MCP๋กœ ์—ฐ๊ฒฐ โ†’

๊ณ„์‚ฐ ์ž…๋ ฅ

๊ฐ ๋ถ€๋ชจ์˜ ๋Œ€๋ฆฝ์œ ์ „์ž ๋‘ ๊ฐœ๋ฅผ ์„ ํƒํ•˜์„ธ์š”. A = ์šฐ์„ฑ, a = ์—ด์„ฑ. ์˜ˆ์‹œ: Aa ร— Aa โ†’ ๋‘ ๋ถ€๋ชจ ๋ชจ๋‘ A์™€ a๋ฅผ ์„ ํƒํ•ฉ๋‹ˆ๋‹ค.

๊ณต์‹

๊ด‘๊ณ 

๊ฒฐ๊ณผ

์œ ์ „์žํ˜• ๋น„์œจ
1 AA : 2 Aa : 1 aa
์ž์† ์นธ 4๊ฐœ ์ค‘
AA Aa
Aa aa
์œ ์ „์žํ˜• AA (์šฐ์„ฑ ๋™ํ˜•์ ‘ํ•ฉ) 1/4 = 25%
์œ ์ „์žํ˜• Aa (์ดํ˜•์ ‘ํ•ฉ) 2/4 = 50%
์œ ์ „์žํ˜• aa (์—ด์„ฑ ๋™ํ˜•์ ‘ํ•ฉ) 1/4 = 25%
์šฐ์„ฑ ํ‘œํ˜„ํ˜• 75%
์—ด์„ฑ ํ‘œํ˜„ํ˜• 25%

ํ‘ธ๋„ท ์‚ฌ๊ฐํ˜•์ด๋ž€?

ํ‘ธ๋„ท ์‚ฌ๊ฐํ˜•(Punnett Square)์€ ๋‘ ๋ถ€๋ชจ๋ฅผ ๊ต๋ฐฐํ–ˆ์„ ๋•Œ ์ž์†์—๊ฒŒ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ์œ ์ „์žํ˜•์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฐ„๋‹จํ•œ ๊ฒฉ์žํ‘œ๋กœ, ์œ ์ „ํ•™์—์„œ ๋„๋ฆฌ ์“ฐ์ž…๋‹ˆ๋‹ค. ์˜๊ตญ์˜ ์œ ์ „ํ•™์ž ๋ ˆ์ง€๋„๋“œ ํ‘ธ๋„ท(Reginald Punnett)์˜ ์ด๋ฆ„์„ ๋”ด ์ด ํ‘œ๋Š” ํ•œ์ชฝ ๋ถ€๋ชจ์˜ ์ƒ์‹์„ธํฌ(๋ฐฐ์šฐ์ž)๋ฅผ ํ‘œ ์œ„์ชฝ์—, ๋‹ค๋ฅธ ๋ถ€๋ชจ์˜ ๋ฐฐ์šฐ์ž๋ฅผ ์™ผ์ชฝ์— ๋ฐฐ์—ดํ•œ ๋’ค, ๊ฐ ์นธ์— ๋‘ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ์กฐํ•ฉํ•ด ์ฑ„์›Œ ๋„ฃ์Šต๋‹ˆ๋‹ค. ์ด ๊ณ„์‚ฐ๊ธฐ๋Š” ๋‹จ์„ฑ์žก์ข… ๊ต๋ฐฐ(monohybrid cross), ์ฆ‰ ์šฐ์„ฑ ๋Œ€๋ฆฝ์œ ์ „์ž(A)์™€ ์—ด์„ฑ ๋Œ€๋ฆฝ์œ ์ „์ž(a) ํ•˜๋‚˜์”ฉ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๋‹จ์ผ ์œ ์ „์ž ๊ต๋ฐฐ๋ฅผ ๋‹ค๋ฃน๋‹ˆ๋‹ค.

์œ„์ชฝ๊ณผ ์™ผ์ชฝ ๊ฐ€์žฅ์ž๋ฆฌ์— ๋ถ€๋ชจ์˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ํ‘œ์‹œํ•œ ๋นˆ 2x2 ํ‘ธ๋„ท ์‚ฌ๊ฐํ˜•
๋‹จ์„ฑ์žก์ข… ํ‘ธ๋„ท ์‚ฌ๊ฐํ˜•: ํ•œ ๋ถ€๋ชจ์˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋Š” ์—ด, ๋‹ค๋ฅธ ๋ถ€๋ชจ์˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋Š” ํ–‰์— ํ‘œ์‹œ๋ฉ๋‹ˆ๋‹ค.

