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ADDITIONAL TESTS -- BIOMEDICAL LITERATURE.
Created: 2017-Jul-20 | Victoria Stuart | "mail"..@t.."VictoriasJourney.com"
Last updated: 2017-Dec-31
==============================================================================
NOTES:
1. The following will often look VERY silly, but I am trying to functionally challenge / break my script. ;-)
2. Sentences of length <= 15 may not be split, a consequence of dealing with journal title abbreviations (see comments in script code).
3. I purposely add a " ." at the end of some lines, below, as an updated version of my script deletes unterminated lines (no terminal ".!?"; e.g. the "Created" and "Last updated" lines, above).
==============================================================================
# CHARACTER SET TO TEST MY "SED CHARACTER REPLACEMENT" CODE (REPLACES SOME LIGATURES, VARIOUS QUOTATION MARKS, OTHER ANNOYANCES ...):
ffi | fi | ff | fl | ffl | � | ␮ | ௡ | ␣ | ␤ | ␦ | 5Ј- | -3Ј | þ | ¼ | ϭ | Ɛ | Ͻ | Ͼ | ␥ | ␧ | ␨ | Ϫ | À | OLD: | ‫؍‬ . | ␹ | Ն | Ն | Յ | Ã | Â | ¥ |  | ™ | ® | → | – | Ϯ | ؉ | ϫ | ϳ | ʽ | ʻ | “ | ˮ | ” | ״ | ʺ | ′′ | 〃 | ’ | ʼ | ‘ | ′ | ` | ׳ | ʹ | ꞌ | ˊ | ˋ | ˌ | — | ؊ | ϩ | ϫ .
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1' 2' 3' 4' 5' 6' 7' 8' 9' .
5'-ACGT .
ACGT-3' .
3.1 x 10e-4 .
p.Arg62His .
"one" .
'two' .
"This is sentence number one."
'This is sentence number one.'
The girls' dresses.
The Wilsons' house.
The women's room.
1 < 2 .
2 > 1 .
x <= y .
x < = y .
x >= y .
x > = y .
p < 0.001 .
p > 0.001 .
P <= 0.001 .
P >= 0.001 .
Proc. Natl. Acad. Sci. U.S.A.
Phys. Rev. Lett.
Esther P.Black.
While Duhr and Braun (Proc. Natl. Acad. Sci. U.S.A. 104, 9346 (2007)) observed a quadratic radial dependence Braibanti et al. (Phys. Rev. Lett. 100, 108303 (2008)) found a linear radial dependence of the Soret coefficient.
...
...
A ... .
B ... .
C... .
... .
... b .
... b .
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# Added "Post-processing" sed expressions 2017-Dec-29 to deal with these problematic text (mostly sentence boundaries involving numbers, Greek letters ...
apple.TGFβ .
also cc.Ms. Smith.
Hcc.When .
invasion. km23-1 .
lines.2OH-BNPP1 .
CBX7. β3 .
manner. β3 .
==============================================================================
Some text ...
https://stackoverflow.com/questions/4283344/sed-to-remove-urls-from-a-file
https://www.google.ca/search?dcr=0&ei=QCsyWtbYF43YjwPpzKyQAQ&q=python+remove++citations&oq=python+remove++citations&gs_l=psy-ab.3...1806.1806.0.2004.1.1.0.0.0.0.61.61.1.1.0....0...1c.1.64.psy-ab..0.0.0....0.-cxpNc6youY
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
https://bbengfort.github.io/tutorials/2016/05/19/text-classification-nltk-sckit-learn.html
http://datasynce.org/2017/05/sentiment-analysis-on-python-through-textblob/
https://www.google.ca/?q=halifax&gws_rd=cr&dcr=0&ei=j7UyWuGKM47SjwOq-ojgCw
http://www.google.ca/?q=halifax&gws_rd=cr&dcr=0&ei=j7UyWuGKM47SjwOq-ojgCw
www.google.ca/?q=halifax&gws_rd=cr&dcr=0&ei=j7UyWuGKM47SjwOq-ojgCw
ftp://ftp.ncbi.nlm.nih.gov/
ftp://ftp.ncbi.nlm.nih.gov/1000genomes/ftp/alignment_indices/20100804.alignment.index
Some more text.
https://doi.org/10.1109/5.771073
doi.org/10.1109/5.771073
==============================================================================
Hello? Ms. Wilson is asking for the file! a. b. e. k. l. m. s. t. u. ... as Dr. Stuart et al .
Where, exactly, do you live? Main St. I live on 1234 Main St. That's where you can find me. They found nothing there? Yes, nothing! However, in v.3.4 (see Fig. 2, and fig 3, and Figs. 5-6), it clearly shows, ... on the last page.. Very good, i.e., excellent! E.g., another example. E.g. another one; e.g one more; i.e. that's the last one!
As indicated in v.1, and in v. 2, and in v3.
The items found on p. 2. The items found on p. 1. The items found on pp.2-3 and pp. 6-19. The items found on p. 23. The items found on p. 9. Apples, bananas, carrots!. The items found on p. 2.
The items found on p. i. The items found on pp. iii-ix.
The item, p. 1. Item, 2-3. The items on ppp. [ << sic] 23.
This versus that versus those. This vs. that versus that vs. that Stuart vs Stuart.
# Ignore incorrectly abbreviated "vs" abbreviation.
This versus that. This vs that. Stuart vs Stuart. THE FOLLOWING WILL NOT SPLIT AS IT IS SHORTER THAN 10 CHAR (SEE SCRIPT "span" COMMENTS) >> . The end.
# The "vs" in the following sentences should remain "as is."
This versus that. This vs, that; Stuart vss Stuart. This vs: that.
You put it where??????????!
The end of this sentence is improperly terminated!. Then the next sentence continues here ...
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CAPITALIZATION TESTS.
THIS IS SENTENCE 1. THIS IS SENTENCE TWO. THIS IS SENTENCE 3. THIS is Sentence FOUR. This IS SENTENCE FIVE. This is Sentence siX. This is sentence 7. This sentence ends in 10. 1, begins this Sentence.
==============================================================================
PARENTHESES:
============
() {} [] <> (some) {more } [text ] <here>
This is sentence number one. This is sentence number 2.
This is sentence number one! This is sentence number 2.
This is sentence number one? This is sentence number 2.
This is sentence number one.) (This is sentence number 2.
This is sentence number one.] [This is sentence number 2.
This is sentence number one.} {This is sentence number 2.
This is sentence number one.) This is sentence number 2.
This is sentence number one.] This is sentence number 2.
This is sentence number one.} This is sentence number 2.
This is sentence number one. (This is sentence number 2.
This is sentence number one. {This is sentence number 2.
This is sentence number one. [This is sentence number 2.
This is sentence number one.) [This is sentence number 2.
This is sentence number one.) {This is sentence number 2.
This is sentence number one.] (This is sentence number 2.
This is sentence number one.] {This is sentence number 2.
This is sentence number one.} (This is sentence number 2.
This is sentence number one.} [This is sentence number 2.
This is sentence number one.' 'This is sentence number 2.
This is sentence number one.' "This is sentence number 2.
This is sentence number one." 'This is sentence number 2.
This is sentence number one." "This is sentence number 2.
This is sentence number one.') This is sentence number 2.
This is sentence number one.') (This is sentence number 2.
This is sentence number one.') [This is sentence number 2.
This is sentence number one.') {This is sentence number 2.
This is sentence number one'.) This is sentence number 2.
This is sentence number one'.) (This is sentence number 2.
