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Neuroimaging Genetics > Quiz 3 (5a, 5b, 6a) > Flashcards

Quiz 3 (5a, 5b, 6a) Flashcards

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1
Q

linkage disequilibrium: sections of DNA may __. alleles at nearby SNPs may __. in other words, if you know the genotype at one SNP, you can __

A

be inherited together; together more often than you would expect based on their allele frequencies; predict the genotype at a nearby SNP pretty well

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2
Q

humans have __ chromosomes (__ pairs of __ and __ __ chromosomes) in each cell

A

46; 22 pairs of autosomes; 2 sex chromosomes

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3
Q

meiosis is a type of __ that results in __

A

cell division; reproductive cells

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4
Q

haploid DNA (__) ends up in germ cells (__ and __)

A

one set per cell; sperm and oocyte

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5
Q

an autosome is __

A

any chromosome that isn’t a sex chromosome

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6
Q

when __ occurs, the double helix is broken in __ and __ in homologous places and the ends __

A

cross-over; one maternal and one paternal chromatid; are combined to form new chromatids that are a combo of DNA from each parent

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7
Q

the homologues have __ but are __

A

the same type of info; from different parents

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8
Q

a chromatid is __

A

one half of 2 identical copies of a chromosome

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9
Q

DNA is replicated before __ resulting in __ which are 2 identical copies of a single chromosome (from one parent) that are __

A

cell division; sister chromatids; connected by a centromere

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10
Q

chiasma =

A

crossing over point

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11
Q

oocyte =

A

immature ovum (egg cell)

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12
Q

crossovers occur __ per chromosome, and occur __

A

many times; anywhere

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13
Q

variants close to each other tend to have __ crossovers between them and so their alleles are __

A

fewer; more correlated

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14
Q

when the alleles at 2 SNPs are correlated, __

A

those SNPs are in linkage disequilibrium

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15
Q

ex: usually when SNP1 is an A, SNP2 on the same chromosome is a T. This indicates that this portion of the DNA __

A

tends to be inherited together/ is in high linkage disequilibrium

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16
Q

if a measured trait is associated with a particular variant, the trait may be cause by __

A

a different variant that is in high LD with the SNP you measured

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17
Q

__ and __ are two measures of LD

A

r^2 and D’

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18
Q

r^2 and D’: 0 values indicate __

A

no LD

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19
Q

r^2 and D’: 1 values indicate __

A

perfect LD

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20
Q

(r^2/D’) __ is sensitive to minor allele frequency but __ is not

A

r^2; D’

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21
Q

r^2 tells you __

A

how correlated the alleles are between two SNPs

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22
Q

D’ tells you __

A

how often your minor allele appears with a particular allele in another SNP

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23
Q

if you have a high __ but a very low __ you may not want one SNP substituting for the other because this means that __ predicts __ but the opposite is not true

A

D’; r^2; minor allele in SNP2; minor allele in SNP1

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24
Q

no clear-cut rules for what makes a strong or weak LD:
strong LD: __
intermediate: __
weak/no LD: __

A

r^2 >/= 0.8;
0.1 = r^2 < 0.8
r^2 < 0.1

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25
Q

__: a factor that can have multiple possible values

A

variable

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26
Q

__: the outcome whose variability is being measured

A

dependent variable

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27
Q

__: the variable we hypothesize will explain some part of the dependent variable’s variability

A

independent variable

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28
Q

“we hypothesize that the variability in the __ depends on, or can be explained by, the __”

A

dependent variable; independent variable

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29
Q

the probability that you are willing to accept that something due to random chance will be accepted as a true association

A

alpha

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30
Q

the probability of getting a result that is as strong/stronger than yours if there is no true effect or association

A

p-value

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31
Q

p-value is usually set to __ for scientific studies

A

0.05

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32
Q

p-value of 0.05: “we accept a probability of __ suggesting a relationship is real when it __”

A

<5%; was due to random chance

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33
Q

slide 17?

A

.

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34
Q

the center value of a defined set of numbers

A

mean/average

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35
Q

why do we care about mean? lets you __ at a glance, although it doesn’t tell you __

A

compare group differences; whether those differences are significant

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36
Q 
u = (x1 + x2 + x3 +...xn)/n
u = \_\_
x = \_\_
n = \_\_
A

mean; individual value in the data set; number of items in the data set

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37
Q

a measure of how spread out the data points are from the mean

A

variance

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38
Q

s^2 = z ….

A

.