์‚ฌ์šฉ ๋ฐฉ๋ฒ•

๋จผ์ € ๊ฐ ๋ถ€๋ชจ๊ฐ€ ๊ฐ€์ง„ ๋‘ ๊ฐœ์˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ์„ ํƒํ•˜์„ธ์š”. ๋Œ€๋ฌธ์ž "A"๋Š” ์šฐ์„ฑ ๋Œ€๋ฆฝ์œ ์ „์ž, ์†Œ๋ฌธ์ž "a"๋Š” ์—ด์„ฑ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ๋œปํ•ฉ๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด ๋ณด์ธ์ž(์ดํ˜•์ ‘ํ•ฉ) ๋ถ€๋ชจ๋Š” Aa, ์šฐ์„ฑ ๋™ํ˜•์ ‘ํ•ฉ ๋ถ€๋ชจ๋Š” AA, ์—ด์„ฑ ๋™ํ˜•์ ‘ํ•ฉ ๋ถ€๋ชจ๋Š” aa๋กœ ํ‘œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๊ณ„์‚ฐํ•˜๊ธฐ ๋ฒ„ํŠผ์„ ๋ˆ„๋ฅด๋ฉด 2ร—2 ๊ฒฉ์ž๊ฐ€ ์ž๋™์œผ๋กœ ๋งŒ๋“ค์–ด์ง€๊ณ , ๊ฐ ์œ ์ „์žํ˜•์˜ ๊ฐœ์ˆ˜์™€ ํ•จ๊ป˜ ์œ ์ „์žํ˜•ยทํ‘œํ˜„ํ˜•์ด ๋‚˜ํƒ€๋‚  ํ™•๋ฅ (%)์„ ํ•œ๋ˆˆ์— ๋ณด์—ฌ ์ค๋‹ˆ๋‹ค.

๊ณ„์‚ฐ ์›๋ฆฌ

๊ฐ ๋ถ€๋ชจ๋Š” ์ž์†์—๊ฒŒ ๋Œ€๋ฆฝ์œ ์ „์ž ํ•˜๋‚˜์”ฉ์„ ๋ฌผ๋ ค์ค๋‹ˆ๋‹ค. ๋ถ€๋ชจ๋งˆ๋‹ค ๋‘ ๊ฐœ์˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ๊ฐ€์ง€๋ฏ€๋กœ \(2 \times 2 = 4\)๊ฐ€์ง€์˜ ์กฐํ•ฉ์ด ๋˜‘๊ฐ™์€ ํ™•๋ฅ ๋กœ ๋งŒ๋“ค์–ด์ง‘๋‹ˆ๋‹ค. ํŠน์ • ์œ ์ „์žํ˜•์ด ๋‚˜ํƒ€๋‚  ํ™•๋ฅ ์€ ๊ทธ ์œ ์ „์žํ˜•์ด ๋“ค์–ด ์žˆ๋Š” ์นธ์˜ ์ˆ˜๋ฅผ 4๋กœ ๋‚˜๋ˆˆ ๊ฐ’์ž…๋‹ˆ๋‹ค.

$$\text{Offspring} = \left\{ \left(\text{P1}_a\,,\, \text{P1}_b\right) \times \left(\text{P2}_a\,,\, \text{P2}_b\right) \right\}$$

์šฐ์„ฑ ํ‘œํ˜„ํ˜•์€ A๊ฐ€ ํ•˜๋‚˜๋ผ๋„ ์žˆ์„ ๋•Œ(AA ๋˜๋Š” Aa) ๋‚˜ํƒ€๋‚˜๊ณ , ์—ด์„ฑ ํ‘œํ˜„ํ˜•์€ aa์ผ ๋•Œ๋งŒ ๋‚˜ํƒ€๋‚ฉ๋‹ˆ๋‹ค.