This is sentence number one'.) [This is sentence number 2.
This is sentence number one'.) {This is sentence number 2.
This is sentence number one.") This is sentence number 2.
This is sentence number one.") (This is sentence number 2.
This is sentence number one.") [This is sentence number 2.
This is sentence number one.") {This is sentence number 2.
This is sentence number one".) This is sentence number 2.
This is sentence number one".) (This is sentence number 2.
This is sentence number one".) [This is sentence number 2.
This is sentence number one".) {This is sentence number 2.
This is sentence number one!' 'This is sentence number 2.
This is sentence number one!' "This is sentence number 2.
This is sentence number one!" 'This is sentence number 2.
This is sentence number one!" "This is sentence number 2.
This is sentence number one'!) This is sentence number 2.
This is sentence number one'!) (This is sentence number 2.
This is sentence number one'!) [This is sentence number 2.
This is sentence number one'!) {This is sentence number 2.
This is sentence number one!") This is sentence number 2.
This is sentence number one!") (This is sentence number 2.
This is sentence number one!") [This is sentence number 2.
This is sentence number one!") {This is sentence number 2.
This is sentence number one"!) This is sentence number 2.
This is sentence number one"!) (This is sentence number 2.
This is sentence number one"!) [This is sentence number 2.
This is sentence number one"!) {This is sentence number 2.
This is sentence number one?' 'This is sentence number 2.
This is sentence number one?' "This is sentence number 2.
This is sentence number one?" 'This is sentence number 2.
This is sentence number one?" "This is sentence number 2.
This is sentence number one'?) This is sentence number 2.
This is sentence number one'?) (This is sentence number 2.
This is sentence number one'?) [This is sentence number 2.
This is sentence number one'?) {This is sentence number 2.
This is sentence number one?") This is sentence number 2.
This is sentence number one?") (This is sentence number 2.
This is sentence number one?") [This is sentence number 2.
This is sentence number one?") {This is sentence number 2.
This is sentence number one"?) This is sentence number 2.
This is sentence number one"?) (This is sentence number 2.
This is sentence number one"?) [This is sentence number 2.
This is sentence number one"?) {This is sentence number 2.
1-year OS was similar between cohorts (58% vs. 56%, respectively) (p = 0.43).
1-year OS was similar between cohorts [58% vs. 56%, respectively] [p = 0.43].
1-year OS was similar between cohorts {58% vs. 56%, respectively} {p = 0.43}.
The objective of this study is to examine the frequency, development. [The objective of this study is to examine the frequency, development.]
The objective of this study is to examine the frequency, development. (The objective of this study is to examine the frequency, development.)
The objective of this study is to examine the frequency, development. {The objective of this study is to examine the frequency, development.}
(The objective of this study is to examine the frequency, development.) (The objective of this study is to examine the frequency, development.)
==============================================================================
QUOTED SENTENCES:
=================
------------------------------------------------------------------------------
DOUBLE-QUOTED:
--------------
Genetic variation of the type described in this paper are "experiments of nature," of a sort, that reveal when a specific gene is altered, disease risk can be affected. "This is direct evidence that if drugs can be designed to target these proteins, we have a chance to alter disease risk in people," said senior author Gerard Schellenberg, PhD, a professor of Pathology and Laboratory Medicine, and director of the Alzheimer Disease Genetics Consortium (ADGC) at the Perelman School of Medicine at the University of Pennsylvania. "It's been known for decades that microglia -- a first-line-of-defense cell we are born with -- surround amyloid plaque deposits associated with Alzheimer's. These multiple gene 'hits' all originating from microglia are the clearest demonstration that these cells are part of Alzheimer's pathology and, more importantly, provide clear protein targets where we can start to intervene with drugs." "The ADGC, supported by the National Institute on Aging (NIA) ..."
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SINGLE-QUOTED:
--------------
Genetic variation of the type described in this paper are "experiments of nature," of a sort, that reveal when a specific gene is altered, disease risk can be affected. 'This is direct evidence that if drugs can be designed to target these proteins, we have a chance to alter disease risk in people,' said senior author Gerard Schellenberg, PhD, a professor of Pathology and Laboratory Medicine, and director of the Alzheimer Disease Genetics Consortium (ADGC) at the Perelman School of Medicine at the University of Pennsylvania. 'It's been known for decades that microglia -- a first-line-of-defense cell we are born with -- surround amyloid plaque deposits associated with Alzheimer's. These multiple gene 'hits' all originating from microglia are the clearest demonstration that these cells are part of Alzheimer's pathology and, more importantly, provide clear protein targets where we can start to intervene with drugs.' 'The ADGC, supported by the National Institute on Aging (NIA) ...'
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MIX -- QUOTES, CONTRACTIONS:
----------------------------
Again. Yet again: "That's a fine example," she said! "Dr. Stuart is correct!" ... One, 'two,", "three," "four', 'five," 'six', 'seven,' 'eight' ... "Another quoted sentence, (Victoria's, again)." Apples apple's apples' apples. Apples, Apple's, Apples'. ... Another ellipsis test! ... {More ellipses tests? ... [Yes, indeed, always more tests!!] ... This one >> ... should not split (i.e., no 'bananas' today -- Ha!! ;-)
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i.e. AND e.g. TESTS:
--------------------
# MANY OF THESE ARE NOT "REAL" SENTENCES, THEREFORE THEY WILL NOT BE SPLIT. MANY ARE JUST TOO SHORT. I JUST WANT TO SEE WHAT HAPPENS.
Hello everyone, e.g. world. Hello everyone, e.g., world. Hello everyone, e.g. World. Hello everyone, e.g., World.
j
Hello everyone, e.g. world. Hello everyone, e.g., world. Hello everyone, e.g. World. Hello everyone, e.g., World.
E.g., give her an apple. E.g. give her an apple. E.g., give her an apple. E.g. give her an apple. E.g., give her another Apple. E.g. give her another Apple. E.g., give her another Apple. E.g. give her another Apple.
Hello everyone, i.e. world. Hello everyone, i.e., world. Hello everyone, i.e. World. Hello everyone, i.e., World.
Hello everyone, i.e. world. Hello everyone, i.e., world. Hello everyone, i.e. World. Hello everyone, i.e., World.
I.e., give her an apple. I.e. give her an apple. I.e., give her an apple. I.e. give her an apple. I.e., give her an Apple. I.e. give her an Apple. I.e., give her an Apple. I.e. give her an Apple.
Very good, i.e., excellent! E.g., yet another example. E.g. another one; e.g one more; i.e. that's the last one!
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COMPARISON, CARBON COPY ABBREVIATIONS:
--------------------------------------
This one cf this one. This one, c.f. this one. This one, c.f. This one. This one, c.f., this one.
Cc. this one, cc. this one, c.c. this one, cc this one, and CC this one.
------------------------------------------------------------------------------
# ITEMIZED LISTS:
# ---------------
1. Studying an isolated population ...
2. Analyzing missense variants with SKAT;
3. Examining only regions known to be associated
4. Final sentence.
a. Studying an isolated population ...
b. Analyzing missense variants with SKAT;
c. Examining only regions known to be associated
d. Final sentence.
i. Studying an isolated population ...
ii. Analyzing missense variants with SKAT;
iii. Examining only regions known to be associated
iv. Final sentence.