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39
Q

standard deviation is __

A

the square root of the variance

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40
Q

continuous variables: variable with __ ex: __

A

an infinite number of possible values between two points; hippocampal volume

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41
Q

categorical variables (3)

A

dichotomous, ordinal, nominal

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42
Q

dichotomous: variables w/ __ ex: __

A

only 2 levels; does the subject live w/in 5 miles of a highway? (any yes/no question)

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43
Q

ordinal: variables w/ __ ex: __ or __

A

at least 2 categories that can be ranked; APOE genotype; past smoking behavior (never, <10 years, or >10 years)

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44
Q

nominal: variables w/ __ ex: __

A

at least 2 categories that DON’T have a particular order; data acquisition site

45
Q

common stats tests (6)

A

chi-squared test, student’s T-test, ANOVA, correlation/linear regression, multiple regression, logistic regression

46
Q

With a nominal variable, the data in each category might be __, but there is no __ (unlike something like __ where __)

A

different; intrinsic order to those categories; APOE genotype; risk increases with each e4 allele

47
Q

chi-square goodness of fit: use when you want to determine __

A

whether the number of subjects in each category of a nominal variable fits an expectation (ex: are the number of people with each genotype different from what we would expect given the minor allele frequency?)

48
Q

chi-square test of independence: use when you have __ and you want to determine __

A

two nominal variables; whether the proportions for one variable are different for different levels of the other variable (ex: sex & disease, is the disease more common in males than in females?)

49
Q

what is a significant chi-square test?

A

…

50
Q

student’s t-test: use when you want to determine __

A

whether a continuous variable is different in two groups (ex: is hippocampal volume smaller in APOE4 carriers versus non-carriers?)

51
Q

what stats test is this an example of: is hippocampal volume smaller in APOE4 carriers versus non-carriers?

A

student’s t-test

52
Q

what stats test is this an example of: variables = sex, disease; is the disease more common in males than in females?

A

chi-square test of independence

53
Q

what stats test is this an example of: are the number of people with each genotype different from what we would expect given the minor allele frequency?

A

chi-square goodness of fit

54
Q

ANOVA: if you want to covary for other measures (use covariates), use __ (__)

A

ANCOVA (analysis of covariance)

55
Q

ANOVA (1-way) aka __. use to measure __. will tell you __ but not __

A

analysis of variance; whether your continuous variable is different among more than two groups; whether a difference exists; which groups are significantly different from each other

56
Q

what stats test is this an example of: is there a difference in hippocampal volume between rs11136000 genotypes C/C, C/T, and T/T?

A

ANOVA

57
Q

correlation/linear regression: use to evaluate __

A

how associated two measurable variables are

58
Q

what stats test is this an example of: is hippocampal volume smaller in adults?

A

correlation/ linear regression

59
Q

when people say ‘regression’, they mean __

A

ordinary least-squares regression

60
Q

linear regression: __ is the vertical distance from the line

A

the residual error

61
Q

linear regression: the residual error is __

A

the vertical distance from the line

62
Q

linear regression: __ is the beta / the effect of the association

A

the slope

63
Q

linear regression: the slope is __

A

the beta / the effect of the association

64
Q

regression line is __ and it __

A

the best fit for the data; minimizes the vertical distances between the data points and the line

65
Q

linear regression graph: the dependent variable is on the __, the independent variable is on the __

A

y-axis; x-axis

66
Q

Pearson’s correlation coefficient: aka __ or __

A

r-value or Pearson’s r

67
Q

Pearson’s correlation coefficient: measures __, indicates __, ranges from __

A

how associated two variables are; direction of relationship; -1 to 1

68
Q

a Pearson’s r of 0 means __

A

no correlation

69
Q

a positive Pearson’s r

A

more of A is associated with more of B

70
Q

a negative Pearson’s r

A

more of A is associated with less of B

71
Q

r^2 = __ : measures __; does not tell you __

A

coefficient of determination; the proportion of the variance in the dependent variable that can be predicted by the independent variable; direction of the relationship

72
Q

Pearson’s r: (absolute) values for no relationship, strong relationship, moderate relationship, weak relationship, perfect correlation

A

0, >.69, 0.30 - 0.69, <0.30, 1

73
Q

r^2 ranges from __ to __

A

0 (no correlation); +1 (perfect correlation)

74
Q

slide 32?

A

..

75
Q

correlation/ linear regression: if the data are not __, they may be transformed by __ before performing the correlation

A

normally distributed; logarithm, square root, etc; ex: instead of using x in your analysis, use log10(x)

76
Q

linear regression: note: it is really the __ that need to be normally distributed rather than __

A

residuals; the data itself

77
Q

a __ test like __ should be used instead of a Pearson’s correlation if:
you have __ (3)

A

non-parametric; Spearman Rank correlation; small sample (<30); data outliers; non-normally distributed residuals that are not fixed through data

78
Q

___ reduces the effect of outliers

A

Spearman Rank correlation

79
Q

to perform a Spearman Rank, __ and assign __. values that are the same will __.