$$\begin{gathered} \text{Boxes} = \left\{ a_i \, b_j : a_i \in \big(\text{P1}_a,\, \text{P1}_b\big),\ b_j \in \big(\text{P2}_a,\, \text{P2}_b\big) \right\} \\[1.5em] \text{where}\quad \left\{ \begin{aligned} \%_{\text{genotype}} &= \frac{\text{count of that pair}}{4} \times 100 \\ \%_{\text{dominant}} &= \frac{AA + Aa}{4} \times 100 \\ \%_{\text{recessive}} &= \frac{aa}{4} \times 100 \end{aligned} \right. \end{gathered}$$
์œ ์ „์žํ˜• ์กฐํ•ฉ๊ณผ 3๋Œ€ 1 ํ‘œํ˜„ํ˜• ๋น„์œจ์„ ๋ณด์—ฌ์ฃผ๋Š” ์ฑ„์›Œ์ง„ 2x2 ํ‘ธ๋„ท ์‚ฌ๊ฐํ˜•
๊ฐ ์นธ์€ ํ–‰๊ณผ ์—ด์˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ์กฐํ•ฉํ•ฉ๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์„œ๋Š” ์„ธ ์นธ์ด ์šฐ์„ฑ, ํ•œ ์นธ์ด ์—ด์„ฑ ํ‘œํ˜„ํ˜•์„ ๋‚˜ํƒ€๋ƒ…๋‹ˆ๋‹ค.

์˜ˆ์ œ๋กœ ์•Œ์•„๋ณด๊ธฐ

๋‘ ์ดํ˜•์ ‘ํ•ฉ ๋ถ€๋ชจ, Aa ร— Aa๋ฅผ ๊ต๋ฐฐํ•ด ๋ด…์‹œ๋‹ค. ๋„ค ์นธ์—๋Š” ๊ฐ๊ฐ AA, Aa, Aa, aa๊ฐ€ ์ฑ„์›Œ์ง‘๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์œ ์ „์žํ˜• ๋น„์œจ์€ 1 AA : 2 Aa : 1 aa, ์ฆ‰ AA 25%, Aa 50%, aa 25%๊ฐ€ ๋ฉ๋‹ˆ๋‹ค. AA์™€ Aa ๋ชจ๋‘ ์šฐ์„ฑ ํ˜•์งˆ์„ ๋‚˜ํƒ€๋‚ด๋ฏ€๋กœ ํ‘œํ˜„ํ˜• ๋น„์œจ์€ ์šฐ์„ฑ 3 : ์—ด์„ฑ 1, ๋‹ค์‹œ ๋งํ•ด ์šฐ์„ฑ ํ˜•์งˆ์ด ๋‚˜ํƒ€๋‚  ํ™•๋ฅ  75%, ์—ด์„ฑ ํ˜•์งˆ์ด ๋‚˜ํƒ€๋‚  ํ™•๋ฅ  25%๊ฐ€ ๋ฉ๋‹ˆ๋‹ค.

์ž์ฃผ ๋ฌป๋Š” ์งˆ๋ฌธ

๋‘ ๊ฐ€์ง€ ํ˜•์งˆ(์–‘์„ฑ์žก์ข…)๋„ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋‚˜์š”? ์•„๋‹ˆ์š”. ์ด ๊ณ„์‚ฐ๊ธฐ๋Š” ๋‹จ์ผ ์œ ์ „์ž(๋‹จ์„ฑ์žก์ข…)๋งŒ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ์–‘์„ฑ์žก์ข… ๊ต๋ฐฐ(dihybrid cross)๋Š” 4ร—4 ๊ฒฉ์ž๊ฐ€ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.

"Aa"์™€ "aA"๋Š” ๊ฐ™์€๊ฐ€์š”? ๋„ค, ๊ฐ™์Šต๋‹ˆ๋‹ค. ์œ ์ „์žํ˜•์—์„œ๋Š” ์ˆœ์„œ๊ฐ€ ์˜๋ฏธ๊ฐ€ ์—†๊ธฐ ๋•Œ๋ฌธ์— ๊ณ„์‚ฐ๊ธฐ๋Š” ๋‘ ๊ฒฝ์šฐ ๋ชจ๋‘ "Aa"๋กœ ํ†ต์ผํ•ด ์ฒ˜๋ฆฌํ•ฉ๋‹ˆ๋‹ค.

ํ‘œํ˜„ํ˜• ํ™•๋ฅ (%)์€ ๋ฌด์—‡์„ ์˜๋ฏธํ•˜๋‚˜์š”? ์™„์ „ ์šฐ์„ฑ์„ ๊ฐ€์ •ํ–ˆ์„ ๋•Œ, ์ž์†์ด ์šฐ์„ฑ ๋˜๋Š” ์—ด์„ฑ ํ˜•์งˆ์„ ์‹ค์ œ๋กœ ๊ฒ‰์œผ๋กœ ๋‚˜ํƒ€๋‚ผ ํ™•๋ฅ ์„ ๋œปํ•ฉ๋‹ˆ๋‹ค.

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