==============================================================================
MISC
====
The objective of this study is to examine the frequency, development, concomitants, and risk factors of falls in a population-based incident Parkinson's disease (PD) cohort. One hundred eighty-one drug-naïve patients with incident PD and 173 normal controls recruited from the Norwegian ParkWest study were prospectively monitored over 7 years. Information on falls was obtained biannually from patients, and at baseline and after 1, 3, 5, and 7 years of follow-up in control subjects. Generalized estimating equation models for correlated data were applied to investigate concomitant features of falls and risk factors for incident falls during 7 years of follow-up in PD. Overall, 64.1% of patients reported falling during the study period. The 7-year cumulative incidence of falls in non-falling patients at baseline (n = 153) was 57.5%, with a relative risk to controls of at least 3.1 (95% confidence interval 1.5-6.3; p < 0.002). Significant concomitants of falls in patients during the study period were higher age, Unified PD Rating Scale motor score, postural instability and gait difficulties (PIGD) phenotype, dementia, and follow-up time. Higher age at baseline, PIGD phenotype at 1-year visit, and follow-up time were independent risk factors for incident falls during follow-up. Nearly two-thirds of patients in the general PD population experience falls within 7 years of diagnosis, representing a more than threefold increased risk compared to age- and gender-matched controls. Patients with higher age at baseline and early PIGD have the greatest risk of falling and may, therefore, be the prime target of specialized assessment and treatment interventions.
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CITATIONS-AUTHOR NAME ABBREVIATIONS
==============================================================================
BRCA1. Genes.
Some additional text. Robert O. Watson. More more added text.
Some additional text. Roel P.J. Bell. More more added text.
Some additional text. Roel P. J. Bell. More more added text.
Some additional text. Adam A.C.E. Franklin. More more added text.
Some additional text. Tim D.D. Somerville. More more added text.
Some additional text. William F.J. McLeod. More more added text.
Some additional text. A. Smith, B. Smith, A.B. Smith, ABCD Smith. More more added text.
Some additional text. A. Smith, A.B. Smith, A. B. Smith, A.B.C. Smith, A. B. C. Smith, A.B.C.D. Smith, A. B. C. D. Smith, ABCD Smith, ABCD.Smith, ABCD. Smith, A. BCD.Smith. More more added text.
Some additional text. Chen A.-B. Jiang. More more added text.
Some additional text. Chen A-B. Jiang. More more added text.
==============================================================================
CITATIONS-JOURNAL TITLE ABBREVIATIONS
==============================================================================
We study the thermodiffusion behavior of spherical polystyrene beads with a diameter of 25 nm by infrared thermal diffusion Forced Rayleigh Scattering (IR-TDFRS). Similar beads were used to investigate the radial dependence of the Soret coefficient by different authors. While Duhr and Braun (Proc. Natl. Acad. Sci. U.S.A. 104, 9346 (2007)) observed a quadratic radial dependence Braibanti et al. (Phys. Rev. Lett. 100, 108303 (2008)) found a linear radial dependence of the Soret coefficient. We demonstrated that special care needs to be taken to obtain reliable thermophoretic data, because the measurements are very sensitive to surface properties. The colloidal particles were characterized by transmission electron microscopy and dynamic light scattering (DLS) experiments were performed. We carried out systematic thermophoretic measurements as a function of temperature, buffer and surfactant concentration. The temperature dependence was analyzed using an empirical formula. To describe the Debye length dependence we used a theoretical model by Dhont. The resulting surface charge density is in agreement with previous literature results. Finally, we analyze the dependence of the Soret coefficient on the concentration of the anionic surfactant sodium dodecyl sulfate (SDS), applying an empirical thermodynamic approach accounting for chemical contributions.
To describe the Debye length dependence we used. A theoretical model. By Dhont. The resulting surface charge density is in agreement with previous literature results.
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Am. J. Cardiol. Virol. 61: 1-72. Some random trailing text.
J. Am. Soc. Bact. Virol. 61: 1-72. Some random trailing text.
Some random preceding text. J. Am. Soc. Bact. 61: 1-72. Some random trailing text.
Some random preceding text. Proc. Natl. Acad. Sci. U.S.A. 88: 7958-7962. Some random trailing text.'a'
Some random preceding text. Proc. Natl. Acad. Sci. U.S.A.88: 7958-7962. Some random trailing text.
Some random preceding text. Proc. Natl. Acad. Sci. U.S.A.Some extraneous text! 88: 7958-7962. Some random trailing text.
Some random preceding text. Proc. Natl. Acad. Sci. U.S.A.
Some random preceding text. Adv. Anat. Embryol. Cell Biol. 61: 1-72. Some random trailing text.
Some random preceding text. Am. J. Hum. Genet. 47: 202-217. Some random trailing text.
Some random preceding text. Am. J. Hum. Genet. 47: 202-217. Some random trailing text.
Some random preceding text. Am. J. Hum. Genet. 47: 202-217. Some random trailing text.
Some random preceding text. Am. J. Hum. Genet. 47: 202-217. Some random trailing text.
Some random preceding text. Am.J.Hum.Genet. 47: 202-217. Some random trailing text.
Some random preceding text. Annu. Rev. Genet. 30: 441-464. Some random trailing text.
Some random preceding text. Biochim. Biophys. Acta 740: 212-219. Some random trailing text.
Some random preceding text. Brain Res. Mol. Brain Res. 53: 317-320. Some random trailing text.
Some random preceding text. Cancer Res. 51: 3075-3079. Some random trailing text.
Some random preceding text. Cell Tissue Kinet. 11: 241-249. Some random trailing text.
Some random preceding text. Crit. Rev. Toxicol. 24: 255-280. Some random trailing text.
Some random preceding text. Curr. Concepts Nutr. 1: 49-97. Some random trailing text.
Some random preceding text. Drug Metab. Rev. 30: 327-338. Some random trailing text.
Some random preceding text. Environ. Health Perspect. 106 (Suppl. 1): 307-312. Some random trailing text.
Some random preceding text. Environ. Mol. Mutagen. Some random trailing text.
Some random preceding text. Exp. Gerontol. 30: 475-484. Some random trailing text.
Some random preceding text. Fundam. Appl. Toxicol. 32: 159-167. Some random trailing text.
Some random preceding text. Hum. Genet. 83: 181-188. Some random trailing text.
Some random preceding text. Indian J. Biochem. Biophys. 29: 49-53. Some random trailing text.
Some random preceding text. Int. J. Biochem. 12: 523-528. Some random trailing text.
Some random preceding text. Int. Rev. Cytol. Some random trailing text.
Some random preceding text. Int. Rev. Cytol. 25: 201-277. Some random trailing text.
Some random preceding text. J. Biol. Chem. 262: 9948-9951. Some random trailing text.
Some random preceding text. J. Molec. Gastroenterol. Some random trailing text.
Some random preceding text. J. Neurosci. Some random trailing text.
Some random preceding text. J. Theor. Biol. 89: 557-571. Some random trailing text.
Some random preceding text. Mech. Ageing Dev. 41: 199-210. Some random trailing text.
Some random preceding text. Mol. Biol. 28: 11-31. Some random trailing text.
Some random preceding text. Mol. Cell. Biol. 12: 767-772. Some random trailing text.
Some random preceding text. Mol. Phylogenet. Evol. 5: 182-187. Some random trailing text.
Some random preceding text. Mutat. Res. 352: 73-78. Some random trailing text.
Some random preceding text. Nucleic Acids Res. Some random trailing text.
Some random preceding text. Nucleic Acids Res. 25: 3007-3013. Some random trailing text.
Some random preceding text. Proc. Natl. Acad. Sci. U.S.A. 88: 7958-7962. Some random trailing text.
