBP Consortium, T2D-Genes Consortium, GoT2D Genes Consortium, Global Lipids Genetics Consortium, ReproGen Consortium, MAGIC Investigators
    354. Rotter JI
    355. Boehnke M
    356. Kathiresan S
    357. McCarthy MI
    358. Willer CJ
    359. Stefansson K
    360. Borecki IB
    361. Liu DJ
    362. North KE
    363. Heard-Costa NL
    364. Pers TH
    365. Lindgren CM
    366. Oxvig C
    367. Kutalik Z
    368. Rivadeneira F
    369. Loos RJ
    370. Frayling TM
    371. Hirschhorn JN
    372. Deloukas P
    373. Lettre G
    (2017) Rare and low-frequency coding variants alter human adult height
    Nature 542:186–190.
    https://doi.org/10.1038/nature21039
    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 Jian'an
    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 AAE
    29. Westra H-J
    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. Leach IM
    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. Ärnlöv 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 ASF
    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 LCPGM
    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 Åsa
    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 PKE
    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 A-C
    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 FVA
    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 SJL
    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 M-R
    280. Jousilahti P
    281. Jula AM
    282. Kaprio J
    283. Kastelein JJP
    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 PAF
    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 PEH
    333. Sebert S
    334. Sever P
    335. Shuldiner AR
    336. Sinisalo J
    337. Steinthorsdottir V
    338. Stolk RP
    339. Tardif J-C
    340. Tönjes A
    341. Tremblay A
    342. Tremoli E
    343. Virtamo J
    344. Vohl M-C
    345. Electronic Medical Records and Genomics (eMEMERGEGE) Consortium, MIGen Consortium, PAGEGE Consortium, LifeLines Cohort Study
    346. Amouyel P
    347. Asselbergs FW
    348. Assimes TL
    349. Bochud M
    350. Boehm BO
    351. Boerwinkle E
    352. Bottinger EP
    353. Bouchard C
    354. Cauchi S
    355. Chambers JC
    356. Chanock SJ
    357. Cooper RS
    358. de Bakker PIW
    359. Dedoussis G
    360. Ferrucci L
    361. Franks PW
    362. Froguel P
    363. Groop LC
    364. Haiman CA
    365. Hamsten A
    366. Hayes MG
    367. Hui J
    368. Hunter DJ
    369. Hveem K
    370. Jukema JW
    371. Kaplan RC
    372. Kivimaki M
    373. Kuh D
    374. Laakso M
    375. Liu Y
    376. Martin NG
    377. März W
    378. Melbye M
    379. Moebus S
    380. Munroe PB
    381. Njølstad I
    382. Oostra BA
    383. Palmer CNA
    384. Pedersen NL
    385. Perola M
    386. Pérusse L
    387. Peters U
    388. Powell JE
    389. Power C
    390. Quertermous T
    391. Rauramaa R
    392. Reinmaa E
    393. Ridker PM
    394. Rivadeneira F
    395. Rotter JI
    396. Saaristo TE
    397. Saleheen D
    398. Schlessinger D
    399. Slagboom PE
    400. Snieder H
    401. Spector TD
    402. Strauch K
    403. Stumvoll M
    404. Tuomilehto J
    405. Uusitupa M
    406. van der Harst P
    407. Völzke H
    408. Walker M
    409. Wareham NJ
    410. Watkins H
    411. Wichmann H-E
    412. Wilson JF
    413. Zanen P
    414. Deloukas P
    415. Heid IM
    416. Lindgren CM
    417. Mohlke KL
    418. Speliotes EK
    419. Thorsteinsdottir U
    420. Barroso I
    421. Fox CS
    422. North KE
    423. Strachan DP
    424. Beckmann JS
    425. Berndt SI
    426. Boehnke M
    427. Borecki IB
    428. McCarthy MI
    429. Metspalu A
    430. Stefansson K
    431. Uitterlinden AG
    432. van Duijn CM
    433. Franke L
    434. Willer CJ
    435. Price AL
    436. Lettre G
    437. Loos RJF
    438. Weedon MN
    439. Ingelsson E
    440. O'Connell JR
    441. Abecasis GR
    442. Chasman DI
    443. Goddard ME
    444. Visscher PM
    445. Hirschhorn JN
    446. Frayling TM
    (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. 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.

Metrics

  • 2,385
    Page views
  • 172
    Downloads
  • 5
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Genetics and Genomics
    Téo Fournier et al.
    Research Article Updated

    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. Genetics and Genomics
    2. Immunology and Inflammation
    Ilana Fox-Fisher et al.
    Research Article Updated

    Blood cell counts often fail to report on immune processes occurring in remote tissues. Here, we use immune cell type-specific methylation patterns in circulating cell-free DNA (cfDNA) for studying human immune cell dynamics. We characterized cfDNA released from specific immune cell types in healthy individuals (N = 242), cross sectionally and longitudinally. Immune cfDNA levels had no individual steady state as opposed to blood cell counts, suggesting that cfDNA concentration reflects adjustment of cell survival to maintain homeostatic cell numbers. We also observed selective elevation of immune-derived cfDNA upon perturbations of immune homeostasis. Following influenza vaccination (N = 92), B-cell-derived cfDNA levels increased prior to elevated B-cell counts and predicted efficacy of antibody production. Patients with eosinophilic esophagitis (N = 21) and B-cell lymphoma (N = 27) showed selective elevation of eosinophil and B-cell cfDNA, respectively, which were undetectable by cell counts in blood. Immune-derived cfDNA provides a novel biomarker for monitoring immune responses to physiological and pathological processes that are not accessible using conventional methods.