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ÀÛ¼ºÀÚ | °ü¸®ÀÚ | ÀÛ¼ºÀÏ | 2015-04-29 |
÷ºÎÆÄÀÏ | ¼¼¹Ì³ª °øÁö_20150507(L)-bit.hwp | ||
¼¿ï´ëÇб³ »ý¹°Á¤º¸¿¬±¸¼Ò¿Í »ý¹°Á¤º¸ÇÐ Çùµ¿°úÁ¤ °øµ¿ ÁÖÃַΠƯº° ¼¼¹Ì³ª¸¦ ¾Æ·¡¿Í °°ÀÌ ¿°íÀÚ ÇÏ¿À´Ï, ¸¹Àº Âü¿© ¹Ù¶ø´Ï´Ù. ¡¡ ÀϽÃ: 2015³â 5¿ù 7ÀÏ ¸ñ¿äÀÏ ¿ÀÀü 11½Ã ¿¬»ç: ¿ø ¼º È£ ±³¼ö´Ô (¼¿ï´ëÇб³ º¸°Ç´ëÇпø º¸°ÇÇаú) Àå¼Ò: ¼¿ï´ëÇб³ 25-1µ¿ 103È£ ¡¡ TITLE Genetic association analysis with sequencing data ¡¡ ABSTRACT Common disease rare varaints hypothesis(CDRV) assumes that the risk for a common disease is influenced by multiple rare variants with strong genetic effects, implying that only a few cases share the same causal variants. With the arrival of high-throughput sequencing data, rare variants are the focus of genetic association studies. However, because single variant analysis has insufficient power to address analysis questions posed by the CDRV hypothesis, various analytical approaches have been suggested for the characterization of rare variants. One main approach is to collapse the genetic information of rare variants in a region and to construct an overall test statistic. Here, we review various approaches for rare variant association analyses. ¡¡ ¡¡ ¡¡ ¡¡ ¡¡ ¡¡ ¡¡ ¡¡ ¡¡ ¡¡ ¼¿ï´ëÇб³ »ý¹°Á¤º¸¿¬±¸¼Ò »ý¹°Á¤º¸ÇÐ Çùµ¿°úÁ¤ °øµ¿ÁÖÃÖ |
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