Some random preceding text. Proc. Natl. Acad. Sci. USA. 91: 6564-6568. Some random trailing text.
Some random preceding text. Somat. Cell Mol. Genet. 19: 543-555. Some random trailing text.
Some random preceding text. Toxicol. Lett. 82-83: 131-134. Some random trailing text.
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Full references, with Authors, Titles ...
Stuart, G. R., & Glickman, B. W. (2000). Through a glass, darkly: reflections of mutation from lacI transgenic mice. Genetics, 155(3), 1359-1367. Cited by 32
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==============================================================================
[CITATIONS] SAMPLED REFERENCES
==============================================================================
Yun J., Huan Yang, Michael A Lizzio, Chunlai Wu, Zu-Hang Sheng & Ming Guo (2014). MUL1 acts in parallel to the PINK1/parkin pathway in regulating mitofusin and compensates for loss of PINK1/parkin.
Diedrich M., Grit Nebrich, Andrea Koppelstaetter, Jie Shen, Claus Zabel, Joachim Klose & Lei Mao (2011). Brain region specific mitophagy capacity could contribute to selective neuronal vulnerability in Parkinson's disease.
Reference: Gian-Luca McLelland, Vincent Soubannier, Carol X Chen, Heidi M McBride, Edward A Fon. Parkin and PINK1 function in a vesicular trafficking pathway regulating mitochondrial quality control.
Reference: Nicholas T. Hertz, Amandine Berthet, Martin L. Sos, Kurt S. Thorn, Al L. Burlingame, Ken Nakamura, Kevan M. Shokat. A Neo-Substrate that Amplifies Catalytic Activity of Parkinson's-Disease-Related Kinase PINK1.
Reference: Shusaku Uchida, Kumiko Hara, Ayumi Kobayashi, Koji Otsuki, Hirotaka Yamagata, Teruyuki Hobara, Takayoshi Suzuki, Naoki Miyata, Yoshifumi Watanabe. Epigenetic Status of Gdnf in the Ventral Striatum Determines Susceptibility and Adaptation to Daily Stressful Events.
Reference: Paolo S. Manzanillo, Janelle S. Ayres, Robert O. Watson, Angela C. Collins, Gianne Souza, Chris S. Rae, David S. Schneider, Ken Nakamura, Michael U. Shiloh, Jeffery S. Cox. The ubiquitin ligase parkin mediates resistance to intracellular pathogens.
Journal Reference: X. Edward Zhou, Yuanzheng He, Parker W. de Waal, Xiang Gao, Yanyong Kang, Ned Van Eps, Yanting Yin, Kuntal Pal, Devrishi Goswami, Thomas A. White, Anton Barty, Naomi R. Latorraca, Henry N. Chapman, Wayne L. Hubbell, Ron O. Dror, Raymond C. Stevens, Vadim Cherezov, Vsevolod V. Gurevich, Patrick R. Griffin, Oliver P. Ernst, Karsten Melcher, H. Eric Xu. Identification of Phosphorylation Codes for Arrestin Recruitment by G Protein-Coupled Receptors. Cell, 2017; 170 (3): 457 DOI: 10.1016/j.cell.2017.07.002
Reference: Gauri Dixit, Joshua B. Kelley, John R. Houser, Timothy C. Elston, Henrik G. Dohlman. Cellular Noise Suppression by the Regulator of G Protein Signaling Sst2. Molecular Cell, 2014; 55 (1): 85 DOI: 10.1016/j.molcel.2014.05.019
Reference: Arun K. Shukla, Gerwin H. Westfield, Kunhong Xiao, Rosana I. Reis, Li-Yin Huang, Prachi Tripathi-Shukla, Jiang Qian, Sheng Li, Adi Blanc, Austin N. Oleskie, Anne M. Dosey, Min Su, Cui-Rong Liang, Ling-Ling Gu, Jin-Ming Shan, Xin Chen, Rachel Hanna, Minjung Choi, Xiao Jie Yao, Bjoern U. Klink, Alem W. Kahsai, Sachdev S. Sidhu, Shohei Koide, Pawel A. Penczek, Anthony A. Kossiakoff, Virgil L. Woods Jr, Brian K. Kobilka, Georgios Skiniotis, Robert J. Lefkowitz. Visualization of arrestin recruitment by a G-protein-coupled receptor. Nature, 2014; DOI: 10.1038/nature13430
Reference: Makoto R. Hara et al Nature 103: 68 (1888).
Reference:
Aaron F. McDaid, Peter K. Joshi, Eleonora Porcu, Andrea Komljenovic, Hao Li, Vincenzo Sorrentino, Maria Litovchenko, Roel P. J. Bevers, Sina Rüeger, Alexandre Reymond, Murielle Bochud, Bart Deplancke, Robert W. Williams, Marc Robinson-Rechavi, Fred Paccaud, Valentin Rousson, Johan Auwerx, James F. Wilson, Zoltán Kutalik. Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Nature Communications, 2017; 8: 15842 DOI: 10.1038/NCOMMS15842
More information: "Aurora-B kinase pathway controls the lateral to end-on conversion of kinetochore-microtubule attachments in human cells", by Roshan L. Shrestha, Duccio Conti, Naoka Tamura, Dominique Braun, Revathy A. Ramalingam, Konstanty Cieslinski, Jonas Ries & Viji M. Draviam. Nature Communications, DOI: 10.1038/10.1038/s41467-017-00209-z
Reference: Yashi Mi, Guoyuan Qi, Rong Fan, Qinglian Qiao, Yali Sun, Yuqi Gao, Xuebo Liu. EGCG ameliorates high-fat- and high-fructose-induced cognitive defects by regulating the IRS/AKT and ERK/CREB/BDNF. The FASEB Journal, 2017; fj.201700400RR DOI: 10.1096/fj.201700400RR
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Further reading
Lupien M, Eeckhoute J, Meyer CA, Wang Q, Zhang Y, Li W, Carroll JS, Liu XS, Brown M (March 2008). "FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription". Cell 132 (6): 958-70. doi:10.1016/j.cell.2008.01.018. PMC 2323438. PMID 18358809.
Lupien M, Eeckhoute J, Meyer CA, Wang Q, Zhang Y, Li W, Carroll JS, Liu XS, Brown M [March 2008]. "FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription". Cell 132 (6): 958-70. doi:10.1016/j.cell.2008.01.018. PMC 2323438. PMID 18358809.
Lupien M, Brown M (June 2009). "Cistromics of hormone-dependent cancer". Endocr. Relat. Cancer 16 (2): 381-9. doi:10.1677/ERC-09-0038. PMID 19369485.
