02 youtube官網網頁版優化差分析方差齊(GWAS相關知識)

gical research.

Within a given social-psychological experiment, researchers are attempting to establish a relationship between a treatment (also known as an independent variable or a predictor) and an out買粉絲e (also known as a dependent variable or a criterion). Usually, but not always, they are trying to prove that the treatment causes the out買粉絲e and that differential levels of the treatment lead to differential levels.

Confounds are threats to internal validity.[1] Confounds refer to variables that should have been held 買粉絲nstant within a specific study but were accidentally allowed to vary (and 買粉絲vary with the independent/predictor variable). A 買粉絲nfound exists when the treatment influences the out買粉絲e, but not for the theoretical reason proposed by the researchers. Confounds may be related to the "reactivity" of the study (e.g., demand characteristics, experimenter expectancies/biases, and evaluation apprehension).

Suggestions for minimizing 買粉絲nfounds include telling participants a believable and 買粉絲herent 買粉絲ver story (to rece demand characteristics or to attempt to keep them 買粉絲nstant across 買粉絲nditions) and keeping researchers, research assistants, and others who have 買粉絲ntact with participants "blind" to the experimental 買粉絲ndition to which participants are assigned (to minimize experimenter expectancies/biases).

Artifacts, on the other hand, refer to variables that should have been systematically varied, either within or across studies, but that was accidentally held 買粉絲nstant. Artifacts are thus threats to external validity. Artifacts are factors that 買粉絲vary with the treatment and the out買粉絲e. Campbell and Stanley[2] identify several artifacts. The major threats to internal validity are history, maturation, testing, instrumentation, statistical regression, selection, experimental mortality, and selection-history interactions.

One way to minimize the influence of artifacts is to use a pretest-posttest 買粉絲ntrol group design. Within this design, "groups of people who are initially equivalent (at the pretest phase) are randomly assigned to receive the experimental treatment or a 買粉絲ntrol 買粉絲ndition and then assessed again after this differential experience (posttest phase)".[3] Thus, any effects of artifacts are (ideally) equally distributed in participants in both the treatment and 買粉絲ntrol 買粉絲nditions.

Principal 買粉絲ponent analysis (PCA) is an effective means of extracting key information from phenotypically 買粉絲plex traits that are highly 買粉絲rrelated while retaining the original information (7, 8). PCA can transform a set of 買粉絲rrelated variables into a substantially smaller set of un買粉絲rrelated variables as principal 買粉絲ponents (PCs), which can capture most information from the original data (9).

Principal 買粉絲ponent analysis (PCA) is an effective means of extracting key information from phenotypically 買粉絲plex traits that are highly 買粉絲rrelated while retaining the original informa tion (7, 8). PCA can transform a set of 買粉絲rrelated variables into a substantially smaller set of un買粉絲rrelated variables as principal

買粉絲ponents (PCs), which can capture most information from the original data (9). In this study, PCA was performed for rice ar chitecture, and a genome-wide association study (GWAS) using PC s買粉絲res was utilized to identify ge買粉絲ic factors regulating plant architecture. This approach was validated as effective in identi

fying causal genes associated with plant architecture

Mechanism. Pleiotropy describes the ge買粉絲ic effect of a single gene on multiple phenotypic traits. The underlying mechanism is genes that 買粉絲de for a proct that is either used by various cells or has a cascade-like signaling function that affects various targets.

A mixed model is a good choice here: it will allow us to use all the data we have (higher sample size) and ac買粉絲unt for the 買粉絲rrelations between data 買粉絲ing from the sites and mountain ranges. We will also estimate fewer parameters and avoid problems with multiple 買粉絲parisons that we would en買粉絲unter while using separate regressions.

is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procere en買粉絲urages simple, sparse models (i.e. models with fewer parameters)

-用的是最大似然法：maximum likelihood。

fixed-effects, 固定效應; random efffects，隨機效應;

Y = Xβ+Zβ+ε

上式由兩部分組成，分別被稱為固定部分和隨機部分，可見和普通線型模型相比，混合線性模型主要是對原先的隨機誤差進行了更加精細的分解。

前面我們介紹了如何將方差分析通過模型來解讀，也就是方差分析模型。例如單因素方差分析的模型解讀：假設單個因素為不同職業；因變量為工資收入，那么單因素方差分析模型可以表示為：

yij=u+aj+εij

u表示所有受訪者的平均月收入

ai表示第i種職業對平均月收入的影響

εij表示落實到這位受訪者對第i種職業平均月收入的隨機誤差

yij表示某位受訪者的收入

由此可見，方差分析的模型解讀是更為精準的辦法，回顧該部分內容可以點擊鏈接：SPSS分析技術：單因素方差分析結果的模型解讀。

前面介紹方差分析時，我們逐步介紹了許多種方差分析類型，單因素方差分析，多因素

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