A

sort by each variable; a rank value to the values in order; share the rank halfway between their rank values

80
Q

1 issue with interpretation of correlation/ linear regression

A

correlation does NOT = causation

81
Q

ex: “We found that older adults who reported a greater consumption of fatty fish in the prior year had larger hippocampal volume. Our results suggest that fatty fish protects the brain from age-related atrophy.” what is wrong with the authors’ conclusions?

A

many other factors could influence hippocampal volume in a correlational study, and some of those may also be correlated with fatty fish consumption: higher socioeconomic class, general health consciousness, lower consumption of something else that is detrimental etc.

82
Q

what is this an example of? does FA decrease with age in any voxel?

A

correlation/ linear regression: voxelwise brain imaging

83
Q

correlation/linear regression: voxelwise brain imaging: each voxel in each brain is __

A

associated with a value (ex: DTI FA)

84
Q

correlation/linear regression: voxelwise brain imaging: different brains are __; stats analyses compare __

A

transformed into a template space; corresponding voxels across subjects with a measure of interest

85
Q

small sample should use more smoothing, decent sample should use less, huge doesnt need smoothing at all

A

..

86
Q

multiple linear regression: similar to __; __

A

linear regression; other variables are added to the model to explain variability in the dependent variable that is not explained by the independent variable of interest

87
Q

what stats test is this an example of? is hippocampal volume related to APOE4 genotype after removing the variability explained by age, sex, and education?

A

multiple linear regression

88
Q

mult. linear reg.: global p tells you __, not __

A

whether the whole model is significantly associated with your dependent variable; whether your variable of interest is

89
Q

when interpreting mult. linear reg. you must evaluate __ using __

A

contribution of individual independent variables; partial correlations

90
Q

global p value is from __

A

multiple linear regression

91
Q

mult. linear reg.: global p value must be significant before __

A

the contribution of any variable can be considered significant

92
Q

when to use a non-parametric test such as Spearman Partial Correlation: __(3)

A

small samples, outliers, and residuals that are not normally distributed

93
Q

a single multiple regression doesn’t test __, only __

A

mediation; how much one variable explains the variability in another

94
Q

Spearman partial correlation tests are the __ equivalent of __

A

non-parametric; multiple regression

95
Q

as with Spearman correlation tests, in spearman partial correlations the variables are __, and then __

A

ranked; multiple regression is run on the ranks

96
Q

spearman partial correlation: to test mediations, you should use __ or __

A

pathway analyses; a specific series of regression analyses

97
Q

spearman partial correlation: to test __, you should use pathway analyses or a specific series of regression analyses

A

mediations

98
Q

covariate

A

a variable that may predict part of the variability of the dependent variable

99
Q

a variable that may predict part of the variability of the dependent variable

A

covariate

100
Q

types of covariates (2)

A

confounding variable, interacting variable

101
Q

why use covariates? (2)

A

they help remove unexplained variability from the relationship that interests you possibly making that relationship clearer; they help ensure that the effect you see between the dependent variable and independent variable of interest isn’t actually due to something else that is correlated with both

102
Q

ex of covariate: hippocampal volume in schizophrenia patients vs controls

A

antipsychotic meds may decrease hippocampal volume and only patients would be taking them

103
Q

ex of covariate: hippocampal volume in schiz vs controls. why control for antipsychotic use in this study?

A

it’s correlated with diagnosis and may be correlated with hippocampal volume

104
Q

ex of covariate: hippocampal volume in schiz vs controls. control for antipsychotics so that we know __

A

whether the measured effect between patients and controls is independent of or driven by the medication

105
Q

You add a confounding covariate and the p value for the relationship between your dependent variable and your independent variable of interest becomes less significant. What could make that happen? (2)

A

the covariate is correlated with both the dependent variable and the independent variable of interest. the covariate is not correlated with either the dependent or the independent variable of interest (adding an extra variable slightly decreases statistical power)

106
Q

You add a confounding covariate and the p value for the relationship between your dependent variable and your independent variable of interest becomes more significant. What could make that happen?

A

the covariate is correlated with the dependent variable, but is not very correlated with the independent variable of interest. controlling for it reduced unexplained variability in the dependent variable, allowing the relationship of interest to become clearer

107
Q

a variable/covariate can still affect an outcome, even if __

A

there are no significant differences in that variable /covariate between groups

108
Q

interaction variables: a variable whose value __

A

helps explain the relationship between another independent variable and the dependent variable

109
Q

variable whose value helps explain the relationship between another independent variable and the dependent variable

A

interaction variable

Neuroimaging Genetics (1 decks)

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