Reference: Richard Cowper-Sal·lari, Xiaoyang Zhang, Jason B Wright, Swneke D Bailey, Michael D Cole, Jerome Eeckhoute, Jason H Moore, Mathieu Lupien. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nature Genetics, 2012; 44 (11): 1191 DOI: 10.1038/ng.2416
Reference: Kayleigh R. McGovern-Gooch, Nivedita S. Mahajani, Ariana Garagozzo, Anthony J. Schramm, Lauren G. Hannah, Michelle A. Sieburg, John D. Chisholm, James L. Hougland. Synthetic Triterpenoid Inhibition of Human Ghrelin O-Acyltransferase: The Involvement of a Functionally Required Cysteine Provides Mechanistic Insight into Ghrelin Acylation. Biochemistry, 2017; 56 (7): 919 DOI: 10.1021/acs.biochem.6b01008
Reference: Aviad Tsherniak, Francisca Vazquez, Phil G. Montgomery, Barbara A. Weir, Gregory Kryukov, Glenn S. Cowley, Stanley Gill, William F. Harrington, Sasha Pantel, John M. Krill-Burger, Robin M. Meyers, Levi Ali, Amy Goodale, Yenarae Lee, Guozhi Jiang, Jessica Hsiao, William F.J. Gerath, Sara Howell, Erin Merkel, Mahmoud Ghandi, Levi A. Garraway, David E. Root, Todd R. Golub, Jesse S. Boehm, William C. Hahn. Defining a Cancer Dependency Map. Cell, 2017; 170 (3): 564 DOI: 10.1016/j.cell.2017.06.010
Journal Reference: X. Edward Zhou, Yuanzheng He, Parker W. de Waal, Xiang Gao, Yanyong Kang, Ned Van Eps, Yanting Yin, Kuntal Pal, Devrishi Goswami, Thomas A. White, Anton Barty, Naomi R. Latorraca, Henry N. Chapman, Wayne L. Hubbell, Ron O. Dror, Raymond C. Stevens, Vadim Cherezov, Vsevolod V. Gurevich, Patrick R. Griffin, Oliver P. Ernst, Karsten Melcher, H. Eric Xu. Identification of Phosphorylation Codes for Arrestin Recruitment by G Protein-Coupled Receptors. Cell, 2017; 170 (3): 457 DOI: 10.1016/j.cell.2017.07.002
Reference: Gauri Dixit, Joshua B. Kelley, John R. Houser, Timothy C. Elston, Henrik G. Dohlman. Cellular Noise Suppression by the Regulator of G Protein Signaling Sst2. Molecular Cell, 2014; 55 (1): 85 DOI: 10.1016/j.molcel.2014.05.019
Reference: Arun K. Shukla, Gerwin H. Westfield, Kunhong Xiao, Rosana I. Reis, Li-Yin Huang, Prachi Tripathi-Shukla, Jiang Qian, Sheng Li, Adi Blanc, Austin N. Oleskie, Anne M. Dosey, Min Su, Cui-Rong Liang, Ling-Ling Gu, Jin-Ming Shan, Xin Chen, Rachel Hanna, Minjung Choi, Xiao Jie Yao, Bjoern U. Klink, Alem W. Kahsai, Sachdev S. Sidhu, Shohei Koide, Pawel A. Penczek, Anthony A. Kossiakoff, Virgil L. Woods Jr, Brian K. Kobilka, Georgios Skiniotis, Robert J. Lefkowitz. Visualization of arrestin recruitment by a G-protein-coupled receptor. Nature, 2014; DOI: 10.1038/nature13430
Reference: Shai Bel, Mihir Pendse, Yuhao Wang, Yun Li, Kelly A. Ruhn, Brian Hassell, Tess Leal, Sebastian E. Winter, Ramnik J. Xavier, Lora V. Hooper. Paneth cells secrete lysozyme via secretory autophagy during bacterial infection of the intestine. Science, July 2017 DOI: 10.1126/science.aal4677
[1] Cadwell K, Patel KK, Maloney NS, Liu TC, Ng AC, Storer CE, Head RD, Xavier R, Stappenbeck TS, & Virgin HW (2010). Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine. Cell, 141 (7), 1135-45 PMID: 20602997 [http://www.ncbi.nlm.nih.gov/pubmed/20602997]
[2] Edwards, M., Symbor-Nagrabska, A., Dollard, L., Gifford, D., & Fink, G. (2014). Interactions between chromosomal and nonchromosomal elements reveal missing heritability. Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.1407126111 [http://dx.doi.org/10.1073/pnas.1407126111]
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Reference-Scientific Reports: http://nature.com/articles/doi:10.1038/s41598-017-05649-7
Reference: Fran Supek, Ben Lehner. Clustered Mutation Signatures Reveal that Error-Prone DNA Repair Targets Mutations to Active Genes. Cell, 2017; 170 (3): 534 DOI: 10.1016/j.cell.2017.07.003
More information: H.D. Ou el al., "ChromEMT: Visualizing 3D chromatin structure and compaction of the human genome in interphase and mitotic cells," Science (2017). science.sciencemag.org/cgi/doi ... 1126/science.aag0025
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More information: Moussy A, Cosette J, Parmentier R, da Silva C, Corre G, Richard A, et al. Hematopoietic cell fate commitment. PLoS Biol 15(7): e2001867. doi.org/10.1371/journal.pbio.2001867
Reference: Dallas R. Donohoe, Nikhil Garge, Xinxin Zhang, Wei Sun, Thomas M. O'Connell, Maureen K. Bunger, Scott J. Bultman. The Microbiome and Butyrate Regulate Energy Metabolism and Autophagy in the Mammalian Colon. Cell Metabolism, 2011; 13 (5): 517-526 DOI: 10.1016/j.cmet.2011.02.018
Reference: Isabel Gonçalves Silva, Inna M. Yasinska, Svetlana S. Sakhnevych, Walter Fiedler, Jasmin Wellbrock, Marco Bardelli, Luca Varani, Rohanah Hussain, Giuliano Siligardi, Giacomo Ceccone, Steffen M. Berger, Yuri A. Ushkaryov, Bernhard F. Gibbs, Elizaveta Fasler-Kan, Vadim V. Sumbayev. The Tim-3-galectin-9 Secretory Pathway is Involved in the Immune Escape of Human Acute Myeloid Leukemia Cells. EBioMedicine, 2017; DOI: 10.1016/j.ebiom.2017.07.018
Reference: Huijie Feng, Benita Sjögren, Behirda Karaj, Vincent Shaw, Aysegul Gezer, Richard R. Neubig. Movement disorder in GNAO1 encephalopathy associated with gain-of-function mutations. Neurology, 2017; 10.1212/WNL.0000000000004262 DOI: 10.1212/WNL.0000000000004262
Reference: Ville Kytola, Umit Topaloglu, Lance D. Miller, Rhonda L. Bitting, Michael M. Goodman, Ralph B. D'Agostino, Rodwige J. Desnoyers, Carol Albright, George Yacoub, Shadi A. Qasem, Barry DeYoung, Vesteinn Thorsson, Ilya Shmulevich, Meng Yang, Anastasia Shcherban, Matthew Pagni, Liang Liu, Matti Nykter, Kexin Chen, Gregory A. Hawkins, Stefan C. Grant, W. Jeffrey Petty, Angela Tatiana Alistar, Edward A. Levine, Edgar D. Staren, Carl D. Langefeld, Vincent Miller, Gaurav Singal, Robin M. Petro, Mac Robinson, William Blackstock, Bayard L. Powell, Lynne I. Wagner, Kristie L. Foley, Edward Abraham, Boris Pasche, Wei Zhang. Mutational Landscapes of Smoking-Related Cancers in Caucasians and African Americans: Precision Oncology Perspectives at Wake Forest Baptist Comprehensive Cancer Center. Theranostics, July 2017 DOI: 10.7150/thno.20355
Reference: Yalin Zhang, Min Soo Kim, Baosen Jia, Jingqi Yan, Juan Pablo Zuniga-Hertz, Cheng Han, Dongsheng Cai. Hypothalamic stem cells control ageing speed partly through exosomal miRNAs. Nature, 2017; DOI: 10.1038/nature23282
Explore further: Synthesizing the human genome from scratch: https://phys.org/news/2017-07-human-genome.html
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More information: Arnaud R. Krebs et al). DOI: 10.1016/j.molcel.2017.06.027. http://linkinghub.elsevier.com/retrieve/pii/S1097276517304616
Reference: Michael D. Gregory, J. Shane Kippenhan, Daniel P. Eisenberg, Philip D. Kohn, Dwight Dickinson, Venkata S. Mattay, Qiang Chen, Daniel R. Weinberger, Ziad S. Saad, Karen F. Berman. Neanderthal-Derived Genetic Variation Shapes Modern Human Cranium and Brain. Scientific Reports, 2017; 7 (1) DOI: 10.1038/s41598-017-06587-0
