(一)要如何證明mRNA有binding到DNA上，或microRNA對mRNA有影響?
1.Wobble mutant: 改變mRNA構型，但不改變protein，利用third codon的mutant。
例子: 看micro RNA 是否對RNA有影響?
方法: 改變第三個密碼子，使蛋白質相同，但mricoRNA不能切。
(二)Southern的應用:
1.檢視某一gene的family大小，是單組或多組，如果是多組就要考慮其他基因family的影響，是否有duplication?或有幾個copy?
2012年3月23日 星期五
Epigenetic 後生遺傳學
Epigenetic:
定義:
相同DNA背景，卻有不同的mRNA表現，可能是histion(在Histion tail的修飾作用)
,micro RNA, siRNA,或DNA甲基化的原因(使染色質緊緊纏繞，導致基因不表現)。
維基百科說明，此為研究DNA序列中未包含的基因調控訊息如何傳遞到下一代。
定義:
相同DNA背景，卻有不同的mRNA表現，可能是histion(在Histion tail的修飾作用)
,micro RNA, siRNA,或DNA甲基化的原因(使染色質緊緊纏繞，導致基因不表現)。
維基百科說明，此為研究DNA序列中未包含的基因調控訊息如何傳遞到下一代。
2012年3月22日 星期四
Genome-Wide Association Mapping
Association mapping also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of historic linkage disequilibrium to link phenotypes(observable characteristics) to genotypes (the genetic constitution of organisms).
Genomic selection in Plant Breeding :
(出處 Advance in Agronomy Volume 110, 2011, Pages 77–123)
“Genomic selection,” the ability to select for even complex, quantitative traits based on marker data alone, has arisen from the conjunction of new high-throughput marker technologies and new statistical methods needed to analyze the data. This review surveys what is known about these technologies, with sections on population and quantitative genetic background, DNA marker development, statistical methods, reported accuracies of genomic selection (GS) predictions, prediction of nonadditive genetic effects, prediction in the presence of subpopulation structure, and impacts of GS on long-term gain. GS works by estimating the effects of many loci spread across the genome. Marker and observation numbers therefore need to scale with the genetic map length in Morgans and with the effective population size of the population under GS. For typical crops, the requirements range from at least 200 to at most 10,000 markers and observations. With that baseline, GS can greatly accelerate the breeding cycle while also using marker information to maintain genetic diversity and potentially prolong gain beyond what is possible with phenotypic selection. With the costs of marker technologies continuing to decline and the statistical methods becoming more routine, the results reviewed here suggest that GS will play a large role in the plant breeding of the future. Our summary and interpretation should prove useful to breeders as they assess the value of GS in the context of their populations and resources.
(出處 Advance in Agronomy Volume 110, 2011, Pages 77–123)
“Genomic selection,” the ability to select for even complex, quantitative traits based on marker data alone, has arisen from the conjunction of new high-throughput marker technologies and new statistical methods needed to analyze the data. This review surveys what is known about these technologies, with sections on population and quantitative genetic background, DNA marker development, statistical methods, reported accuracies of genomic selection (GS) predictions, prediction of nonadditive genetic effects, prediction in the presence of subpopulation structure, and impacts of GS on long-term gain. GS works by estimating the effects of many loci spread across the genome. Marker and observation numbers therefore need to scale with the genetic map length in Morgans and with the effective population size of the population under GS. For typical crops, the requirements range from at least 200 to at most 10,000 markers and observations. With that baseline, GS can greatly accelerate the breeding cycle while also using marker information to maintain genetic diversity and potentially prolong gain beyond what is possible with phenotypic selection. With the costs of marker technologies continuing to decline and the statistical methods becoming more routine, the results reviewed here suggest that GS will play a large role in the plant breeding of the future. Our summary and interpretation should prove useful to breeders as they assess the value of GS in the context of their populations and resources.
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