Reference: Manav Korpal, Xiaoling Puyang, Zhenhua Jeremy Wu, Roland Seiler, Craig Furman, Htoo Z. Oo, Michael Seiler, Sean Irwin, Vanitha Subramanian, Jaya Julie Joshi, Chris K. Wang, Victoria Rimkunas, Davide Tortora, Hua Yang, Namita Kumar, Galina Kuznetsov, Mark Matijevic, Jesse Chow, Pavan Kumar, Jian Zou, Jacob Feala, Laura Corson, Ryan Henry, Anand Selvaraj, Allison Davis, Kristjan Bloudoff, James Douglas, Bernhard Kiss, Morgan Roberts, Ladan Fazli, Peter C. Black, Peter Fekkes, Peter G. Smith, Markus Warmuth, Lihua Yu, Ming-Hong Hao, Nicholas Larsen, Mads Daugaard, Ping Zhu. Evasion of immunosurveillance by genomic alterations of PPARγ/RXRα in bladder cancer. Nature Communications, 2017; 8 (1) DOI: 10.1038/s41467-017-00147-w
Explore further: New gene-editing technique could drive out mosquito-borne disease: https://phys.org/news/2017-06-gene-editing-technique-mosquito-borne-disease.html
More information: Champer J, Reeves R, Oh SY, Liu C, Liu J, Clark AG, et al. Efficiency in genetically diverse populations. PLoS Genet 13(7): e1006796. doi.org/10.1371/journal.pgen.1006796
Bull JJ, Malik HS (2017) The gene drive bubble: New realities. PLoS Genet 13(7): e1006850. doi.org/10.1371/journal.pgen.1006850
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BIOMEDICAL TEXT
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From: "Victoria Stuart (VictoriasJourney.com)"
To: "Victoria Stuart"
Subject: [PLCG2; ABI3; TREM2] Newly discovered gene variants link innate immunity and Alzheimer's disease. Findings give neurologists fresh ideas for enlisting immune system to fight Alzheimer's [Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease] [Re: Two new genes linked to Alzheimer's risk [Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease]]
Date: Tue, 18 Jul 2017 11:36:11 -0700
https://www.sciencedaily.com/releases/2017/07/170717110511.htm
[PLCG2; ABI3; TREM2] Newly discovered gene variants link innate immunity and Alzheimer's disease. Findings give neurologists fresh ideas for enlisting immune system to fight Alzheimer's [Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease]
Date: July 17, 2017
Source: University of Pennsylvania School of Medicine
Three new gene variants, found in a genome wide association study of Alzheimer's disease (AD), point to the brain's immune cells in the onset of the disorder. These genes encode three proteins that are found in microglia, cells that are part of the brain's injury response system. The study is an international collaboration of four AD research consortia that analyzed DNA from 85,000 subjects. The results are reported online this week in Nature Genetics. Studies of this type focus on identifying new therapeutic targets for treatment or prevention of AD, a goal of researchers world-wide. Genetic variation of the type described in this paper are "experiments of nature," of a sort, that reveal when a specific gene is altered, disease risk can be affected.
"This is direct evidence that if drugs can be designed to target these proteins, we have a chance to alter disease risk in people," said senior author Gerard Schellenberg, PhD, a professor of Pathology and Laboratory Medicine, and director of the Alzheimer Disease Genetics Consortium (ADGC) at the Perelman School of Medicine at the University of Pennsylvania. "It's been known for decades that microglia -- a first-line-of-defense cell we are born with -- surround amyloid plaque deposits associated with Alzheimer's. These multiple gene 'hits' all originating from microglia are the clearest demonstration that these cells are part of Alzheimer's pathology and, more importantly, provide clear protein targets where we can start to intervene with drugs." The ADGC, supported by the National Institute on Aging (NIA) at the National Institutes of Health, is one of the four consortia of the International Genomics of Alzheimer's Project on this study. The others are Cohorts for Heart and Aging in Genomic Epidemiology (CHARGE), European Alzheimer's Disease Initiative (EADI), and Genetic and Environmental Risk in Alzheimer's Disease (GERAD).
The variants the team found -- PLCG2, ABI3, and TREM2 -- are all protein-coding mutations in genes that are highly expressed in microglia and are part of an immune cell protein network where multiple components contribute to AD risk. One of the genes, PLCG2, is an enzyme that is a potential drug target. Key questions remain in how microglia should be targeted and whether the injury response should be inhibited or activated and at what stage of disease. "Since prevention is a key goal of therapy, influencing microglial cells before onset of cognitive changes needs to be explored," Schellenberg said.
The three variants they identified are fairly rare and he accounts for their success in finding them to their three-stage study. In the first stage, the entire protein coding regions of 34,290 samples were sequenced. In the second and third stages, the team further refined the sequences of variants and verified the significant hits against untested samples from AD patients. "Our findings show that microglia and the innate immune system -- via microglia -- directly contribute to susceptibility of late-onset Alzheimer's disease, and are not just a down-stream 'after-the-fact' consequence of damage to the brain," Schellenberg said.
Reference: Gerard D Schellenberg et al. Genetics, 2017; DOI: 10.1038/ng.3916
Image: Beta-amyloid plaques from an AD mouse model. Pink represents beta-amyloid deposits (plaques), brown represents microglia cells and blue/purple is nuclei of neurons and glial cells.
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https://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3916.html
Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease
Rebecca Sims, Sven J van der Lee, Adam C Naj, Céline Bellenguez, Nandini Badarinarayan, Johanna Jakobsdottir, Brian W Kunkle, Anne Boland, Rachel Raybould, Joshua C Bis, Eden R Martin, Benjamin Grenier-Boley, Stefanie Heilmann-Heimbach, Vincent Chouraki, Amanda B Kuzma, Kristel Sleegers, Maria Vronskaya, Agustin Ruiz, Robert R Graham, Robert Olaso, Per Hoffmann, Megan L Grove, Badri N Vardarajan, Mikko Hiltunen, Markus M Nöthen et al
Nature Genetics (2017)
doi:10.1038/ng.3916
We identified rare coding variants associated with Alzheimer's disease in a three-stage case-control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 x 10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 x 10-8) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 x 10-10, odds ratio (OR) = 0.68, minor allele frequency (MAF)cases = 0.0059, MAFcontrols = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 x 10-10, OR = 1.43, MAFcases = 0.011, MAFcontrols = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 x 10-14, OR = 1.67, MAFcases = 0.0143, MAFcontrols = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.
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From: "Victoria Stuart (VictoriasJourney.com)"
To: "Victoria Stuart"
Subject: Rare Variants in Complex Disease: ABCA7 and Alzheimer's [Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease] [Re: [deCODE] Warn people of genetic health risks, says deCODE boss [re: One thousand genes you could live without]]
Date: Mon, 6 Apr 2015 09:50:42 -0700
http://massgenomics.org/2015/04/rare-variants-complex-disease-abca7-alzheimers.html
RARE VARIANTS IN COMPLEX DISEASE: ABCA7 AND ALZHEIMER'S [LOSS-OF-FUNCTION VARIANTS IN ABCA7 CONFER RISK OF ALZHEIMER'S DISEASE]
[April 6, 2015] Although the cost of sequencing continues to fall precipitously (cue the NIH sequencing-versus-Moore's-Law figure), it's still expensive relative to high-throughput genotyping. Whole-genome sequencing on the X Ten costs around $2500 per sample by the time you account for basic analysis and data storage. This means that a well-powered genetic association study for complex disease (10,000 samples) would cost over $20 million just for data generation. The same cohort genotyped on a high-density SNP array might only cost about $1 million. Undoubtedly, that's why most large scale genome-wide association studies to date (>50,000 samples) have relied primarily on SNP array data.
There is a growing body of evidence, however, that rare variants (especially ones not present on SNP arrays) might confer a significant proportion of the genetic risk for complex disease. In age-related macular degeneration (AMD), for example, sequencing studies of moderate size (~5,000 samples) were able to identify rare coding variants in C3 and CFH associated with risk of disease. An important advantage of a sequencing approach is the ability to perform aggregation tests of private and rare coding variants (e.g. with the sequence kernel association test, SKAT) to boost the power to detect association.
A recent paper in Nature Genetics illustrates the feasibility of this approach for sequencing studies of complex disease. Stacy Steinberg and colleagues from deCODE Genetics conducted a search for rare functional variants in the known risk loci for Alzheimer's disease (AD) [http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3246.html] using a unique resource: whole-genome sequences of 2,636 Icelanders imputed into 104,220 long-range phased individuals and their relatives.
So here we have a rare variant association study (RVAS) that employs several strategies for an efficient design:
1. Studying an isolated population (Iceland), whose genetic structure enabled accurate genotype imputation of a large sample set (>100k individuals) with sequencing data for just 2,500.
2. Analyzing missense variants with SKAT, which aggregates rare variants (i.e. collapses them at the level of the gene) to boost power for association but allows for multiple directions of effect.
3. Examining only regions known to be associated with AD-which seem likely to harbor [rare] functional variants-to reduce the multiple testing penalty.
TARGETED ASSOCIATION STUDIES
There are, of course, disadvantages to limiting the scope of association testing to known regions. Obviously you won't be discovering any new associations, especially ones that sequencing (but not genotyping) might be able to uncover. Even so, you're stacking the deck in your favor because the known GWAS loci almost certainly harbor some functional variation that hasn't yet been fully interrogated.
Sometimes, sequencing will only serve to replicate the common variant association signal (i.e. not find anything new). Yet these targeted approaches might help narrow the boundaries of the associated region-which could encompass dozens or hundreds of genes-or, even better, identify disruptive variants whose LD with the lead SNP makes them good candidates for causal variants. Thirdly, one might uncover secondary independent association signals in GWAS loci, implicating that there are multiple haplotypes that influence disease risk.
VARIANT ANNOTATION AND AGGREGATION
As anyone who has done aggregation/burden testing in association studies can tell you, the analysis choices can have a significant impact on results. The annotation tool/source, MAF threshold, and variant mask (definition of what's deleterious and should be included) can introduce a lot of variability. In this case, the authors tried two variant masks:
1. Loss of function variants: nonsense, frameshift or canonical splice site variants. These are usually quite rare, and so the authors collapsed them to a single "meta variant" at the level of the gene.
2. Missense variants: nonsynonymous variants or splice region variants. This latter one is an interesting choice, and not necessarily one I'd have thought to make at the discovery stage.
The burden tests included only variants with MAF<1% and information (call rate) >0.80. The authors tested about 80 genes across the 17 loci, and the top-scoring hit was ABCA7 (p=0.00020).
SPLICE REGION VARIATION IN ABCA7
ABCA7 encodes ATP-binding cassette transporter A7, a member of ABC transporters that move lipids across membranes. The SKAT result was primarily driven by a single variant, c.5570+5G>C. Without it, the test had a p-value of 0.46. If you're familiar with the notation, then you know that c.5570+5 indicates a noncoding variant 5 bases into an intron. We call this the "splice region" and, unlike the canonical splice site (+/- 2bp) it's not clear that variants here affect splicing.
But the authors had another NGS tool to look at this: RNA-seq. When they looked at the transcript sequences of c.5570+5G>C carriers, they included a retained intron that eventually included a stop codon.
Image: splicing variant in ABCA7 in Alzheimers-Intron retention in carriers (Steinberg et al ).
The image here is from Supplemental Figure 1 (the main text had no figures) and shows the intron retention in c.5570+5G>C carriers. Side note: according to the legend, the coordinates are on NCBI build 36, which practically a crime. But moving on, the RNA-seq results justified including the variant in the loss-of-function test (mask #1), which then yielded a p-value of 5.3e-10 with odds ratio of 1.97.
FOLLOW-UP AND REPLICATION OF ASSOCIATION
With a possible causal variant in hand, the authors next examined the long-range haplotypes to see if this variant was on the same background as rs4147929, the common variant previously associated with AD by GWAS. It was never on the same allele, which is a fascinating result; the common variant signal and this rare variant association appear to be independent. It's possible, therefore, that the mechanisms are different as well. To replicate the association, the authors genotyped ABCA7 loss-of-function variants in study groups from Europe and the United States, finding a p-value of 0.0056 with OR of 1.73. When combined with the Icelandic data by meta-analysis, the OR was 2.03 and the p-value 6.8e-15.
WHAT'S NEXT FOR AD AND COMMON DISEASE
ABCA7 certainly merits future studies, both in the genetics realm and in the laboratory for functional evaluation. It's strongly expressed in the brain, where it promotes the efflux of phospholipids and cholesterol to apoA-I and apoE. But the ortholog of ABCA7 in C. elegans and results from mouse models suggest that regulation of phagocytosis might be the primary function of the gene. The authors tested for correlation between variants in ABCA7 and two disease-associated alleles (in APOE and TREM2), but found none. Thus, the mechanism by which ABCA7 loss-of-function confers susceptibility to AD will need further investigation. Still, it's a promising start to detangling the etiology of a complex human disease, and a demonstration of the power of genome sequencing to uncover promising new leads.
REFERENCE: Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, Sulem P, Magnusson OT, Gudjonsson SA, Unnsteinsdottir U, Kong A, Helisalmi S, Soininen H, Lah JJ, DemGene, Aarsland D, Fladby T, Ulstein ID, Djurovic S, Sando SB, White LR, Knudsen GP, Westlye LT, Selbæk G, Giegling I, Hampel H, Hiltunen M, Levey AI, Andreassen OA, Rujescu D, Jonsson PV, Bjornsson S, Snaedal J, & Stefansson K (2015). Loss-of-function variants in ABCA7 confer risk of Alzheimer's disease. Nature genetics PMID: 25807283 : http://www.ncbi.nlm.nih.gov/pubmed/25807283
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GENOMIC VARIANTS [NOMENCLATURE]
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http://varnomen.hgvs.org/recommendations/general/
SEQUENCE variant nomenclature
GENERAL
Since references to web sites are not yet acknowledged as citations, please mention Den Dunnen et al relating to these pages. Note that although the examples on these pages mainly give examples for human (Homo sapiens), the recommendations can be applied to all species.
GENERAL RECOMMENDATIONS
all variants should be described at the most basic level, the DNA level. Descriptions at the RNA and/or protein level may be given in addition.
descriptions should make clear whether the change was experimentally determined or theoretically deduced by giving predicted consequences in parentheses.
all variants should be described in relation to an accepted reference sequence (see Reference Sequences).
the reference sequence file used should be public and clearly described, e.g. NC_000023.10, LRG_199, NG_012232.1, ENST00000357033, NM_004006.2, NR_002196.1, NP_003997.1, etc.
when variants are not reported in relation to a genomic reference sequence from a recent genome build, the preferred reference sequence is a Locus Reference Genomic sequence (LRG)
when no LRG is available, one should be requested (see Reference Sequences).
the reference sequence used must contain the residue(s) described to be changed.
a letter prefix should be used to indicate the type of reference sequence used. Accepted prefixes are;
"g." for a genomic reference sequence
"c." for a coding DNA reference sequence
"n." for a non-coding DNA reference sequence
"r." for an RNA reference sequence (transcript)
"p." for a protein reference sequence
numbering of the residues (nucleotide or amino acid) in relation to the reference sequence used should follow the approved scheme (see Numbering)
3'rule: for all descriptions the most 3' position possible of the reference sequence is arbitrarily assigned to have been changed
the 3'rule also applies for changes in single residue stretches and tandem repeats (nucleotide or amino acid)
the 3'rule applies to ALL descriptions (genome, gene, transcript and protein) of a given variant
descriptions at DNA, RNA and protein level are clearly different:
DNA-level 123456A>T (see Details): number(s) referring to the nucleotide(s) affected, nucleotides in CAPITALS using IUPAC-IUBMB assigned nucleotide symbols
RNA-level 76a>u (see Details): number(s) referring to the nucleotide(s) affected, nucleotides in lower case using IUPAC-IUBMB assigned nucleotide symbols
protein level Lys76Asn (see Details): the amino acid(s) affected in 3- or 1-letter followed by a number IUPAC-IUBMB assigned amino acid symbols * three-letter amino acid code is preferred (see Standards)
prioritisation: when a description is possible according to several types, the preferred description is: (1) deletion, (2) inversion, (3) duplication, (4) conversion, (5) insertion
when a variant can be described as a duplication or an insertion, prioritisation determines it should be described as a duplication
only approved HGNC gene symbols should be used to describe genes or proteins
CHARACTERS USED
In HGVS nomenclature some characters have a specific meaning
"+" (plus) is used in nucleotide numbering; c.123+45A>G
"-" (minus) is used in nucleotide numbering; c.124-56C>T
"*" (asterisk) is used in nucleotide numbering and to indicate a translation termination (stop) codon (see Standards); c.*32G>A and P.Trp41*
"_" (underscore) is used to indicate a range; g.12345_12678del
"[ ]" (angled brackets) are used for alleles (see DNA, RNA, protein)
";" (semi colon) is used to separate variants and alleles; g.[123456A>G;345678G>C] or g.[123456A>G];[345678G>C]
"," (comma) is used to separate different transcripts/proteins derived from one allele; r.[123a>t, 122_154del]
":" (colon) is used to separate the reference sequence file identifier (accession.version_number) from the actual description of a variant; NC_000011.9:g.12345611G>A
"( )" (parentheses) are used to indicate uncertainties; g.(123456_234567)_(345678_456789)del
NOTE: the range of the uncertainty should be described as precisely as possible (see below)
"?" (question mark) is used to indicate unknown positions (nucleotide or amino acid); g.(?_234567)_(345678_?)del
"^" (caret) is used as "or"; c.(370A>C^372C>R) as back translation of p.Ser124Arg
">" (greater than) is used to describe substitution variants (DNA and RNA level); g.12345A>T, r.123a>u (see DNA, RNA)
"{ }" (curly braces) suggested for the description of variants in otherwise perfect copy sequences (see Open Issues); g.24_65dup{46G>T}
"=" (equals) is used to indicate a sequence was tested but found unchanged; p.(Arg234=)
"/" (forward slash) is used to indicate mosaicism (see Complex (HGVS/ISCN))
"//" (double forward slash) is used to indicate chimerism (see Complex (HGVS/ISCN))
ABBREVIATIONS IN VARIANT DESCRIPTIONS
Specific abbreviations are used to describe different variant types.
">" (greater then) indicates a substitution (DNA and RNA level); g.123456G>A, r.123c>u (see DNA, RNA)
a substitution at the protein level is described as p.Ser321Arg (see protein)
"del" indicates a deletion; c.76delA (see DNA, RNA, protein)
"dup" indicates a duplication; c.76dupA (see DNA, RNA, protein)
"ins" indicates an insertion; c.76_77insG (see DNA, RNA, protein)
duplicating insertions are described as duplications, not as insertions
"inv" indicates an inversion; c.76_83inv (see DNA, RNA)
"con" indicates a conversion; NC_000022.10:g.42522624_42522669con42536337_42536382 (see DNA, RNA)
"fs" indicates a frame shift; p.Arg456GlyfsTer17 (or p.Arg456Glyfs*17, see Frame shifts)
"ext" indicates an extension; p.Met1ext-5 (see Frame shifts)
HGVS/ISCN (see Community Consultation)
"add" indicates an additional chromosome (marker chromosome)
"cen" indicates the centromere of a chromosome
"chr" indicates a chromosome; chr11:g.12345611G>A (NC_000011.9)
"pter indicates the first nucleotide of a chromosome
"qter" indicates the last nucleotide of a chromosome
QUESTIONS
Some papers and web sites use a "-" (minus) to indicate a range, is this correct?
The sign used to indicate a range is "_" (underscore) and not a "-" (minus). The minus sign should only be used as a minus in the description of variants based on a coding DNA reference sequence. c.12-14del describes a deletion of nucleotide -14 in the intron directly preceding coding DNA nucleotide 12, not a deletion of nucleotides c.12 to c.14.
Why is it recommended to use three-letter amino acid code to describe protein variants?
Several amino acids start with the same initial letter (Ala, Arg, Asn, Asp start with A, Gln, Glu, Gly with G, Leu, Lys with L, Phe, Pro with P and Thr, Tyr with T) but in one-letter amino acid code this letter is used as abbreviation for only one. In practice this leads to many mistakes. It is therefore recommended to use three-letter amino acid code abbreviations.
When I want to report a variant on DNA, RNA and protein level do I need to use a specific separator?
No, best is to report the variant using the format c.124G>A r.(?) p.(Val42Met). NOTE: needles to say, always mention the reference sequence file used
Is it correct that when I apply the 3'rule for genes that are on the minus strand of a chromosome, the "g." and "c." variant descriptions differ regarding the nucleotide that I describe as deleted?
Yes, when a gene is on the minus strand of a chromosome (opposite transcriptional orientation) and the change is located in a repeated sequence (mono-, di-, tri-, etc. nucleotide stretches) the 3'rule has this as a consequence. When the chromosome sequence is -TGGGGCAT- and one of the G's is deleted (change to -TGGG_CAT-) the description based on chromosome coordinates is g.5delG. When the annotated coding DNA reference sequence is on the minus strand (ATGCCCCA) the description is c.7delC. Not only is the deleted nucleotide different (delG vs. delC), in fact the descriptions also point to another nucleotide, g.5 vs. g.2 (equivalent to c.7delC).
Can I describe a deletion when I have not yet sequenced the break point?
Yes, using the characters to indicate uncertainties, i.e. the question mark ("?") and brackets ("( )"), such cases can be described. Describe the range of uncertainty as precise as possible. For details see Uncertain.
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